{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5a034c7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Enhanced NAL (Non-Axiomatic Logic) Learning Rate Scheduler\n",
    "\n",
    "This module implements a learning rate scheduler for neural network training that uses\n",
    "Non-Axiomatic Logic reasoning to dynamically adjust learning rates based on training metrics.\n",
    "\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import random\n",
    "import torch\n",
    "from collections import deque\n",
    "from enum import Enum\n",
    "from typing import Dict, List, Tuple, Set, Optional, Union\n",
    "from dataclasses import dataclass\n",
    "import copy\n",
    "import time\n",
    "import math\n",
    "\n",
    "\n",
    "# NAL Term Types (for NAL-2 and above)\n",
    "class TermType(Enum):\n",
    "    ATOM = 0          # Atomic term (e.g., \"x\")\n",
    "    COMPOUND = 1      # Compound term (e.g., intersection, difference)\n",
    "    STATEMENT = 2     # Statement with a copula (e.g., inheritance)\n",
    "\n",
    "\n",
    "# Copula Types (NAL-2 and above)\n",
    "class CopulaType(Enum):\n",
    "    INHERITANCE = 0   # Inheritance relation (-->)\n",
    "    SIMILARITY = 1    # Similarity relation (<->)\n",
    "    IMPLICATION = 2   # Implication relation (==>)\n",
    "    EQUIVALENCE = 3   # Equivalence relation (<==>)\n",
    "    INSTANCE = 4      # Instance relation (e.g., {John} --> Human)\n",
    "    PROPERTY = 5      # Property relation (e.g., Red --> [Color])\n",
    "\n",
    "\n",
    "# Connective Types (NAL-3 and above)\n",
    "class ConnectiveType(Enum):\n",
    "    CONJUNCTION = 0   # Logical AND\n",
    "    DISJUNCTION = 1   # Logical OR\n",
    "    NEGATION = 2      # Logical NOT\n",
    "    SEQUENTIAL = 3    # Sequential conjunction (for NAL-7)\n",
    "    PARALLEL = 4      # Parallel conjunction (for NAL-7)\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class TruthValue:\n",
    "    \"\"\"Truth value in Non-Axiomatic Logic with frequency and confidence\"\"\"\n",
    "    f: float  # frequency\n",
    "    c: float  # confidence\n",
    "    \n",
    "    def __post_init__(self):\n",
    "        self.f = float(np.clip(self.f, 0.0, 1.0))\n",
    "        self.c = float(np.clip(self.c, 0.0, 1.0))\n",
    "    \n",
    "    def __str__(self):\n",
    "        return f\"TV({self.f:.2f}, {self.c:.2f})\"\n",
    "    \n",
    "    @property\n",
    "    def expectation(self) -> float:\n",
    "        \"\"\"Calculate the expectation (strength) of the truth value (NAL-1)\"\"\"\n",
    "        return self.c * (self.f - 0.5) + 0.5\n",
    "    \n",
    "    @property\n",
    "    def evidence(self) -> float:\n",
    "        \"\"\"Calculate the amount of evidence represented (NAL-1)\"\"\"\n",
    "        if self.c == 0:\n",
    "            return 0\n",
    "        return self.c / (1 - self.c)\n",
    "    \n",
    "    # Explicitly define method versions that call the properties\n",
    "    # This helps with code that might call them as methods\n",
    "    def expectation_method(self):\n",
    "        return self.expectation\n",
    "    \n",
    "    def evidence_method(self):\n",
    "        return self.evidence\n",
    "    \n",
    "    @classmethod\n",
    "    def revision(cls, tv1: 'TruthValue', tv2: 'TruthValue') -> 'TruthValue':\n",
    "        \"\"\"Truth value revision (NAL-1)\"\"\"\n",
    "        if tv1.c == 0:\n",
    "            return tv2\n",
    "        if tv2.c == 0:\n",
    "            return tv1\n",
    "            \n",
    "        w1 = tv1.evidence  # Using property\n",
    "        w2 = tv2.evidence  # Using property\n",
    "        w = w1 + w2\n",
    "        \n",
    "        f = (w1 * tv1.f + w2 * tv2.f) / w\n",
    "        c = w / (w + 1)\n",
    "        \n",
    "        return cls(f, c)\n",
    "    \n",
    "    @classmethod\n",
    "    def deduction(cls, tv1: 'TruthValue', tv2: 'TruthValue') -> 'TruthValue':\n",
    "        \"\"\"Deduction inference (NAL-2)\"\"\"\n",
    "        f = tv1.f * tv2.f\n",
    "        c = tv1.c * tv2.c * tv1.f\n",
    "        return cls(f, c)\n",
    "    \n",
    "    @classmethod\n",
    "    def abduction(cls, tv1: 'TruthValue', tv2: 'TruthValue') -> 'TruthValue':\n",
    "        \"\"\"Abduction inference (NAL-2)\"\"\"\n",
    "        f = tv1.f\n",
    "        c = tv1.c * tv2.c * tv2.f\n",
    "        return cls(f, c)\n",
    "    \n",
    "    @classmethod\n",
    "    def induction(cls, tv1: 'TruthValue', tv2: 'TruthValue') -> 'TruthValue':\n",
    "        \"\"\"Induction inference (NAL-2)\"\"\"\n",
    "        f = tv2.f\n",
    "        c = tv1.c * tv2.c * tv1.f\n",
    "        return cls(f, c)\n",
    "    \n",
    "    @classmethod\n",
    "    def exemplification(cls, tv1: 'TruthValue', tv2: 'TruthValue') -> 'TruthValue':\n",
    "        \"\"\"Exemplification inference (NAL-2)\"\"\"\n",
    "        f = 1.0\n",
    "        c = tv1.c * tv2.c * tv1.f * tv2.f\n",
    "        return cls(f, c)\n",
    "    \n",
    "    @classmethod\n",
    "    def intersection(cls, tv1: 'TruthValue', tv2: 'TruthValue') -> 'TruthValue':\n",
    "        \"\"\"Intersection (NAL-3)\"\"\"\n",
    "        f = tv1.f * tv2.f\n",
    "        c = tv1.c * tv2.c\n",
    "        return cls(f, c)\n",
    "    \n",
    "    @classmethod\n",
    "    def union(cls, tv1: 'TruthValue', tv2: 'TruthValue') -> 'TruthValue':\n",
    "        \"\"\"Union (NAL-3)\"\"\"\n",
    "        f = tv1.f + tv2.f - tv1.f * tv2.f\n",
    "        c = tv1.c * tv2.c\n",
    "        return cls(f, c)\n",
    "    \n",
    "    @classmethod\n",
    "    def negation(cls, tv: 'TruthValue') -> 'TruthValue':\n",
    "        \"\"\"Negation (NAL-3)\"\"\"\n",
    "        return cls(1.0 - tv.f, tv.c)\n",
    "\n",
    "\n",
    "class Term:\n",
    "    \"\"\"Base class for NAL terms (NAL-2 and above)\"\"\"\n",
    "    def __init__(self, name: str, term_type: TermType = TermType.ATOM):\n",
    "        self.name = name\n",
    "        self.term_type = term_type\n",
    "    \n",
    "    def __str__(self):\n",
    "        return self.name\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return f\"{self.__class__.__name__}({self.name})\"\n",
    "    \n",
    "    def __eq__(self, other):\n",
    "        if not isinstance(other, Term):\n",
    "            return False\n",
    "        return self.name == other.name and self.term_type == other.term_type\n",
    "    \n",
    "    def __hash__(self):\n",
    "        return hash((self.name, self.term_type))\n",
    "\n",
    "\n",
    "class AtomicTerm(Term):\n",
    "    \"\"\"Atomic term with no internal structure (NAL-1)\"\"\"\n",
    "    def __init__(self, name: str):\n",
    "        super().__init__(name, TermType.ATOM)\n",
    "\n",
    "\n",
    "class CompoundTerm(Term):\n",
    "    \"\"\"Compound term with connective and components (NAL-3)\"\"\"\n",
    "    def __init__(self, \n",
    "                 connective: ConnectiveType, \n",
    "                 components: List[Term],\n",
    "                 name: Optional[str] = None):\n",
    "        self.connective = connective\n",
    "        self.components = components\n",
    "        \n",
    "        # Generate name if not provided\n",
    "        if name is None:\n",
    "            if connective == ConnectiveType.CONJUNCTION:\n",
    "                connector = \"&&\"\n",
    "            elif connective == ConnectiveType.DISJUNCTION:\n",
    "                connector = \"||\"\n",
    "            elif connective == ConnectiveType.NEGATION:\n",
    "                connector = \"~\"\n",
    "                if len(components) == 1:\n",
    "                    name = f\"{connector}{components[0]}\"\n",
    "            elif connective == ConnectiveType.SEQUENTIAL:\n",
    "                connector = \"&/\"\n",
    "            else:  # PARALLEL\n",
    "                connector = \"&|\"\n",
    "                \n",
    "            if name is None:\n",
    "                name = f\"({connector.join(str(c) for c in components)})\"\n",
    "                \n",
    "        super().__init__(name, TermType.COMPOUND)\n",
    "    \n",
    "    @classmethod\n",
    "    def conjunction(cls, terms: List[Term]) -> 'CompoundTerm':\n",
    "        \"\"\"Create a conjunction compound term (NAL-3)\"\"\"\n",
    "        return cls(ConnectiveType.CONJUNCTION, terms)\n",
    "    \n",
    "    @classmethod\n",
    "    def disjunction(cls, terms: List[Term]) -> 'CompoundTerm':\n",
    "        \"\"\"Create a disjunction compound term (NAL-3)\"\"\"\n",
    "        return cls(ConnectiveType.DISJUNCTION, terms)\n",
    "    \n",
    "    @classmethod\n",
    "    def negation(cls, term: Term) -> 'CompoundTerm':\n",
    "        \"\"\"Create a negation compound term (NAL-3)\"\"\"\n",
    "        return cls(ConnectiveType.NEGATION, [term])\n",
    "    \n",
    "    @classmethod\n",
    "    def sequential(cls, terms: List[Term]) -> 'CompoundTerm':\n",
    "        \"\"\"Create a sequential conjunction (NAL-7)\"\"\"\n",
    "        return cls(ConnectiveType.SEQUENTIAL, terms)\n",
    "    \n",
    "    @classmethod\n",
    "    def parallel(cls, terms: List[Term]) -> 'CompoundTerm':\n",
    "        \"\"\"Create a parallel conjunction (NAL-7)\"\"\"\n",
    "        return cls(ConnectiveType.PARALLEL, terms)\n",
    "\n",
    "\n",
    "class Statement(Term):\n",
    "    \"\"\"Statement with subject, predicate and copula (NAL-2)\"\"\"\n",
    "    def __init__(self, \n",
    "                 subject: Term, \n",
    "                 predicate: Term, \n",
    "                 copula: CopulaType,\n",
    "                 name: Optional[str] = None):\n",
    "        self.subject = subject\n",
    "        self.predicate = predicate\n",
    "        self.copula = copula\n",
    "        \n",
    "        # Generate name if not provided\n",
    "        if name is None:\n",
    "            if copula == CopulaType.INHERITANCE:\n",
    "                connector = \"-->\"\n",
    "            elif copula == CopulaType.SIMILARITY:\n",
    "                connector = \"<->\"\n",
    "            elif copula == CopulaType.IMPLICATION:\n",
    "                connector = \"==>\"\n",
    "            elif copula == CopulaType.EQUIVALENCE:\n",
    "                connector = \"<==>\"\n",
    "            elif copula == CopulaType.INSTANCE:\n",
    "                connector = \":{-->\"\n",
    "            else:  # PROPERTY\n",
    "                connector = \"--{:\"\n",
    "                \n",
    "            name = f\"({subject} {connector} {predicate})\"\n",
    "            \n",
    "        super().__init__(name, TermType.STATEMENT)\n",
    "    \n",
    "    @classmethod\n",
    "    def inheritance(cls, subject: Term, predicate: Term) -> 'Statement':\n",
    "        \"\"\"Create an inheritance statement (NAL-2)\"\"\"\n",
    "        return cls(subject, predicate, CopulaType.INHERITANCE)\n",
    "    \n",
    "    @classmethod\n",
    "    def similarity(cls, term1: Term, term2: Term) -> 'Statement':\n",
    "        \"\"\"Create a similarity statement (NAL-2)\"\"\"\n",
    "        return cls(term1, term2, CopulaType.SIMILARITY)\n",
    "    \n",
    "    @classmethod\n",
    "    def implication(cls, condition: Term, prediction: Term) -> 'Statement':\n",
    "        \"\"\"Create an implication statement (NAL-5)\"\"\"\n",
    "        return cls(condition, prediction, CopulaType.IMPLICATION)\n",
    "    \n",
    "    @classmethod\n",
    "    def equivalence(cls, term1: Term, term2: Term) -> 'Statement':\n",
    "        \"\"\"Create an equivalence statement (NAL-5)\"\"\"\n",
    "        return cls(term1, term2, CopulaType.EQUIVALENCE)\n",
    "\n",
    "\n",
    "class TemporalStatement(Statement):\n",
    "    \"\"\"Temporal statement with subject, predicate, copula and time (NAL-7)\"\"\"\n",
    "    def __init__(self,\n",
    "                 subject: Term,\n",
    "                 predicate: Term,\n",
    "                 copula: CopulaType,\n",
    "                 time_offset: int = 0,\n",
    "                 name: Optional[str] = None):\n",
    "        self.time_offset = time_offset\n",
    "        \n",
    "        # Generate name if not provided\n",
    "        if name is None:\n",
    "            base_statement = Statement(subject, predicate, copula)\n",
    "            if time_offset == 0:\n",
    "                name = str(base_statement)\n",
    "            else:\n",
    "                name = f\"{base_statement} @ {time_offset}\"\n",
    "                \n",
    "        super().__init__(subject, predicate, copula, name)\n",
    "\n",
    "\n",
    "class Belief:\n",
    "    \"\"\"A belief consisting of a term and its truth value (NAL-1)\"\"\"\n",
    "    def __init__(self, term: Union[str, Term], tv: TruthValue):\n",
    "        if isinstance(term, str):\n",
    "            self.term = AtomicTerm(term)\n",
    "        else:\n",
    "            self.term = term\n",
    "        self.tv = tv\n",
    "        \n",
    "    def __str__(self):\n",
    "        return f\"{self.term}: {self.tv}\"\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return f\"Belief({repr(self.term)}, {repr(self.tv)})\"\n",
    "\n",
    "\n",
    "class NALEngine:\n",
    "    \"\"\"Non-Axiomatic Logic reasoning engine\"\"\"\n",
    "    def __init__(self):\n",
    "        self.beliefs = {}  # Dictionary mapping terms to truth values\n",
    "        self.inference_rules = {\n",
    "            \"deduction\": TruthValue.deduction,\n",
    "            \"abduction\": TruthValue.abduction,\n",
    "            \"induction\": TruthValue.induction,\n",
    "            \"exemplification\": TruthValue.exemplification,\n",
    "            \"revision\": TruthValue.revision,\n",
    "            \"intersection\": TruthValue.intersection,\n",
    "            \"union\": TruthValue.union,\n",
    "            \"negation\": TruthValue.negation\n",
    "        }\n",
    "        \n",
    "    def add_belief(self, term: Union[str, Term], frequency: float, confidence: float):\n",
    "        \"\"\"Add a belief with given frequency and confidence\"\"\"\n",
    "        if isinstance(term, str):\n",
    "            term = AtomicTerm(term)\n",
    "            \n",
    "        tv = TruthValue(frequency, confidence)\n",
    "        \n",
    "        # Check if we already have this belief\n",
    "        if term in self.beliefs:\n",
    "            # Revise with existing belief\n",
    "            tv = TruthValue.revision(self.beliefs[term], tv)\n",
    "            \n",
    "        self.beliefs[term] = tv\n",
    "        \n",
    "    def add_implication(self, antecedent: Union[str, Term], consequent: Union[str, Term], \n",
    "                         frequency: float, confidence: float):\n",
    "        \"\"\"Add an implication belief\"\"\"\n",
    "        if isinstance(antecedent, str):\n",
    "            antecedent = AtomicTerm(antecedent)\n",
    "        if isinstance(consequent, str):\n",
    "            consequent = AtomicTerm(consequent)\n",
    "            \n",
    "        # Create implication statement\n",
    "        statement = Statement.implication(antecedent, consequent)\n",
    "        \n",
    "        # Add as belief\n",
    "        self.add_belief(statement, frequency, confidence)\n",
    "        \n",
    "    def _get_term_truth_value(self, term: Union[str, Term]) -> TruthValue:\n",
    "        \"\"\"Get truth value for a term, or default if not found\"\"\"\n",
    "        if isinstance(term, str):\n",
    "            term = AtomicTerm(term)\n",
    "            \n",
    "        if term in self.beliefs:\n",
    "            return self.beliefs[term]\n",
    "        else:\n",
    "            # Default truth value with zero confidence\n",
    "            return TruthValue(0.5, 0.0)\n",
    "            \n",
    "    def get_belief_strength(self, term: Union[str, Term]) -> float:\n",
    "        \"\"\"Get the expectation strength of a belief\"\"\"\n",
    "        tv = self._get_term_truth_value(term)\n",
    "        return tv.expectation\n",
    "        \n",
    "    def _find_matching_statements(self, term_type=None):\n",
    "        \"\"\"Find statements of specified type\"\"\"\n",
    "        matching = []\n",
    "        \n",
    "        for term in self.beliefs.keys():\n",
    "            if isinstance(term, Statement):\n",
    "                if term_type is None or term.copula == term_type:\n",
    "                    matching.append(term)\n",
    "                    \n",
    "        return matching\n",
    "        \n",
    "    def _apply_deduction(self, a_b: Statement, b_c: Statement):\n",
    "        \"\"\"Apply deduction A-->B, B-->C |- A-->C\"\"\"\n",
    "        if (a_b.copula == CopulaType.INHERITANCE and \n",
    "            b_c.copula == CopulaType.INHERITANCE and\n",
    "            a_b.predicate == b_c.subject):\n",
    "            \n",
    "            # Create new statement\n",
    "            a_c = Statement.inheritance(a_b.subject, b_c.predicate)\n",
    "            \n",
    "            # Get truth values\n",
    "            tv_a_b = self.beliefs[a_b]\n",
    "            tv_b_c = self.beliefs[b_c]\n",
    "            \n",
    "            # Apply deduction\n",
    "            tv_a_c = TruthValue.deduction(tv_a_b, tv_b_c)\n",
    "            \n",
    "            # Add derived belief\n",
    "            self.add_belief(a_c, tv_a_c.f, tv_a_c.c)\n",
    "            \n",
    "            return True\n",
    "            \n",
    "        return False\n",
    "        \n",
    "    def _apply_induction(self, a_b: Statement, a_c: Statement):\n",
    "        \"\"\"Apply induction A-->B, A-->C |- B-->C\"\"\"\n",
    "        if (a_b.copula == CopulaType.INHERITANCE and\n",
    "            a_c.copula == CopulaType.INHERITANCE and\n",
    "            a_b.subject == a_c.subject):\n",
    "            \n",
    "            # Create new statement\n",
    "            b_c = Statement.inheritance(a_b.predicate, a_c.predicate)\n",
    "            \n",
    "            # Get truth values\n",
    "            tv_a_b = self.beliefs[a_b]\n",
    "            tv_a_c = self.beliefs[a_c]\n",
    "            \n",
    "            # Apply induction\n",
    "            tv_b_c = TruthValue.induction(tv_a_b, tv_a_c)\n",
    "            \n",
    "            # Add derived belief\n",
    "            self.add_belief(b_c, tv_b_c.f, tv_b_c.c)\n",
    "            \n",
    "            return True\n",
    "            \n",
    "        return False\n",
    "        \n",
    "    def _apply_abduction(self, a_c: Statement, b_c: Statement):\n",
    "        \"\"\"Apply abduction A-->C, B-->C |- A-->B\"\"\"\n",
    "        if (a_c.copula == CopulaType.INHERITANCE and\n",
    "            b_c.copula == CopulaType.INHERITANCE and\n",
    "            a_c.predicate == b_c.predicate):\n",
    "            \n",
    "            # Create new statement\n",
    "            a_b = Statement.inheritance(a_c.subject, b_c.subject)\n",
    "            \n",
    "            # Get truth values\n",
    "            tv_a_c = self.beliefs[a_c]\n",
    "            tv_b_c = self.beliefs[b_c]\n",
    "            \n",
    "            # Apply abduction\n",
    "            tv_a_b = TruthValue.abduction(tv_a_c, tv_b_c)\n",
    "            \n",
    "            # Add derived belief\n",
    "            self.add_belief(a_b, tv_a_b.f, tv_a_b.c)\n",
    "            \n",
    "            return True\n",
    "            \n",
    "        return False\n",
    "        \n",
    "    def _apply_exemplification(self, a_b: Statement, c_a: Statement):\n",
    "        \"\"\"Apply exemplification A-->B, C-->A |- C-->B\"\"\"\n",
    "        if (a_b.copula == CopulaType.INHERITANCE and\n",
    "            c_a.copula == CopulaType.INHERITANCE and\n",
    "            a_b.subject == c_a.predicate):\n",
    "            \n",
    "            # Create new statement\n",
    "            c_b = Statement.inheritance(c_a.subject, a_b.predicate)\n",
    "            \n",
    "            # Get truth values\n",
    "            tv_a_b = self.beliefs[a_b]\n",
    "            tv_c_a = self.beliefs[c_a]\n",
    "            \n",
    "            # Apply exemplification\n",
    "            tv_c_b = TruthValue.exemplification(tv_a_b, tv_c_a)\n",
    "            \n",
    "            # Add derived belief\n",
    "            self.add_belief(c_b, tv_c_b.f, tv_c_b.c)\n",
    "            \n",
    "            return True\n",
    "            \n",
    "        return False\n",
    "        \n",
    "    def _apply_conversion(self, a_b: Statement):\n",
    "        \"\"\"Apply conversion A-->B |- B-->A\"\"\"\n",
    "        if a_b.copula == CopulaType.INHERITANCE:\n",
    "            # Create new statement\n",
    "            b_a = Statement.inheritance(a_b.predicate, a_b.subject)\n",
    "            \n",
    "            # Get truth value\n",
    "            tv_a_b = self.beliefs[a_b]\n",
    "            \n",
    "            # Apply conversion (w=1)\n",
    "            f = tv_a_b.c\n",
    "            c = tv_a_b.f\n",
    "            tv_b_a = TruthValue(f, c)\n",
    "            \n",
    "            # Add derived belief\n",
    "            self.add_belief(b_a, tv_b_a.f, tv_b_a.c)\n",
    "            \n",
    "            return True\n",
    "            \n",
    "        return False\n",
    "        \n",
    "    def _apply_implication_deduction(self, s1: Statement, s2: Statement):\n",
    "        \"\"\"Apply deduction on implication statements\"\"\"\n",
    "        # For s1: A ==> B, s2: B ==> C |- A ==> C\n",
    "        if (s1.copula == CopulaType.IMPLICATION and\n",
    "            s2.copula == CopulaType.IMPLICATION and\n",
    "            s1.predicate == s2.subject):\n",
    "            \n",
    "            # Create new statement\n",
    "            new_stmt = Statement.implication(s1.subject, s2.predicate)\n",
    "            \n",
    "            # Get truth values\n",
    "            tv_s1 = self.beliefs[s1]\n",
    "            tv_s2 = self.beliefs[s2]\n",
    "            \n",
    "            # Apply deduction\n",
    "            tv_new = TruthValue.deduction(tv_s1, tv_s2)\n",
    "            \n",
    "            # Add derived belief\n",
    "            self.add_belief(new_stmt, tv_new.f, tv_new.c)\n",
    "            \n",
    "            return True\n",
    "            \n",
    "        return False\n",
    "        \n",
    "    def inference_cycle(self, max_cycles=5):\n",
    "        \"\"\"Run inference cycles to derive new beliefs\"\"\"\n",
    "        cycle_count = 0\n",
    "        total_derived = 0\n",
    "        \n",
    "        while cycle_count < max_cycles:\n",
    "            derived_count = 0\n",
    "            \n",
    "            # Get all inheritance statements\n",
    "            inheritance_stmts = self._find_matching_statements(CopulaType.INHERITANCE)\n",
    "            \n",
    "            # Get all implication statements\n",
    "            implication_stmts = self._find_matching_statements(CopulaType.IMPLICATION)\n",
    "            \n",
    "            # Apply syllogistic rules on inheritance statements\n",
    "            for i, s1 in enumerate(inheritance_stmts):\n",
    "                for s2 in inheritance_stmts[i+1:]:\n",
    "                    # Try all possible syllogistic rules\n",
    "                    if self._apply_deduction(s1, s2) or self._apply_deduction(s2, s1):\n",
    "                        derived_count += 1\n",
    "                    if self._apply_induction(s1, s2) or self._apply_induction(s2, s1):\n",
    "                        derived_count += 1\n",
    "                    if self._apply_abduction(s1, s2) or self._apply_abduction(s2, s1):\n",
    "                        derived_count += 1\n",
    "                    if self._apply_exemplification(s1, s2) or self._apply_exemplification(s2, s1):\n",
    "                        derived_count += 1\n",
    "                \n",
    "                # Apply conversion\n",
    "                if self._apply_conversion(s1):\n",
    "                    derived_count += 1\n",
    "            \n",
    "            # Apply rules on implication statements\n",
    "            for i, s1 in enumerate(implication_stmts):\n",
    "                for s2 in implication_stmts[i+1:]:\n",
    "                    if self._apply_implication_deduction(s1, s2) or self._apply_implication_deduction(s2, s1):\n",
    "                        derived_count += 1\n",
    "            \n",
    "            # Stop if no new beliefs were derived\n",
    "            if derived_count == 0:\n",
    "                break\n",
    "                \n",
    "            total_derived += derived_count\n",
    "            cycle_count += 1\n",
    "            \n",
    "        return total_derived\n",
    "            \n",
    "    def print_beliefs(self):\n",
    "        \"\"\"Print all current beliefs\"\"\"\n",
    "        print(\"\\nCurrent Beliefs:\")\n",
    "        for term, tv in sorted(self.beliefs.items(), key=lambda x: str(x[0])):\n",
    "            print(f\"  {term}: {tv} (exp: {tv.expectation:.3f})\")\n",
    "            \n",
    "    def select_best_action(self, action_beliefs=None, min_confidence=0.3):\n",
    "        \"\"\"Select best action based on belief strengths\"\"\"\n",
    "        if action_beliefs is None:\n",
    "            # Default action beliefs to look for\n",
    "            action_beliefs = [\n",
    "                'should_increase_lr',\n",
    "                'should_decrease_lr',\n",
    "                'should_decrease_lr_gently',\n",
    "                'should_hold_lr', \n",
    "                'continue_current_strategy',\n",
    "                'should_change_strategy'\n",
    "            ]\n",
    "            \n",
    "        best_action = None\n",
    "        best_strength = -1.0\n",
    "        \n",
    "        for action in action_beliefs:\n",
    "            tv = self._get_term_truth_value(action)\n",
    "            strength = tv.expectation\n",
    "            \n",
    "            # Only consider beliefs with sufficient confidence\n",
    "            if tv.c >= min_confidence and strength > best_strength:\n",
    "                best_action = action\n",
    "                best_strength = strength\n",
    "                \n",
    "        return best_action, best_strength\n",
    "\n",
    "\n",
    "class NALLRTunerFinal:\n",
    "    def __init__(self, initial_lr=1e-4, lr_min=1e-6, lr_max=0.1, history_len=40):\n",
    "        # Initialize NAL reasoning engine\n",
    "        self.engine = NALEngine()\n",
    "        \n",
    "        # Learning rate parameters\n",
    "        self.initial_lr = initial_lr\n",
    "        self.lr = initial_lr\n",
    "        self.lr_min = lr_min\n",
    "        self.lr_max = lr_max\n",
    "        \n",
    "        # History tracking with appropriate length\n",
    "        self.history_len = history_len\n",
    "        self.recent_lrs = deque(maxlen=history_len)\n",
    "        self.loss_history = deque(maxlen=history_len)\n",
    "        self.grad_history = deque(maxlen=history_len)\n",
    "        self.improvement_history = deque(maxlen=history_len)\n",
    "        \n",
    "        # State tracking\n",
    "        self.epoch = 0\n",
    "        self.steps = 0\n",
    "        self.best_loss = float('inf')\n",
    "        self.loss_plateau_count = 0\n",
    "        self.consecutive_increases = 0\n",
    "        self.consecutive_decreases = 0\n",
    "        \n",
    "        # Adaptive memory\n",
    "        self.successful_moves = {'increase': 0, 'decrease': 0, 'hold': 0}\n",
    "        self.total_moves = {'increase': 0, 'decrease': 0, 'hold': 0}\n",
    "        self.context_memories = []\n",
    "        \n",
    "        # Initialize with first learning rate\n",
    "        self.recent_lrs.append(initial_lr)\n",
    "        \n",
    "        # Performance tracking\n",
    "        self.validation_history = []\n",
    "        self.best_accuracy = 0.0\n",
    "        self.accuracy_not_improved = 0\n",
    "        \n",
    "    def on_epoch_start(self):\n",
    "        \"\"\"Called at the beginning of each epoch\"\"\"\n",
    "        self.epoch += 1\n",
    "        \n",
    "        # Reset batch-level counters\n",
    "        self.batch_steps = 0\n",
    "        \n",
    "        # Add epoch-based NAL beliefs\n",
    "        self.add_epoch_beliefs()\n",
    "        \n",
    "    def add_epoch_beliefs(self):\n",
    "        \"\"\"Add epoch-specific beliefs to the NAL engine\"\"\"\n",
    "        # Define training phase based on epoch\n",
    "        early_training = self.epoch <= 3\n",
    "        mid_training = 3 < self.epoch <= 10\n",
    "        late_training = self.epoch > 10\n",
    "        \n",
    "        # Add beliefs with high confidence\n",
    "        self.engine.add_belief('early_training', 1.0 if early_training else 0.0, 0.95)\n",
    "        self.engine.add_belief('mid_training', 1.0 if mid_training else 0.0, 0.95)\n",
    "        self.engine.add_belief('late_training', 1.0 if late_training else 0.0, 0.95)\n",
    "        \n",
    "        # Add phase-specific implications\n",
    "        if early_training:\n",
    "            # In early training, be more exploratory\n",
    "            self.engine.add_implication('grad_large', 'should_decrease_lr', 0.9, 0.9)\n",
    "            self.engine.add_implication('step_small', 'should_increase_lr', 0.9, 0.9)\n",
    "        elif mid_training:\n",
    "            # In mid training, balance exploration vs exploitation\n",
    "            self.engine.add_implication('performance_improving', 'continue_current_strategy', 0.8, 0.9)\n",
    "            self.engine.add_implication('oscillating', 'should_decrease_lr', 0.8, 0.8)\n",
    "        else:\n",
    "            # In late training, be more conservative\n",
    "            self.engine.add_implication('plateau_detected', 'should_decrease_lr', 0.7, 0.8)\n",
    "            self.engine.add_implication('good_performance', 'should_decrease_lr_gently', 0.8, 0.7)\n",
    "    \n",
    "    def detect_plateau(self, window=5, threshold=0.005):\n",
    "        \"\"\"Detect if loss has plateaued\"\"\"\n",
    "        if len(self.loss_history) < window + 1:\n",
    "            return False\n",
    "            \n",
    "        recent = list(self.loss_history)[-window:]\n",
    "        # Calculate relative changes\n",
    "        rel_changes = [abs(recent[i] - recent[i-1]) / (abs(recent[i-1]) + 1e-10) \n",
    "                       for i in range(1, len(recent))]\n",
    "        \n",
    "        return max(rel_changes) < threshold\n",
    "    \n",
    "    def detect_oscillation(self, window=5):\n",
    "        \"\"\"Detect if learning rate is oscillating\"\"\"\n",
    "        if len(self.recent_lrs) < window:\n",
    "            return False\n",
    "            \n",
    "        # Get most recent LR changes\n",
    "        recent = list(self.recent_lrs)[-window:]\n",
    "        changes = [np.sign(recent[i] - recent[i-1]) for i in range(1, len(recent))]\n",
    "        sign_flips = sum(changes[i] != changes[i-1] for i in range(1, len(changes)))\n",
    "        \n",
    "        # Consider oscillating if more than half the changes flip sign\n",
    "        return sign_flips >= (window - 2) // 2\n",
    "    \n",
    "    def calculate_trends(self, window=5):\n",
    "        \"\"\"Calculate trends in metrics\"\"\"\n",
    "        if len(self.grad_history) < window or len(self.improvement_history) < window:\n",
    "            return 0.0, 0.0\n",
    "        \n",
    "        # Get recent values\n",
    "        recent_grads = list(self.grad_history)[-window:]\n",
    "        recent_imps = list(self.improvement_history)[-window:]\n",
    "        \n",
    "        # Linear trend calculation (simple slope)\n",
    "        grad_trend = (recent_grads[-1] - recent_grads[0]) / (window - 1)\n",
    "        imp_trend = (recent_imps[-1] - recent_imps[0]) / (window - 1)\n",
    "        \n",
    "        return grad_trend, imp_trend\n",
    "    \n",
    "    def create_context_vector(self):\n",
    "        \"\"\"Create a context vector representing current training state\"\"\"\n",
    "        return {\n",
    "            'epoch_phase': 'early' if self.epoch <= 3 else 'mid' if self.epoch <= 10 else 'late',\n",
    "            'loss_trend': 'decreasing' if self.loss_is_decreasing() else 'plateau' if self.detect_plateau() else 'increasing',\n",
    "            'grad_trend': 'decreasing' if self.grad_is_decreasing() else 'stable' if self.grad_is_stable() else 'increasing',\n",
    "            'oscillating': self.detect_oscillation(),\n",
    "            'lr_position': 'min' if self.is_near_min_lr() else 'max' if self.is_near_max_lr() else 'mid',\n",
    "            'accuracy': self.best_accuracy,\n",
    "        }\n",
    "        \n",
    "    def context_similarity(self, ctx1, ctx2):\n",
    "        \"\"\"Calculate similarity between two context vectors\"\"\"\n",
    "        # Compare discrete features\n",
    "        matches = sum(1 for k in ctx1 if k in ctx2 and ctx1[k] == ctx2[k])\n",
    "        total = len(set(ctx1.keys()).union(ctx2.keys()))\n",
    "        \n",
    "        # Special handling for accuracy (continuous)\n",
    "        if 'accuracy' in ctx1 and 'accuracy' in ctx2:\n",
    "            acc_diff = abs(ctx1['accuracy'] - ctx2['accuracy'])\n",
    "            acc_sim = max(0, 1 - acc_diff / 0.2)  # Within 20% is similar\n",
    "            return (matches + acc_sim) / (total + 1)\n",
    "        \n",
    "        return matches / total if total > 0 else 0\n",
    "    \n",
    "    def remember_successful_action(self, action, context):\n",
    "        \"\"\"Record successful actions with context\"\"\"\n",
    "        self.context_memories.append({\n",
    "            'context': context,\n",
    "            'action': action,\n",
    "            'success': True,\n",
    "            'lr_factor': self.recent_lrs[-1] / self.recent_lrs[-2] if len(self.recent_lrs) >= 2 else 1.0,\n",
    "            'confidence': 0.8\n",
    "        })\n",
    "        \n",
    "        # Update success statistics\n",
    "        self.successful_moves[action] += 1\n",
    "        self.total_moves[action] += 1\n",
    "    \n",
    "    def remember_failed_action(self, action, context):\n",
    "        \"\"\"Record failed actions with context\"\"\"\n",
    "        self.context_memories.append({\n",
    "            'context': context,\n",
    "            'action': action,\n",
    "            'success': False,\n",
    "            'lr_factor': self.recent_lrs[-1] / self.recent_lrs[-2] if len(self.recent_lrs) >= 2 else 1.0,\n",
    "            'confidence': 0.8\n",
    "        })\n",
    "        \n",
    "        # Update success statistics\n",
    "        self.total_moves[action] += 1\n",
    "    \n",
    "    def get_success_rate(self, action):\n",
    "        \"\"\"Get success rate for a particular action\"\"\"\n",
    "        if self.total_moves[action] == 0:\n",
    "            return 0.5  # Default to neutral\n",
    "        return self.successful_moves[action] / self.total_moves[action]\n",
    "    \n",
    "    def loss_is_decreasing(self, window=3):\n",
    "        \"\"\"Check if loss is decreasing over recent iterations\"\"\"\n",
    "        if len(self.loss_history) < window:\n",
    "            return False\n",
    "        recent = list(self.loss_history)[-window:]\n",
    "        return recent[0] > recent[-1]\n",
    "    \n",
    "    def grad_is_decreasing(self, window=3):\n",
    "        \"\"\"Check if gradient norm is decreasing\"\"\"\n",
    "        if len(self.grad_history) < window:\n",
    "            return False\n",
    "        recent = list(self.grad_history)[-window:]\n",
    "        return recent[0] > recent[-1]\n",
    "    \n",
    "    def grad_is_stable(self, window=3, threshold=0.1):\n",
    "        \"\"\"Check if gradient norm is stable\"\"\"\n",
    "        if len(self.grad_history) < window:\n",
    "            return False\n",
    "        recent = list(self.grad_history)[-window:]\n",
    "        rel_changes = [abs(recent[i] - recent[i-1]) / (abs(recent[i-1]) + 1e-10) \n",
    "                     for i in range(1, len(recent))]\n",
    "        return max(rel_changes) < threshold\n",
    "    \n",
    "    def is_near_min_lr(self):\n",
    "        \"\"\"Check if current LR is near minimum\"\"\"\n",
    "        return self.lr < self.lr_min * 1.2\n",
    "    \n",
    "    def is_near_max_lr(self):\n",
    "        \"\"\"Check if current LR is near maximum\"\"\"\n",
    "        return self.lr > self.lr_max * 0.8\n",
    "        \n",
    "    def observe(self, improvement, grad_norm, step_ratio, current_loss, accuracy=None):\n",
    "        \"\"\"Observe training metrics and update NAL beliefs\"\"\"\n",
    "        self.steps += 1\n",
    "        \n",
    "        # Store metrics history\n",
    "        self.loss_history.append(current_loss)\n",
    "        self.grad_history.append(grad_norm)\n",
    "        self.improvement_history.append(improvement)\n",
    "        \n",
    "        if accuracy is not None:\n",
    "            self.validation_history.append(accuracy)\n",
    "            if accuracy > self.best_accuracy:\n",
    "                self.best_accuracy = accuracy\n",
    "                self.accuracy_not_improved = 0\n",
    "            else:\n",
    "                self.accuracy_not_improved += 1\n",
    "        \n",
    "        # Reset NAL engine for fresh reasoning\n",
    "        self.engine = NALEngine()\n",
    "        \n",
    "        # Add primary observations (NAL-1)\n",
    "        self.engine.add_belief('improved', float(improvement > 0), 0.9)\n",
    "        self.engine.add_belief('grad_norm', float(grad_norm), 0.95)\n",
    "        self.engine.add_belief('step_ratio', float(step_ratio), 0.95)\n",
    "        self.engine.add_belief('current_loss', float(current_loss), 0.95)\n",
    "        \n",
    "        # Add derived observations\n",
    "        plateau_detected = self.detect_plateau()\n",
    "        oscillation_detected = self.detect_oscillation()\n",
    "        grad_trend, imp_trend = self.calculate_trends()\n",
    "        \n",
    "        # Add thresholded beliefs\n",
    "        self.engine.add_belief('grad_small', float(grad_norm < 0.01), 0.8)\n",
    "        self.engine.add_belief('grad_large', float(grad_norm > 0.1), 0.8)\n",
    "        self.engine.add_belief('step_small', float(step_ratio < 0.001), 0.8)\n",
    "        self.engine.add_belief('step_large', float(step_ratio > 0.01), 0.8)\n",
    "        self.engine.add_belief('plateau_detected', float(plateau_detected), 0.8)\n",
    "        self.engine.add_belief('oscillating', float(oscillation_detected), 0.9)\n",
    "        \n",
    "        # Add trend beliefs\n",
    "        self.engine.add_belief('grad_decreasing', float(grad_trend < 0), 0.7)\n",
    "        self.engine.add_belief('improvement_increasing', float(imp_trend > 0), 0.7)\n",
    "        \n",
    "        # Add performance beliefs\n",
    "        if accuracy is not None:\n",
    "            self.engine.add_belief('good_performance', float(accuracy > 0.75), 0.9)\n",
    "            self.engine.add_belief('performance_improving', float(accuracy > self.best_accuracy * 0.98), 0.8)\n",
    "            self.engine.add_belief('accuracy_plateau', float(self.accuracy_not_improved > 2), 0.8)\n",
    "        \n",
    "        # Add epoch-specific beliefs\n",
    "        self.add_epoch_beliefs()\n",
    "        \n",
    "        # Add core reasoning rules\n",
    "        self.add_core_implications()\n",
    "        \n",
    "        # Run inference\n",
    "        self.engine.inference_cycle(max_cycles=10)\n",
    "    \n",
    "    def add_core_implications(self):\n",
    "        \"\"\"Add core implication rules to NAL engine\"\"\"\n",
    "        # Basic adaptation rules\n",
    "        self.engine.add_implication('step_small', 'should_increase_lr', 0.8, 0.8)\n",
    "        self.engine.add_implication('step_large', 'should_decrease_lr', 0.8, 0.8)\n",
    "        self.engine.add_implication('oscillating', 'should_decrease_lr', 0.9, 0.9)\n",
    "        self.engine.add_implication('plateau_detected', 'should_change_strategy', 0.8, 0.7)\n",
    "        \n",
    "        # Performance-based adaptation\n",
    "        self.engine.add_implication('good_performance', 'continue_current_strategy', 0.7, 0.8)\n",
    "        self.engine.add_implication('accuracy_plateau', 'should_decrease_lr', 0.8, 0.7)\n",
    "        \n",
    "        # Gradient-based adaptations\n",
    "        self.engine.add_implication('grad_large', 'should_decrease_lr', 0.8, 0.8)\n",
    "        self.engine.add_implication('grad_small', 'converging', 0.7, 0.7)\n",
    "        self.engine.add_implication('converging', 'should_decrease_lr_gently', 0.7, 0.7)\n",
    "        \n",
    "        # Add success rate beliefs from memory\n",
    "        for action in ['increase', 'decrease', 'hold']:\n",
    "            success_rate = self.get_success_rate(action)\n",
    "            self.engine.add_belief(f'{action}_success_rate', success_rate, 0.8)\n",
    "            \n",
    "        # Higher success rate should promote that action\n",
    "        self.engine.add_implication('increase_success_rate', 'should_increase_lr', 0.8, 0.7)\n",
    "        self.engine.add_implication('decrease_success_rate', 'should_decrease_lr', 0.8, 0.7)\n",
    "        self.engine.add_implication('hold_success_rate', 'should_hold_lr', 0.8, 0.7)\n",
    "    \n",
    "    def adjust_lr(self):\n",
    "        \"\"\"Adjust learning rate based on NAL inference results\"\"\"\n",
    "        # Get key belief strengths\n",
    "        should_increase = self.engine.get_belief_strength('should_increase_lr')\n",
    "        should_decrease = self.engine.get_belief_strength('should_decrease_lr')\n",
    "        should_decrease_gently = self.engine.get_belief_strength('should_decrease_lr_gently')\n",
    "        should_hold = self.engine.get_belief_strength('should_hold_lr')\n",
    "        should_change = self.engine.get_belief_strength('should_change_strategy')\n",
    "        should_continue = self.engine.get_belief_strength('continue_current_strategy')\n",
    "        \n",
    "        # Default action\n",
    "        action = 'hold'\n",
    "        factor = 1.0\n",
    "        \n",
    "        # Handle special case for early learning\n",
    "        if self.epoch <= 1 and self.steps < 100:\n",
    "            # Very early in training - gradual increase\n",
    "            factor = min(1.05, 1.0 + 0.005 * min(self.steps, 20))\n",
    "            action = 'early_warmup'\n",
    "        \n",
    "        # Handle case where model is stuck at extremes\n",
    "        elif self.is_near_min_lr() and should_increase > 0.3:\n",
    "            # Significant but controlled increase from minimum\n",
    "            factor = 2.0\n",
    "            action = 'escape_minimum'\n",
    "            self.consecutive_increases += 1\n",
    "            self.consecutive_decreases = 0\n",
    "            \n",
    "        elif self.is_near_max_lr() and should_decrease > 0.3:\n",
    "            # Significant decrease from maximum\n",
    "            factor = 0.5\n",
    "            action = 'escape_maximum'\n",
    "            self.consecutive_decreases += 1\n",
    "            self.consecutive_increases = 0\n",
    "        \n",
    "        # Handle strong signals\n",
    "        elif should_increase > 0.3 and should_increase > should_decrease:\n",
    "            # Adjust factor based on confidence and historical success\n",
    "            base_factor = 1.1\n",
    "            if self.consecutive_increases > 1:\n",
    "                # More aggressive for consecutive increases\n",
    "                base_factor = min(1.2, 1.1 + 0.02 * self.consecutive_increases)\n",
    "            \n",
    "            factor = base_factor\n",
    "            action = 'increase'\n",
    "            self.consecutive_increases += 1\n",
    "            self.consecutive_decreases = 0\n",
    "            \n",
    "        elif should_decrease > 0.6 and should_decrease > should_increase:\n",
    "            # Adjust factor based on confidence\n",
    "            base_factor = 0.9\n",
    "            if self.consecutive_decreases > 1:\n",
    "                # More aggressive for consecutive decreases\n",
    "                base_factor = max(0.8, 0.9 - 0.02 * self.consecutive_decreases)\n",
    "            \n",
    "            factor = base_factor\n",
    "            action = 'decrease'\n",
    "            self.consecutive_decreases += 1\n",
    "            self.consecutive_increases = 0\n",
    "            \n",
    "        elif should_decrease_gently > 0.6:\n",
    "            # Gentle decrease (for converging but still improving)\n",
    "            factor = 0.95\n",
    "            action = 'gentle_decrease'\n",
    "            self.consecutive_decreases += 1\n",
    "            self.consecutive_increases = 0\n",
    "            \n",
    "        elif should_change > 0.7 and should_hold < 0.3:\n",
    "            # Need to change strategy but no clear direction\n",
    "            \n",
    "            # Check if we've been increasing or decreasing\n",
    "            if self.consecutive_increases > self.consecutive_decreases:\n",
    "                # Try opposite direction\n",
    "                factor = 0.85\n",
    "                action = 'strategy_reverse_to_decrease'\n",
    "                self.consecutive_decreases = 1\n",
    "                self.consecutive_increases = 0\n",
    "            else:\n",
    "                # Try opposite direction\n",
    "                factor = 1.15\n",
    "                action = 'strategy_reverse_to_increase'\n",
    "                self.consecutive_increases = 1\n",
    "                self.consecutive_decreases = 0\n",
    "                \n",
    "        elif should_continue > 0.6:\n",
    "            # Continue current strategy with small adjustments\n",
    "            \n",
    "            # Based on recent trajectory\n",
    "            if self.consecutive_increases > 0:\n",
    "                factor = 1.05\n",
    "                action = 'continue_increase'\n",
    "            elif self.consecutive_decreases > 0:\n",
    "                factor = 0.95\n",
    "                action = 'continue_decrease'\n",
    "            else:\n",
    "                # Tiny random adjustment to avoid perfect stagnation\n",
    "                factor = 1.0 + (random.random() - 0.5) * 0.1\n",
    "                action = 'micro_adjustment'\n",
    "        \n",
    "        # Apply change with bounds\n",
    "        old_lr = self.lr\n",
    "        self.lr = np.clip(self.lr * factor, self.lr_min, self.lr_max)\n",
    "        \n",
    "        # Store LR history\n",
    "        if len(self.recent_lrs) >= self.history_len:\n",
    "            self.recent_lrs.popleft()\n",
    "        self.recent_lrs.append(self.lr)\n",
    "        \n",
    "        # Remember current context\n",
    "        current_context = self.create_context_vector()\n",
    "        \n",
    "        # Remember this action for future reference\n",
    "        self.last_action = action\n",
    "        self.last_context = current_context\n",
    "        # Force occasional exploration to break plateaus\n",
    "        if self.epoch % 5 == 0 and random.random() < 0.5:  # Every 5th epoch with 50% probability\n",
    "            if random.random() < 0.5:\n",
    "                self.lr = self.lr * 0.98  # Significant decrease\n",
    "                action = 'forced_exploration_decrease'\n",
    "            else:\n",
    "                self.lr = self.lr * 1.02  # Significant increase\n",
    "                action = 'forced_exploration_increase'\n",
    "        \n",
    "        return self.lr\n",
    "    \n",
    "    def update_action_success(self, was_successful):\n",
    "        \"\"\"Update memory based on whether last action was successful\"\"\"\n",
    "        if hasattr(self, 'last_action') and hasattr(self, 'last_context'):\n",
    "            if was_successful:\n",
    "                self.remember_successful_action(self.last_action, self.last_context)\n",
    "            else:\n",
    "                self.remember_failed_action(self.last_action, self.last_context)\n",
    "\n",
    "# Add these at the end of your NAL implementation file\n",
    "\n",
    "class EnhancedNALLRTunerWithCheckpoint(NALLRTunerFinal):\n",
    "    \"\"\"Enhanced NAL learning rate tuner with checkpointing capabilities\"\"\"\n",
    "    def __init__(self, initial_lr=1e-4, lr_min=1e-6, lr_max=0.1, warmup_steps=0, debug=False):\n",
    "        super().__init__(initial_lr, lr_min, lr_max)\n",
    "        self.warmup_steps = warmup_steps\n",
    "        self.debug = debug\n",
    "        \n",
    "        # Checkpointing\n",
    "        self.model = None\n",
    "        self.best_state = None\n",
    "        self.best_accuracy = 0.0\n",
    "        self.best_loss = float('inf')\n",
    "        \n",
    "        # Recent metrics\n",
    "        self.recent_accuracy = 0.0\n",
    "        \n",
    "        # History for analysis\n",
    "        self.metrics_history = []\n",
    "        self.decision_history = []\n",
    "        \n",
    "        # Procedural knowledge\n",
    "        self.procedural_patterns = {}\n",
    "        self.applied_procedures = []\n",
    "        \n",
    "        # Add warmup schedule for early training\n",
    "        self.warmup_counter = 0\n",
    "        \n",
    "    def set_model(self, model):\n",
    "        \"\"\"Set model reference for checkpointing\"\"\"\n",
    "        self.model = model\n",
    "        # Initialize best state\n",
    "        if hasattr(model, 'state_dict'):\n",
    "            self.best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}\n",
    "            \n",
    "    def set_recent_accuracy(self, accuracy):\n",
    "        \"\"\"Set recent validation accuracy\"\"\"\n",
    "        self.recent_accuracy = accuracy\n",
    "        \n",
    "        # Track in metrics history\n",
    "        self.metrics_history.append({\n",
    "            'step': self.steps,\n",
    "            'epoch': self.epoch,\n",
    "            'lr': self.lr,\n",
    "            'accuracy': accuracy,\n",
    "            'loss': self.loss_history[-1] if self.loss_history else None\n",
    "        })\n",
    "        \n",
    "    def update_best_state(self, accuracy, loss):\n",
    "        \"\"\"Update best model state if performance improved\"\"\"\n",
    "        improved = False\n",
    "        \n",
    "        # Check if accuracy improved\n",
    "        if accuracy > self.best_accuracy:\n",
    "            self.best_accuracy = accuracy\n",
    "            improved = True\n",
    "            \n",
    "        # Check if loss improved significantly\n",
    "        if loss < self.best_loss * 0.98:  # 2% improvement threshold\n",
    "            self.best_loss = loss\n",
    "            improved = True\n",
    "            \n",
    "        # Update best state if improved and model exists\n",
    "        if improved and hasattr(self, 'model') and self.model is not None:\n",
    "            self.best_state = {k: v.cpu().clone() for k, v in self.model.state_dict().items()}\n",
    "            \n",
    "            # Log this decision\n",
    "            self.decision_history.append({\n",
    "                'step': self.steps,\n",
    "                'epoch': self.epoch,\n",
    "                'action': 'save_checkpoint',\n",
    "                'lr': self.lr,\n",
    "                'accuracy': accuracy,\n",
    "                'loss': loss\n",
    "            })\n",
    "            \n",
    "        return improved\n",
    "        \n",
    "    def restore_best_state(self):\n",
    "        \"\"\"Restore model to best checkpointed state\"\"\"\n",
    "        if self.model is not None and self.best_state is not None:\n",
    "            self.model.load_state_dict(self.best_state)\n",
    "            \n",
    "            # Log this decision\n",
    "            self.decision_history.append({\n",
    "                'step': self.steps,\n",
    "                'epoch': self.epoch,\n",
    "                'action': 'restore_checkpoint',\n",
    "                'lr': self.lr\n",
    "            })\n",
    "            \n",
    "            return True\n",
    "        return False\n",
    "        \n",
    "    def update_successful_ranges(self, accuracy):\n",
    "        \"\"\"Update knowledge about successful learning rate ranges\"\"\"\n",
    "        # Only consider significant accuracy \n",
    "        if accuracy > 0.5:  # Basic threshold for meaningful performance\n",
    "            lr_range = 'high' if self.lr > self.lr_max * 0.5 else 'medium' if self.lr > self.lr_min * 5 else 'low'\n",
    "            epoch_phase = 'early' if self.epoch <= 3 else 'mid' if self.epoch <= 10 else 'late'\n",
    "            \n",
    "            # Create key for this context\n",
    "            context_key = f\"{epoch_phase}_{lr_range}\"\n",
    "            \n",
    "            # Update performance statistics for this range\n",
    "            if context_key not in self.procedural_patterns:\n",
    "                self.procedural_patterns[context_key] = {\n",
    "                    'count': 0,\n",
    "                    'total_accuracy': 0.0,\n",
    "                    'max_accuracy': 0.0,\n",
    "                    'lr_values': []\n",
    "                }\n",
    "                \n",
    "            pattern = self.procedural_patterns[context_key]\n",
    "            pattern['count'] += 1\n",
    "            pattern['total_accuracy'] += accuracy\n",
    "            pattern['max_accuracy'] = max(pattern['max_accuracy'], accuracy)\n",
    "            pattern['lr_values'].append(self.lr)\n",
    "            \n",
    "            # Log application of pattern\n",
    "            self.applied_procedures.append({\n",
    "                'step': self.steps,\n",
    "                'epoch': self.epoch,\n",
    "                'pattern': context_key,\n",
    "                'lr': self.lr,\n",
    "                'accuracy': accuracy,\n",
    "                'similarity': 1.0  # For plotting\n",
    "            })\n",
    "            \n",
    "    def get_best_lr_for_context(self):\n",
    "        \"\"\"Get best learning rate for current context based on history\"\"\"\n",
    "        epoch_phase = 'early' if self.epoch <= 3 else 'mid' if self.epoch <= 10 else 'late'\n",
    "        \n",
    "        # Find best performing pattern for this phase\n",
    "        best_accuracy = 0.0\n",
    "        best_lr = self.lr  # Default to current\n",
    "        \n",
    "        for key, pattern in self.procedural_patterns.items():\n",
    "            if key.startswith(f\"{epoch_phase}_\") and pattern['max_accuracy'] > best_accuracy:\n",
    "                best_accuracy = pattern['max_accuracy']\n",
    "                # Get average of top 3 performing LRs in this pattern\n",
    "                if len(pattern['lr_values']) > 0:\n",
    "                    sorted_lrs = sorted(pattern['lr_values'])\n",
    "                    mid_idx = len(sorted_lrs) // 2\n",
    "                    best_lr = sorted_lrs[mid_idx]  # Use median value\n",
    "                    \n",
    "        return best_lr\n",
    "        \n",
    "    def observe(self, improvement, grad_norm, step_ratio, current_loss, accuracy=None):\n",
    "        \"\"\"Enhanced observation with warmup handling\"\"\"\n",
    "        # Handle warmup period\n",
    "        if self.warmup_counter < self.warmup_steps:\n",
    "            self.warmup_counter += 1\n",
    "            warmup_factor = min(1.0, self.warmup_counter / self.warmup_steps)\n",
    "            self.lr = self.initial_lr * warmup_factor\n",
    "            \n",
    "            # Store metrics but don't adjust LR during warmup\n",
    "            self.loss_history.append(current_loss)\n",
    "            self.grad_history.append(grad_norm)\n",
    "            self.improvement_history.append(improvement)\n",
    "            \n",
    "            # Store LR history\n",
    "            self.recent_lrs.append(self.lr)\n",
    "            \n",
    "            return self.lr\n",
    "            \n",
    "        # Normal observation after warmup\n",
    "        return super().observe(improvement, grad_norm, step_ratio, current_loss, accuracy)\n",
    "        \n",
    "    def adjust_lr(self):\n",
    "        \"\"\"Enhanced learning rate adjustment with extended heuristics\"\"\"\n",
    "        # Basic adjustment from parent class\n",
    "        new_lr = super().adjust_lr()\n",
    "        \n",
    "        # Store decision in history\n",
    "        self.decision_history.append({\n",
    "            'step': self.steps,\n",
    "            'epoch': self.epoch,\n",
    "            'action': self.last_action,\n",
    "            'lr_old': self.recent_lrs[-2] if len(self.recent_lrs) > 1 else None,\n",
    "            'lr_new': new_lr,\n",
    "            'factor': new_lr / (self.recent_lrs[-2] if len(self.recent_lrs) > 1 else 1.0)\n",
    "        })\n",
    "        \n",
    "        # Special case: if we've been in late training for a while, consider best historical LR\n",
    "        if self.epoch > 15 and len(self.procedural_patterns) > 3:\n",
    "            # Every 3 epochs, try the historically best LR for this context\n",
    "            if self.epoch % 3 == 0 and random.random() < 0.3:  # 30% chance\n",
    "                historical_best_lr = self.get_best_lr_for_context()\n",
    "                # Only make significant changes\n",
    "                if abs(historical_best_lr - new_lr) / new_lr > 0.2:  # More than 20% difference\n",
    "                    new_lr = historical_best_lr\n",
    "                    self.decision_history[-1]['action'] = 'historical_best'\n",
    "                    self.decision_history[-1]['lr_new'] = new_lr\n",
    "        \n",
    "        return new_lr\n",
    "\n",
    "\n",
    "def train_with_enhanced_nal(model, train_loader, optimizer, criterion, device, \n",
    "                           epochs=10, validation_loader=None, debug=False):\n",
    "    \"\"\"Train a model using the enhanced NAL learning rate tuner\"\"\"\n",
    "    # Initialize the enhanced NAL tuner\n",
    "    tuner = EnhancedNALLRTunerWithCheckpoint(\n",
    "        initial_lr=optimizer.param_groups[0]['lr'],\n",
    "        lr_min=1e-6,\n",
    "        lr_max=0.1,\n",
    "        #warmup_steps=len(train_loader),\n",
    "        warmup_steps=100,\n",
    "        debug=debug\n",
    "    )\n",
    "    tuner.set_model(model)\n",
    "    \n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    val_accuracies = []\n",
    "    lr_history = []\n",
    "    \n",
    "    for epoch in range(1, epochs + 1):\n",
    "        # Train\n",
    "        model.train()\n",
    "        tuner.on_epoch_start()\n",
    "        \n",
    "        running_loss = 0.0\n",
    "        prev_loss = None\n",
    "        weight_norm = sum(p.norm().item() ** 2 for p in model.parameters()) ** 0.5\n",
    "        \n",
    "        for i, (inputs, targets) in enumerate(train_loader):\n",
    "            inputs, targets = inputs.to(device), targets.to(device)\n",
    "            \n",
    "            # Forward/backward pass\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, targets)\n",
    "            loss.backward()\n",
    "            \n",
    "            # Calculate metrics for NAL\n",
    "            current_loss = loss.item()\n",
    "            running_loss += current_loss\n",
    "            \n",
    "            grad_norm = sum(p.grad.norm().item() ** 2 for p in model.parameters() if p.grad is not None) ** 0.5\n",
    "            improvement = 0.0 if prev_loss is None else prev_loss - current_loss\n",
    "            prev_loss = current_loss\n",
    "            \n",
    "            # Update NAL tuner\n",
    "            tuner.observe(improvement, grad_norm, weight_norm, current_loss)\n",
    "            new_lr = tuner.adjust_lr()\n",
    "            \n",
    "            # Update optimizer's learning rate\n",
    "            for pg in optimizer.param_groups:\n",
    "                pg['lr'] = new_lr\n",
    "                \n",
    "            # Apply gradients\n",
    "            optimizer.step()\n",
    "            \n",
    "            # Calculate new weight norm\n",
    "            if (i + 1) % 10 == 0:\n",
    "                weight_norm = sum(p.norm().item() ** 2 for p in model.parameters()) ** 0.5\n",
    "                \n",
    "            if debug and i % 100 == 0:\n",
    "                print(f\"Epoch {epoch}, Batch {i}: Loss={current_loss:.4f}, LR={new_lr:.6f}\")\n",
    "        \n",
    "        # Calculate average training loss\n",
    "        avg_train_loss = running_loss / len(train_loader)\n",
    "        train_losses.append(avg_train_loss)\n",
    "        \n",
    "        # Validate if loader provided\n",
    "        if validation_loader:\n",
    "            model.eval()\n",
    "            val_loss = 0.0\n",
    "            correct = 0\n",
    "            total = 0\n",
    "            \n",
    "            with torch.no_grad():\n",
    "                for inputs, targets in validation_loader:\n",
    "                    inputs, targets = inputs.to(device), targets.to(device)\n",
    "                    outputs = model(inputs)\n",
    "                    loss = criterion(outputs, targets)\n",
    "                    \n",
    "                    val_loss += loss.item()\n",
    "                    _, predicted = torch.max(outputs.data, 1)\n",
    "                    total += targets.size(0)\n",
    "                    correct += (predicted == targets).sum().item()\n",
    "            \n",
    "            avg_val_loss = val_loss / len(validation_loader)\n",
    "            accuracy = correct / total\n",
    "            \n",
    "            val_losses.append(avg_val_loss)\n",
    "            val_accuracies.append(accuracy)\n",
    "            \n",
    "            # Update NAL tuner with validation results\n",
    "            tuner.set_recent_accuracy(accuracy)\n",
    "            tuner.update_best_state(accuracy, avg_val_loss)\n",
    "            tuner.update_successful_ranges(accuracy)\n",
    "            \n",
    "            print(f\"Epoch {epoch}: train_loss={avg_train_loss:.4f}, val_loss={avg_val_loss:.4f}, accuracy={accuracy:.2%}, lr={new_lr:.6f}\")\n",
    "        else:\n",
    "            print(f\"Epoch {epoch}: train_loss={avg_train_loss:.4f}, lr={new_lr:.6f}\")\n",
    "        \n",
    "        # Store current learning rate\n",
    "        lr_history.append(new_lr)\n",
    "    \n",
    "    # Return training history\n",
    "    return {\n",
    "        'train_losses': train_losses,\n",
    "        'val_losses': val_losses,\n",
    "        'val_accuracies': val_accuracies,\n",
    "        'lr_history': lr_history,\n",
    "        'tuner_metrics': tuner.metrics_history,\n",
    "        'tuner_decisions': tuner.decision_history,\n",
    "        'procedural_patterns': tuner.procedural_patterns,\n",
    "        'applied_procedures': tuner.applied_procedures\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9692c162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: mps\n",
      "Starting training with Enhanced NAL LR Tuner for 50 epochs...\n",
      "Initial learning rate: 1e-05\n",
      "Epoch 1, Batch 0: Loss=2.4309, LR=0.000001\n",
      "Epoch 1, Batch 100: Loss=2.2208, LR=0.000010\n",
      "Epoch 1, Batch 200: Loss=1.1539, LR=0.001007\n",
      "Epoch 1, Batch 300: Loss=0.9367, LR=0.001007\n",
      "Epoch 1: train_loss=1.4852, val_loss=0.6684, accuracy=74.88%, lr=0.001007\n",
      "Epoch 2, Batch 0: Loss=0.8382, LR=0.001007\n",
      "Epoch 2, Batch 100: Loss=0.7719, LR=0.001007\n",
      "Epoch 2, Batch 200: Loss=0.6626, LR=0.001007\n",
      "Epoch 2, Batch 300: Loss=0.8230, LR=0.001007\n",
      "Epoch 2: train_loss=0.7426, val_loss=0.5991, accuracy=77.30%, lr=0.001007\n",
      "Epoch 3, Batch 0: Loss=0.7195, LR=0.001007\n",
      "Epoch 3, Batch 100: Loss=0.7033, LR=0.001007\n",
      "Epoch 3, Batch 200: Loss=0.7080, LR=0.001007\n",
      "Epoch 3, Batch 300: Loss=0.6987, LR=0.001007\n",
      "Epoch 3: train_loss=0.6749, val_loss=0.5518, accuracy=79.50%, lr=0.001007\n",
      "Epoch 4, Batch 0: Loss=0.6547, LR=0.001007\n",
      "Epoch 4, Batch 100: Loss=0.5890, LR=0.001007\n",
      "Epoch 4, Batch 200: Loss=0.6287, LR=0.001007\n",
      "Epoch 4, Batch 300: Loss=0.6371, LR=0.001007\n",
      "Epoch 4: train_loss=0.6412, val_loss=0.5311, accuracy=80.03%, lr=0.001007\n",
      "Epoch 5, Batch 0: Loss=0.6233, LR=0.000987\n",
      "Epoch 5, Batch 100: Loss=0.6019, LR=0.000868\n",
      "Epoch 5, Batch 200: Loss=0.7291, LR=0.000811\n",
      "Epoch 5, Batch 300: Loss=0.5870, LR=0.000836\n",
      "Epoch 5: train_loss=0.6119, val_loss=0.5058, accuracy=81.40%, lr=0.000736\n",
      "Epoch 6, Batch 0: Loss=0.5351, LR=0.000736\n",
      "Epoch 6, Batch 100: Loss=0.5829, LR=0.000736\n",
      "Epoch 6, Batch 200: Loss=0.4905, LR=0.000736\n",
      "Epoch 6, Batch 300: Loss=0.5799, LR=0.000736\n",
      "Epoch 6: train_loss=0.5953, val_loss=0.4873, accuracy=82.36%, lr=0.000736\n",
      "Epoch 7, Batch 0: Loss=0.5619, LR=0.000736\n",
      "Epoch 7, Batch 100: Loss=0.4880, LR=0.000736\n",
      "Epoch 7, Batch 200: Loss=0.6149, LR=0.000736\n",
      "Epoch 7, Batch 300: Loss=0.6080, LR=0.000736\n",
      "Epoch 7: train_loss=0.5840, val_loss=0.4772, accuracy=82.21%, lr=0.000736\n",
      "Epoch 8, Batch 0: Loss=0.5830, LR=0.000736\n",
      "Epoch 8, Batch 100: Loss=0.6043, LR=0.000736\n",
      "Epoch 8, Batch 200: Loss=0.6170, LR=0.000736\n",
      "Epoch 8, Batch 300: Loss=0.6112, LR=0.000736\n",
      "Epoch 8: train_loss=0.5695, val_loss=0.4684, accuracy=82.72%, lr=0.000736\n",
      "Epoch 9, Batch 0: Loss=0.4344, LR=0.000736\n",
      "Epoch 9, Batch 100: Loss=0.5505, LR=0.000736\n",
      "Epoch 9, Batch 200: Loss=0.7413, LR=0.000736\n",
      "Epoch 9, Batch 300: Loss=0.5289, LR=0.000736\n",
      "Epoch 9: train_loss=0.5671, val_loss=0.4727, accuracy=82.47%, lr=0.000736\n",
      "Epoch 10, Batch 0: Loss=0.5131, LR=0.000736\n",
      "Epoch 10, Batch 100: Loss=0.5121, LR=0.000929\n",
      "Epoch 10, Batch 200: Loss=0.5388, LR=0.001018\n",
      "Epoch 10, Batch 300: Loss=0.5152, LR=0.000843\n",
      "Epoch 10: train_loss=0.5635, val_loss=0.4579, accuracy=83.37%, lr=0.000833\n",
      "Epoch 11, Batch 0: Loss=0.6795, LR=0.000833\n",
      "Epoch 11, Batch 100: Loss=0.6453, LR=0.000833\n",
      "Epoch 11, Batch 200: Loss=0.5325, LR=0.000833\n",
      "Epoch 11, Batch 300: Loss=0.5492, LR=0.000833\n",
      "Epoch 11: train_loss=0.5547, val_loss=0.4553, accuracy=83.46%, lr=0.000833\n",
      "Epoch 12, Batch 0: Loss=0.4473, LR=0.000833\n",
      "Epoch 12, Batch 100: Loss=0.4232, LR=0.000833\n",
      "Epoch 12, Batch 200: Loss=0.5868, LR=0.000833\n",
      "Epoch 12, Batch 300: Loss=0.5593, LR=0.000833\n",
      "Epoch 12: train_loss=0.5482, val_loss=0.4531, accuracy=83.15%, lr=0.000833\n",
      "Epoch 13, Batch 0: Loss=0.5269, LR=0.000833\n",
      "Epoch 13, Batch 100: Loss=0.5773, LR=0.000833\n",
      "Epoch 13, Batch 200: Loss=0.7322, LR=0.000833\n",
      "Epoch 13, Batch 300: Loss=0.4730, LR=0.000833\n",
      "Epoch 13: train_loss=0.5412, val_loss=0.4478, accuracy=83.42%, lr=0.000833\n",
      "Epoch 14, Batch 0: Loss=0.5206, LR=0.000833\n",
      "Epoch 14, Batch 100: Loss=0.5822, LR=0.000833\n",
      "Epoch 14, Batch 200: Loss=0.6624, LR=0.000833\n",
      "Epoch 14, Batch 300: Loss=0.6096, LR=0.000833\n",
      "Epoch 14: train_loss=0.5351, val_loss=0.4442, accuracy=83.40%, lr=0.000833\n",
      "Epoch 15, Batch 0: Loss=0.5016, LR=0.000850\n",
      "Epoch 15, Batch 100: Loss=0.6083, LR=0.000761\n",
      "Epoch 15, Batch 200: Loss=0.4976, LR=0.000883\n",
      "Epoch 15, Batch 300: Loss=0.5002, LR=0.001045\n",
      "Epoch 15: train_loss=0.5376, val_loss=0.4626, accuracy=82.67%, lr=0.001170\n",
      "Epoch 16, Batch 0: Loss=0.5110, LR=0.001170\n",
      "Epoch 16, Batch 100: Loss=0.7699, LR=0.001170\n",
      "Epoch 16, Batch 200: Loss=0.5319, LR=0.001170\n",
      "Epoch 16, Batch 300: Loss=0.6250, LR=0.001170\n",
      "Epoch 16: train_loss=0.5449, val_loss=0.4344, accuracy=84.28%, lr=0.001170\n",
      "Epoch 17, Batch 0: Loss=0.4908, LR=0.001170\n",
      "Epoch 17, Batch 100: Loss=0.4833, LR=0.001170\n",
      "Epoch 17, Batch 200: Loss=0.4869, LR=0.001170\n",
      "Epoch 17, Batch 300: Loss=0.5574, LR=0.001170\n",
      "Epoch 17: train_loss=0.5333, val_loss=0.4352, accuracy=84.15%, lr=0.001170\n",
      "Epoch 18, Batch 0: Loss=0.5271, LR=0.001170\n",
      "Epoch 18, Batch 100: Loss=0.4760, LR=0.001170\n",
      "Epoch 18, Batch 200: Loss=0.5446, LR=0.001170\n",
      "Epoch 18, Batch 300: Loss=0.4601, LR=0.001170\n",
      "Epoch 18: train_loss=0.5317, val_loss=0.4486, accuracy=83.33%, lr=0.001170\n",
      "Epoch 19, Batch 0: Loss=0.4532, LR=0.001170\n",
      "Epoch 19, Batch 100: Loss=0.4470, LR=0.001170\n",
      "Epoch 19, Batch 200: Loss=0.5012, LR=0.001170\n",
      "Epoch 19, Batch 300: Loss=0.3855, LR=0.001170\n",
      "Epoch 19: train_loss=0.5274, val_loss=0.4308, accuracy=84.10%, lr=0.001170\n",
      "Epoch 20, Batch 0: Loss=0.4853, LR=0.001146\n",
      "Epoch 20, Batch 100: Loss=0.4605, LR=0.001160\n",
      "Epoch 20, Batch 200: Loss=0.5710, LR=0.001061\n",
      "Epoch 20, Batch 300: Loss=0.3900, LR=0.001010\n",
      "Epoch 20: train_loss=0.5259, val_loss=0.4435, accuracy=84.00%, lr=0.001326\n",
      "Epoch 21, Batch 0: Loss=0.5416, LR=0.001326\n",
      "Epoch 21, Batch 100: Loss=0.6086, LR=0.001326\n",
      "Epoch 21, Batch 200: Loss=0.5147, LR=0.001326\n",
      "Epoch 21, Batch 300: Loss=0.4730, LR=0.001326\n",
      "Epoch 21: train_loss=0.5256, val_loss=0.4294, accuracy=84.21%, lr=0.001326\n",
      "Epoch 22, Batch 0: Loss=0.4310, LR=0.001326\n",
      "Epoch 22, Batch 100: Loss=0.5660, LR=0.001326\n",
      "Epoch 22, Batch 200: Loss=0.5728, LR=0.001326\n",
      "Epoch 22, Batch 300: Loss=0.4603, LR=0.001326\n",
      "Epoch 22: train_loss=0.5229, val_loss=0.4271, accuracy=84.38%, lr=0.001326\n",
      "Epoch 23, Batch 0: Loss=0.4917, LR=0.001326\n",
      "Epoch 23, Batch 100: Loss=0.4664, LR=0.001326\n",
      "Epoch 23, Batch 200: Loss=0.5501, LR=0.001326\n",
      "Epoch 23, Batch 300: Loss=0.5818, LR=0.001326\n",
      "Epoch 23: train_loss=0.5180, val_loss=0.4421, accuracy=83.56%, lr=0.001326\n",
      "Epoch 24, Batch 0: Loss=0.5626, LR=0.001326\n",
      "Epoch 24, Batch 100: Loss=0.4831, LR=0.001326\n",
      "Epoch 24, Batch 200: Loss=0.4226, LR=0.001326\n",
      "Epoch 24, Batch 300: Loss=0.5177, LR=0.001326\n",
      "Epoch 24: train_loss=0.5167, val_loss=0.4287, accuracy=84.34%, lr=0.001326\n",
      "Epoch 25, Batch 0: Loss=0.5047, LR=0.001326\n",
      "Epoch 25, Batch 100: Loss=0.4846, LR=0.001163\n",
      "Epoch 25, Batch 200: Loss=0.4492, LR=0.001407\n",
      "Epoch 25, Batch 300: Loss=0.6239, LR=0.001508\n",
      "Epoch 25: train_loss=0.5127, val_loss=0.4339, accuracy=84.10%, lr=0.001754\n",
      "Epoch 26, Batch 0: Loss=0.5458, LR=0.001754\n",
      "Epoch 26, Batch 100: Loss=0.4640, LR=0.001754\n",
      "Epoch 26, Batch 200: Loss=0.6252, LR=0.001754\n",
      "Epoch 26, Batch 300: Loss=0.4291, LR=0.001754\n",
      "Epoch 26: train_loss=0.5163, val_loss=0.4287, accuracy=84.12%, lr=0.001754\n",
      "Epoch 27, Batch 0: Loss=0.5631, LR=0.001754\n",
      "Epoch 27, Batch 100: Loss=0.4397, LR=0.001754\n",
      "Epoch 27, Batch 200: Loss=0.5116, LR=0.001754\n",
      "Epoch 27, Batch 300: Loss=0.4983, LR=0.001754\n",
      "Epoch 27: train_loss=0.5152, val_loss=0.4205, accuracy=84.69%, lr=0.001754\n",
      "Epoch 28, Batch 0: Loss=0.5829, LR=0.001754\n",
      "Epoch 28, Batch 100: Loss=0.4597, LR=0.001754\n",
      "Epoch 28, Batch 200: Loss=0.5353, LR=0.001754\n",
      "Epoch 28, Batch 300: Loss=0.6077, LR=0.001754\n",
      "Epoch 28: train_loss=0.5142, val_loss=0.4201, accuracy=84.79%, lr=0.001754\n",
      "Epoch 29, Batch 0: Loss=0.5173, LR=0.001754\n",
      "Epoch 29, Batch 100: Loss=0.4826, LR=0.001754\n",
      "Epoch 29, Batch 200: Loss=0.4441, LR=0.001754\n",
      "Epoch 29, Batch 300: Loss=0.4399, LR=0.001754\n",
      "Epoch 29: train_loss=0.5126, val_loss=0.4208, accuracy=85.20%, lr=0.001754\n",
      "Epoch 30, Batch 0: Loss=0.6249, LR=0.001754\n",
      "Epoch 30, Batch 100: Loss=0.4719, LR=0.001395\n",
      "Epoch 30, Batch 200: Loss=0.4801, LR=0.001525\n",
      "Epoch 30, Batch 300: Loss=0.3887, LR=0.001846\n",
      "Epoch 30: train_loss=0.5066, val_loss=0.4294, accuracy=84.65%, lr=0.001942\n",
      "Epoch 31, Batch 0: Loss=0.6825, LR=0.001942\n",
      "Epoch 31, Batch 100: Loss=0.3563, LR=0.001942\n",
      "Epoch 31, Batch 200: Loss=0.5210, LR=0.001942\n",
      "Epoch 31, Batch 300: Loss=0.5331, LR=0.001942\n",
      "Epoch 31: train_loss=0.5099, val_loss=0.4215, accuracy=84.86%, lr=0.001942\n",
      "Epoch 32, Batch 0: Loss=0.5984, LR=0.001942\n",
      "Epoch 32, Batch 100: Loss=0.5088, LR=0.001942\n",
      "Epoch 32, Batch 200: Loss=0.5366, LR=0.001942\n",
      "Epoch 32, Batch 300: Loss=0.5897, LR=0.001942\n",
      "Epoch 32: train_loss=0.5072, val_loss=0.4179, accuracy=84.81%, lr=0.001942\n",
      "Epoch 33, Batch 0: Loss=0.4449, LR=0.001942\n",
      "Epoch 33, Batch 100: Loss=0.4009, LR=0.001942\n",
      "Epoch 33, Batch 200: Loss=0.5038, LR=0.001942\n",
      "Epoch 33, Batch 300: Loss=0.4362, LR=0.001942\n",
      "Epoch 33: train_loss=0.5059, val_loss=0.4201, accuracy=84.77%, lr=0.001942\n",
      "Epoch 34, Batch 0: Loss=0.4775, LR=0.001942\n",
      "Epoch 34, Batch 100: Loss=0.5302, LR=0.001942\n",
      "Epoch 34, Batch 200: Loss=0.4432, LR=0.001942\n",
      "Epoch 34, Batch 300: Loss=0.5977, LR=0.001942\n",
      "Epoch 34: train_loss=0.5007, val_loss=0.4168, accuracy=85.06%, lr=0.001942\n",
      "Epoch 35, Batch 0: Loss=0.4510, LR=0.001942\n",
      "Epoch 35, Batch 100: Loss=0.5759, LR=0.002084\n",
      "Epoch 35, Batch 200: Loss=0.5551, LR=0.002474\n",
      "Epoch 35, Batch 300: Loss=0.4329, LR=0.002819\n",
      "Epoch 35: train_loss=0.5092, val_loss=0.4229, accuracy=84.68%, lr=0.002476\n",
      "Epoch 36, Batch 0: Loss=0.6653, LR=0.002476\n",
      "Epoch 36, Batch 100: Loss=0.4602, LR=0.002476\n",
      "Epoch 36, Batch 200: Loss=0.4921, LR=0.002476\n",
      "Epoch 36, Batch 300: Loss=0.6338, LR=0.002476\n",
      "Epoch 36: train_loss=0.5068, val_loss=0.4181, accuracy=85.33%, lr=0.002476\n",
      "Epoch 37, Batch 0: Loss=0.6071, LR=0.002476\n",
      "Epoch 37, Batch 100: Loss=0.5437, LR=0.002476\n",
      "Epoch 37, Batch 200: Loss=0.4676, LR=0.002476\n",
      "Epoch 37, Batch 300: Loss=0.5591, LR=0.002476\n",
      "Epoch 37: train_loss=0.5067, val_loss=0.4257, accuracy=84.37%, lr=0.002476\n",
      "Epoch 38, Batch 0: Loss=0.3936, LR=0.002476\n",
      "Epoch 38, Batch 100: Loss=0.5928, LR=0.002476\n",
      "Epoch 38, Batch 200: Loss=0.7334, LR=0.002476\n",
      "Epoch 38, Batch 300: Loss=0.5564, LR=0.002476\n",
      "Epoch 38: train_loss=0.5041, val_loss=0.4099, accuracy=85.51%, lr=0.002476\n",
      "Epoch 39, Batch 0: Loss=0.4552, LR=0.002476\n",
      "Epoch 39, Batch 100: Loss=0.5107, LR=0.002476\n",
      "Epoch 39, Batch 200: Loss=0.4429, LR=0.002476\n",
      "Epoch 39, Batch 300: Loss=0.4152, LR=0.002476\n",
      "Epoch 39: train_loss=0.5028, val_loss=0.4074, accuracy=85.43%, lr=0.002476\n",
      "Epoch 40, Batch 0: Loss=0.3839, LR=0.002476\n",
      "Epoch 40, Batch 100: Loss=0.4928, LR=0.002823\n",
      "Epoch 40, Batch 200: Loss=0.5801, LR=0.002962\n",
      "Epoch 40, Batch 300: Loss=0.5715, LR=0.003236\n",
      "Epoch 40: train_loss=0.5075, val_loss=0.4223, accuracy=84.35%, lr=0.003334\n",
      "Epoch 41, Batch 0: Loss=0.3905, LR=0.003334\n",
      "Epoch 41, Batch 100: Loss=0.4452, LR=0.003334\n",
      "Epoch 41, Batch 200: Loss=0.5474, LR=0.003334\n",
      "Epoch 41, Batch 300: Loss=0.3966, LR=0.003334\n",
      "Epoch 41: train_loss=0.5099, val_loss=0.4082, accuracy=85.05%, lr=0.003334\n",
      "Epoch 42, Batch 0: Loss=0.5511, LR=0.003334\n",
      "Epoch 42, Batch 100: Loss=0.5490, LR=0.003334\n",
      "Epoch 42, Batch 200: Loss=0.4258, LR=0.003334\n",
      "Epoch 42, Batch 300: Loss=0.4806, LR=0.003334\n",
      "Epoch 42: train_loss=0.5082, val_loss=0.4192, accuracy=84.94%, lr=0.003334\n",
      "Epoch 43, Batch 0: Loss=0.5364, LR=0.003334\n",
      "Epoch 43, Batch 100: Loss=0.4350, LR=0.003334\n",
      "Epoch 43, Batch 200: Loss=0.5436, LR=0.003334\n",
      "Epoch 43, Batch 300: Loss=0.4659, LR=0.003334\n",
      "Epoch 43: train_loss=0.5025, val_loss=0.4140, accuracy=85.27%, lr=0.003334\n",
      "Epoch 44, Batch 0: Loss=0.3993, LR=0.003334\n",
      "Epoch 44, Batch 100: Loss=0.5544, LR=0.003334\n",
      "Epoch 44, Batch 200: Loss=0.6126, LR=0.003334\n",
      "Epoch 44, Batch 300: Loss=0.4685, LR=0.003334\n",
      "Epoch 44: train_loss=0.5008, val_loss=0.4133, accuracy=84.91%, lr=0.003334\n",
      "Epoch 45, Batch 0: Loss=0.5252, LR=0.003267\n",
      "Epoch 45, Batch 100: Loss=0.3993, LR=0.002496\n",
      "Epoch 45, Batch 200: Loss=0.5473, LR=0.002418\n",
      "Epoch 45, Batch 300: Loss=0.4181, LR=0.002870\n",
      "Epoch 45: train_loss=0.4941, val_loss=0.4004, accuracy=85.44%, lr=0.002627\n",
      "Epoch 46, Batch 0: Loss=0.6866, LR=0.002627\n",
      "Epoch 46, Batch 100: Loss=0.4494, LR=0.002627\n",
      "Epoch 46, Batch 200: Loss=0.7501, LR=0.002627\n",
      "Epoch 46, Batch 300: Loss=0.7476, LR=0.002627\n",
      "Epoch 46: train_loss=0.4886, val_loss=0.4018, accuracy=85.94%, lr=0.002627\n",
      "Epoch 47, Batch 0: Loss=0.5023, LR=0.002627\n",
      "Epoch 47, Batch 100: Loss=0.6160, LR=0.002627\n",
      "Epoch 47, Batch 200: Loss=0.3572, LR=0.002627\n",
      "Epoch 47, Batch 300: Loss=0.6701, LR=0.002627\n",
      "Epoch 47: train_loss=0.4911, val_loss=0.4012, accuracy=85.51%, lr=0.002627\n",
      "Epoch 48, Batch 0: Loss=0.4507, LR=0.002627\n",
      "Epoch 48, Batch 100: Loss=0.4209, LR=0.002627\n",
      "Epoch 48, Batch 200: Loss=0.4815, LR=0.002627\n",
      "Epoch 48, Batch 300: Loss=0.5805, LR=0.002627\n",
      "Epoch 48: train_loss=0.4863, val_loss=0.4118, accuracy=85.14%, lr=0.002627\n",
      "Epoch 49, Batch 0: Loss=0.4721, LR=0.002627\n",
      "Epoch 49, Batch 100: Loss=0.4151, LR=0.002627\n",
      "Epoch 49, Batch 200: Loss=0.4640, LR=0.002627\n",
      "Epoch 49, Batch 300: Loss=0.4027, LR=0.002627\n",
      "Epoch 49: train_loss=0.4884, val_loss=0.4027, accuracy=85.96%, lr=0.002627\n",
      "Epoch 50, Batch 0: Loss=0.4375, LR=0.002627\n",
      "Epoch 50, Batch 100: Loss=0.4975, LR=0.002172\n",
      "Epoch 50, Batch 200: Loss=0.5338, LR=0.001660\n",
      "Epoch 50, Batch 300: Loss=0.4830, LR=0.001486\n",
      "Epoch 50: train_loss=0.4774, val_loss=0.3945, accuracy=85.93%, lr=0.001596\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete! Results saved to 'nal_training_results.png'\n",
      "Model saved to 'nal_trained_model.pth'\n",
      "Best validation accuracy: 85.96%\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader, Subset\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "# Import your enhanced NAL implementation\n",
    "# Assuming the code is in a file called \"enhanced_nal.py\"\n",
    "\n",
    "# Set up device for training\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \n",
    "                      \"mps\" if torch.backends.mps.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Define a simple neural network model\n",
    "class SimpleNN(nn.Module):\n",
    "    def __init__(self, hidden_size=128, dropout=0.2):\n",
    "        super().__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(28*28, hidden_size),\n",
    "            nn.BatchNorm1d(hidden_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(hidden_size, 10)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        return self.model(x)\n",
    "\n",
    "# Set up data paths and hyperparameters\n",
    "DATA_ROOT = os.path.join(os.path.expanduser(\"~\"), \"data\")\n",
    "os.makedirs(DATA_ROOT, exist_ok=True)\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "EPOCHS = 50\n",
    "INIT_LR = 0.00001\n",
    "\n",
    "# Define transforms for the dataset\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.RandomRotation(10),\n",
    "    transforms.RandomAffine(degrees=0, translate=(0.1, 0.1)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "# Load Fashion MNIST dataset\n",
    "train_dataset = torchvision.datasets.FashionMNIST(\n",
    "    DATA_ROOT, train=True, download=True, transform=train_transform\n",
    ")\n",
    "test_dataset = torchvision.datasets.FashionMNIST(\n",
    "    DATA_ROOT, train=False, download=True, transform=test_transform\n",
    ")\n",
    "\n",
    "# Use smaller subset for faster training (optional)\n",
    "train_subset = Subset(train_dataset, list(range(50000)))\n",
    "test_subset = Subset(test_dataset, list(range(10000)))\n",
    "\n",
    "# Create data loaders\n",
    "train_loader = DataLoader(\n",
    "    train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True\n",
    ")\n",
    "test_loader = DataLoader(\n",
    "    test_subset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True\n",
    ")\n",
    "\n",
    "# Initialize model, loss function, and optimizer\n",
    "model = SimpleNN(hidden_size=128, dropout=0.2).to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=INIT_LR)\n",
    "\n",
    "# Train with the enhanced NAL learning rate scheduler\n",
    "print(f\"Starting training with Enhanced NAL LR Tuner for {EPOCHS} epochs...\")\n",
    "print(f\"Initial learning rate: {INIT_LR}\")\n",
    "\n",
    "training_history = train_with_enhanced_nal(\n",
    "    model=model,\n",
    "    train_loader=train_loader,\n",
    "    optimizer=optimizer,\n",
    "    criterion=criterion,\n",
    "    device=device,\n",
    "    epochs=EPOCHS,\n",
    "    validation_loader=test_loader,\n",
    "    debug=True  # Set to False for less verbose output\n",
    ")\n",
    "\n",
    "# Plot training results\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# Plot training and validation loss\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.plot(range(1, EPOCHS+1), training_history['train_losses'], 'b-', label='Train Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history['val_losses'], 'r-', label='Val Loss')\n",
    "plt.title('Loss vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot validation accuracy\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.plot(range(1, EPOCHS+1), training_history['val_accuracies'], 'g-', marker='o')\n",
    "plt.title('Validation Accuracy vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim(0.5, 1.0)\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot learning rate over time\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.plot(range(1, EPOCHS+1), training_history['lr_history'], 'k-', marker='.')\n",
    "plt.title('Learning Rate vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Learning Rate')\n",
    "plt.yscale('log')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot applied procedures (NAL-8)\n",
    "plt.subplot(2, 2, 4)\n",
    "if training_history['applied_procedures']:\n",
    "    applied_steps = [p['step'] for p in training_history['applied_procedures']]\n",
    "    similarities = [p['similarity'] for p in training_history['applied_procedures']]\n",
    "    plt.scatter(applied_steps, similarities, c='purple', alpha=0.7)\n",
    "    plt.title('NAL-8 Procedural Applications')\n",
    "    plt.xlabel('Training Step')\n",
    "    plt.ylabel('Context Similarity')\n",
    "    plt.ylim(0, 1.1)\n",
    "else:\n",
    "    plt.text(0.5, 0.5, 'No procedural applications recorded', \n",
    "             horizontalalignment='center', verticalalignment='center')\n",
    "    plt.title('NAL-8 Procedural Applications')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Training complete! Results saved to 'nal_training_results.png'\")\n",
    "\n",
    "# Save model\n",
    "print(\"Model saved to 'nal_trained_model.pth'\")\n",
    "\n",
    "# Print best accuracy achieved\n",
    "best_accuracy = max(training_history['val_accuracies'])\n",
    "print(f\"Best validation accuracy: {best_accuracy:.2%}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bbe043ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Learned NAL-8 Procedural Patterns:\n",
      "Pattern 1: early_medium\n",
      "  Count: 3\n",
      "  Max Accuracy: 79.50%\n",
      "  Avg Accuracy: 77.23%\n",
      "  LR Range: 0.001007 - 0.001007\n",
      "\n",
      "Pattern 2: mid_medium\n",
      "  Count: 7\n",
      "  Max Accuracy: 83.37%\n",
      "  Avg Accuracy: 82.08%\n",
      "  LR Range: 0.000736 - 0.001007\n",
      "\n",
      "Pattern 3: late_medium\n",
      "  Count: 40\n",
      "  Max Accuracy: 85.96%\n",
      "  Avg Accuracy: 84.58%\n",
      "  LR Range: 0.000833 - 0.003334\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print NAL-8 procedural patterns that were learned\n",
    "if training_history['procedural_patterns']:\n",
    "    print(\"\\nLearned NAL-8 Procedural Patterns:\")\n",
    "    for i, (context_key, pattern) in enumerate(training_history['procedural_patterns'].items()):\n",
    "        avg_accuracy = pattern['total_accuracy'] / pattern['count'] if pattern['count'] > 0 else 0\n",
    "        print(f\"Pattern {i+1}: {context_key}\")\n",
    "        print(f\"  Count: {pattern['count']}\")\n",
    "        print(f\"  Max Accuracy: {pattern['max_accuracy']:.2%}\")\n",
    "        print(f\"  Avg Accuracy: {avg_accuracy:.2%}\")\n",
    "        if pattern['lr_values']:\n",
    "            print(f\"  LR Range: {min(pattern['lr_values']):.6f} - {max(pattern['lr_values']):.6f}\")\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "edf99ca3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Batch numbers\n",
    "batches = [0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300, 0, 100, 200, 300]\n",
    "\n",
    "# Epoch for each batch measurement\n",
    "batch_epochs = [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 15, 16, 16, 16, 16, 17, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 19, 20, 20, 20, 20, 21, 21, 21, 21, 22, 22, 22, 22, 23, 23, 23, 23, 24, 24, 24, 24, 25, 25, 25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 30, 30, 30, 30]\n",
    "\n",
    "# Learning rate per batch\n",
    "batch_learning_rates = [0.000001, 0.000100, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.010075, 0.009873, 0.011017, 0.012289, 0.013992, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014141, 0.014857, 0.013573, 0.013707, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.012291, 0.014275, 0.014704, 0.015473, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013597, 0.013869, 0.014583, 0.017317, 0.016789, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014470, 0.014911, 0.017324, 0.014896, 0.016015, 0.016015, 0.016015, 0.016015, 0.016015, 0.016015, 0.016015, 0.016015, 0.016015, 0.016015, 0.016015, 0.016015, 0.015694, 0.018986, 0.014792, 0.012989]\n",
    "\n",
    "# Loss per batch\n",
    "batch_losses = [2.3690, 1.5166, 0.9313, 0.7901, 0.6604, 0.8045, 0.6816, 0.7123, 0.5676, 0.7553, 0.6175, 0.6746, 0.5538, 0.5618, 0.4750, 0.6026, 0.6632, 0.6810, 0.6444, 0.5755, 0.6114, 0.6459, 0.5243, 0.5822, 0.5480, 0.6094, 0.4926, 0.4567, 0.5301, 0.5357, 0.7297, 0.5176, 0.5981, 0.4846, 0.5345, 0.4565, 0.6862, 0.5187, 0.4485, 0.6689, 0.6403, 0.5460, 0.4377, 0.5461, 0.5285, 0.5344, 0.4624, 0.4280, 0.5197, 0.6525, 0.6213, 0.4418, 0.4743, 0.5967, 0.5973, 0.5319, 0.6034, 0.4787, 0.5732, 0.7104, 0.5444, 0.5338, 0.8754, 0.6383, 0.5452, 0.6389, 0.5073, 0.4765, 0.5611, 0.5125, 0.6154, 0.4014, 0.5657, 0.4932, 0.3924, 0.5128, 0.5334, 0.7881, 0.7436, 0.4968, 0.5447, 0.5906, 0.5537, 0.4969, 0.5221, 0.5778, 0.4500, 0.4446, 0.4180, 0.6083, 0.6122, 0.5594, 0.4917, 0.7239, 0.5420, 0.5533, 0.3797, 0.4504, 0.5792, 0.6526, 0.5047, 0.5838, 0.6863, 0.6048, 0.4012, 0.5964, 0.4445, 0.5280, 0.4402, 0.6575, 0.4704, 0.4665, 0.5299, 0.6044, 0.4401, 0.6498, 0.4894, 0.4083, 0.6082, 0.4395]\n",
    "\n",
    "# Global step (for continuous plotting)\n",
    "global_steps = [0, 100, 200, 300, 391, 491, 591, 691, 782, 882, 982, 1082, 1173, 1273, 1373, 1473, 1564, 1664, 1764, 1864, 1955, 2055, 2155, 2255, 2346, 2446, 2546, 2646, 2737, 2837, 2937, 3037, 3128, 3228, 3328, 3428, 3519, 3619, 3719, 3819, 3910, 4010, 4110, 4210, 4301, 4401, 4501, 4601, 4692, 4792, 4892, 4992, 5083, 5183, 5283, 5383, 5474, 5574, 5674, 5774, 5865, 5965, 6065, 6165, 6256, 6356, 6456, 6556, 6647, 6747, 6847, 6947, 7038, 7138, 7238, 7338, 7429, 7529, 7629, 7729, 7820, 7920, 8020, 8120, 8211, 8311, 8411, 8511, 8602, 8702, 8802, 8902, 8993, 9093, 9193, 9293, 9384, 9484, 9584, 9684, 9775, 9875, 9975, 10075, 10166, 10266, 10366, 10466, 10557, 10657, 10757, 10857, 10948, 11048, 11148, 11248]\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(range(len(batch_learning_rates)),batch_learning_rates)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "745b7238",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: mps\n",
      "Starting training with OneCycleLR for 50 epochs...\n",
      "Initial learning rate: 0.009999999999999995\n",
      "Maximum learning rate: 0.1\n",
      "Epoch 1, Batch 50: Loss = 1.0745, LR = 0.010016\n",
      "Epoch 1, Batch 100: Loss = 0.8347, LR = 0.010065\n",
      "Epoch 1, Batch 150: Loss = 0.7999, LR = 0.010145\n",
      "Epoch 1, Batch 200: Loss = 0.7586, LR = 0.010258\n",
      "Epoch 1, Batch 250: Loss = 0.7411, LR = 0.010403\n",
      "Epoch 1, Batch 300: Loss = 0.7385, LR = 0.010580\n",
      "Epoch 1, Batch 350: Loss = 0.7022, LR = 0.010789\n",
      "Epoch 1/50 completed.\n",
      "  Train Loss: 0.0710\n",
      "  Val Loss: 0.5843\n",
      "  Val Accuracy: 0.7734\n",
      "  Current LR: 0.010984\n",
      "Epoch 2, Batch 50: Loss = 0.6736, LR = 0.011250\n",
      "Epoch 2, Batch 100: Loss = 0.6766, LR = 0.011548\n",
      "Epoch 2, Batch 150: Loss = 0.6678, LR = 0.011877\n",
      "Epoch 2, Batch 200: Loss = 0.6725, LR = 0.012237\n",
      "Epoch 2, Batch 250: Loss = 0.6464, LR = 0.012627\n",
      "Epoch 2, Batch 300: Loss = 0.6497, LR = 0.013048\n",
      "Epoch 2, Batch 350: Loss = 0.6607, LR = 0.013500\n",
      "Epoch 2/50 completed.\n",
      "  Train Loss: 0.0691\n",
      "  Val Loss: 0.5321\n",
      "  Val Accuracy: 0.8018\n",
      "  Current LR: 0.013892\n",
      "Epoch 3, Batch 50: Loss = 0.6572, LR = 0.014397\n",
      "Epoch 3, Batch 100: Loss = 0.6707, LR = 0.014931\n",
      "Epoch 3, Batch 150: Loss = 0.6290, LR = 0.015494\n",
      "Epoch 3, Batch 200: Loss = 0.6096, LR = 0.016085\n",
      "Epoch 3, Batch 250: Loss = 0.6337, LR = 0.016704\n",
      "Epoch 3, Batch 300: Loss = 0.6383, LR = 0.017351\n",
      "Epoch 3, Batch 350: Loss = 0.6040, LR = 0.018025\n",
      "Epoch 3/50 completed.\n",
      "  Train Loss: 0.0655\n",
      "  Val Loss: 0.5382\n",
      "  Val Accuracy: 0.8001\n",
      "  Current LR: 0.018597\n",
      "Epoch 4, Batch 50: Loss = 0.6548, LR = 0.019319\n",
      "Epoch 4, Batch 100: Loss = 0.6271, LR = 0.020066\n",
      "Epoch 4, Batch 150: Loss = 0.6153, LR = 0.020838\n",
      "Epoch 4, Batch 200: Loss = 0.6071, LR = 0.021635\n",
      "Epoch 4, Batch 250: Loss = 0.6238, LR = 0.022456\n",
      "Epoch 4, Batch 300: Loss = 0.6285, LR = 0.023300\n",
      "Epoch 4, Batch 350: Loss = 0.6348, LR = 0.024167\n",
      "Epoch 4/50 completed.\n",
      "  Train Loss: 0.0689\n",
      "  Val Loss: 0.5381\n",
      "  Val Accuracy: 0.7946\n",
      "  Current LR: 0.024894\n",
      "Epoch 5, Batch 50: Loss = 0.5977, LR = 0.025801\n",
      "Epoch 5, Batch 100: Loss = 0.6171, LR = 0.026728\n",
      "Epoch 5, Batch 150: Loss = 0.6158, LR = 0.027676\n",
      "Epoch 5, Batch 200: Loss = 0.6311, LR = 0.028643\n",
      "Epoch 5, Batch 250: Loss = 0.6460, LR = 0.029630\n",
      "Epoch 5, Batch 300: Loss = 0.6409, LR = 0.030634\n",
      "Epoch 5, Batch 350: Loss = 0.6575, LR = 0.031656\n",
      "Epoch 5/50 completed.\n",
      "  Train Loss: 0.0691\n",
      "  Val Loss: 0.5065\n",
      "  Val Accuracy: 0.8077\n",
      "  Current LR: 0.032507\n",
      "Epoch 6, Batch 50: Loss = 0.6439, LR = 0.033559\n",
      "Epoch 6, Batch 100: Loss = 0.6416, LR = 0.034626\n",
      "Epoch 6, Batch 150: Loss = 0.6225, LR = 0.035708\n",
      "Epoch 6, Batch 200: Loss = 0.6576, LR = 0.036804\n",
      "Epoch 6, Batch 250: Loss = 0.6344, LR = 0.037913\n",
      "Epoch 6, Batch 300: Loss = 0.6382, LR = 0.039034\n",
      "Epoch 6, Batch 350: Loss = 0.6418, LR = 0.040167\n",
      "Epoch 6/50 completed.\n",
      "  Train Loss: 0.0668\n",
      "  Val Loss: 0.5447\n",
      "  Val Accuracy: 0.8043\n",
      "  Current LR: 0.041103\n",
      "Epoch 7, Batch 50: Loss = 0.6411, LR = 0.042255\n",
      "Epoch 7, Batch 100: Loss = 0.6203, LR = 0.043415\n",
      "Epoch 7, Batch 150: Loss = 0.6364, LR = 0.044584\n",
      "Epoch 7, Batch 200: Loss = 0.6725, LR = 0.045760\n",
      "Epoch 7, Batch 250: Loss = 0.6824, LR = 0.046943\n",
      "Epoch 7, Batch 300: Loss = 0.6396, LR = 0.048132\n",
      "Epoch 7, Batch 350: Loss = 0.6642, LR = 0.049326\n",
      "Epoch 7/50 completed.\n",
      "  Train Loss: 0.0718\n",
      "  Val Loss: 0.5282\n",
      "  Val Accuracy: 0.7998\n",
      "  Current LR: 0.050307\n",
      "Epoch 8, Batch 50: Loss = 0.6683, LR = 0.051508\n",
      "Epoch 8, Batch 100: Loss = 0.6404, LR = 0.052711\n",
      "Epoch 8, Batch 150: Loss = 0.6672, LR = 0.053915\n",
      "Epoch 8, Batch 200: Loss = 0.6870, LR = 0.055121\n",
      "Epoch 8, Batch 250: Loss = 0.6648, LR = 0.056326\n",
      "Epoch 8, Batch 300: Loss = 0.6797, LR = 0.057530\n",
      "Epoch 8, Batch 350: Loss = 0.6815, LR = 0.058733\n",
      "Epoch 8/50 completed.\n",
      "  Train Loss: 0.0720\n",
      "  Val Loss: 0.5703\n",
      "  Val Accuracy: 0.7900\n",
      "  Current LR: 0.059717\n",
      "Epoch 9, Batch 50: Loss = 0.6718, LR = 0.060914\n",
      "Epoch 9, Batch 100: Loss = 0.6903, LR = 0.062106\n",
      "Epoch 9, Batch 150: Loss = 0.6978, LR = 0.063294\n",
      "Epoch 9, Batch 200: Loss = 0.6835, LR = 0.064475\n",
      "Epoch 9, Batch 250: Loss = 0.6809, LR = 0.065650\n",
      "Epoch 9, Batch 300: Loss = 0.7413, LR = 0.066818\n",
      "Epoch 9, Batch 350: Loss = 0.6957, LR = 0.067976\n",
      "Epoch 9/50 completed.\n",
      "  Train Loss: 0.0714\n",
      "  Val Loss: 0.5576\n",
      "  Val Accuracy: 0.7967\n",
      "  Current LR: 0.068920\n",
      "Epoch 10, Batch 50: Loss = 0.7163, LR = 0.070061\n",
      "Epoch 10, Batch 100: Loss = 0.7549, LR = 0.071191\n",
      "Epoch 10, Batch 150: Loss = 0.7130, LR = 0.072310\n",
      "Epoch 10, Batch 200: Loss = 0.7366, LR = 0.073416\n",
      "Epoch 10, Batch 250: Loss = 0.7240, LR = 0.074509\n",
      "Epoch 10, Batch 300: Loss = 0.7118, LR = 0.075588\n",
      "Epoch 10, Batch 350: Loss = 0.7267, LR = 0.076653\n",
      "Epoch 10/50 completed.\n",
      "  Train Loss: 0.0772\n",
      "  Val Loss: 0.5666\n",
      "  Val Accuracy: 0.8015\n",
      "  Current LR: 0.077514\n",
      "Epoch 11, Batch 50: Loss = 0.7508, LR = 0.078549\n",
      "Epoch 11, Batch 100: Loss = 0.7505, LR = 0.079568\n",
      "Epoch 11, Batch 150: Loss = 0.7116, LR = 0.080569\n",
      "Epoch 11, Batch 200: Loss = 0.7384, LR = 0.081552\n",
      "Epoch 11, Batch 250: Loss = 0.7364, LR = 0.082515\n",
      "Epoch 11, Batch 300: Loss = 0.7376, LR = 0.083459\n",
      "Epoch 11, Batch 350: Loss = 0.7099, LR = 0.084383\n",
      "Epoch 11/50 completed.\n",
      "  Train Loss: 0.0766\n",
      "  Val Loss: 0.5715\n",
      "  Val Accuracy: 0.7893\n",
      "  Current LR: 0.085124\n",
      "Epoch 12, Batch 50: Loss = 0.7321, LR = 0.086009\n",
      "Epoch 12, Batch 100: Loss = 0.7123, LR = 0.086871\n",
      "Epoch 12, Batch 150: Loss = 0.7708, LR = 0.087710\n",
      "Epoch 12, Batch 200: Loss = 0.7673, LR = 0.088526\n",
      "Epoch 12, Batch 250: Loss = 0.7245, LR = 0.089318\n",
      "Epoch 12, Batch 300: Loss = 0.7454, LR = 0.090086\n",
      "Epoch 12, Batch 350: Loss = 0.7436, LR = 0.090828\n",
      "Epoch 12/50 completed.\n",
      "  Train Loss: 0.0797\n",
      "  Val Loss: 0.5787\n",
      "  Val Accuracy: 0.7874\n",
      "  Current LR: 0.091417\n",
      "Epoch 13, Batch 50: Loss = 0.7141, LR = 0.092112\n",
      "Epoch 13, Batch 100: Loss = 0.7312, LR = 0.092780\n",
      "Epoch 13, Batch 150: Loss = 0.7694, LR = 0.093422\n",
      "Epoch 13, Batch 200: Loss = 0.7619, LR = 0.094035\n",
      "Epoch 13, Batch 250: Loss = 0.7576, LR = 0.094621\n",
      "Epoch 13, Batch 300: Loss = 0.7720, LR = 0.095178\n",
      "Epoch 13, Batch 350: Loss = 0.7623, LR = 0.095707\n",
      "Epoch 13/50 completed.\n",
      "  Train Loss: 0.0816\n",
      "  Val Loss: 0.5977\n",
      "  Val Accuracy: 0.7783\n",
      "  Current LR: 0.096118\n",
      "Epoch 14, Batch 50: Loss = 0.7878, LR = 0.096593\n",
      "Epoch 14, Batch 100: Loss = 0.7440, LR = 0.097038\n",
      "Epoch 14, Batch 150: Loss = 0.7251, LR = 0.097453\n",
      "Epoch 14, Batch 200: Loss = 0.7531, LR = 0.097838\n",
      "Epoch 14, Batch 250: Loss = 0.7556, LR = 0.098191\n",
      "Epoch 14, Batch 300: Loss = 0.7463, LR = 0.098514\n",
      "Epoch 14, Batch 350: Loss = 0.7373, LR = 0.098806\n",
      "Epoch 14/50 completed.\n",
      "  Train Loss: 0.0819\n",
      "  Val Loss: 0.5618\n",
      "  Val Accuracy: 0.7934\n",
      "  Current LR: 0.099021\n",
      "Epoch 15, Batch 50: Loss = 0.7582, LR = 0.099256\n",
      "Epoch 15, Batch 100: Loss = 0.7556, LR = 0.099458\n",
      "Epoch 15, Batch 150: Loss = 0.7355, LR = 0.099629\n",
      "Epoch 15, Batch 200: Loss = 0.7655, LR = 0.099767\n",
      "Epoch 15, Batch 250: Loss = 0.7314, LR = 0.099873\n",
      "Epoch 15, Batch 300: Loss = 0.7705, LR = 0.099948\n",
      "Epoch 15, Batch 350: Loss = 0.7644, LR = 0.099990\n",
      "Epoch 15/50 completed.\n",
      "  Train Loss: 0.0803\n",
      "  Val Loss: 0.5989\n",
      "  Val Accuracy: 0.7794\n",
      "  Current LR: 0.100000\n",
      "Epoch 16, Batch 50: Loss = 0.7417, LR = 0.099997\n",
      "Epoch 16, Batch 100: Loss = 0.7093, LR = 0.099987\n",
      "Epoch 16, Batch 150: Loss = 0.7580, LR = 0.099970\n",
      "Epoch 16, Batch 200: Loss = 0.7349, LR = 0.099947\n",
      "Epoch 16, Batch 250: Loss = 0.7625, LR = 0.099917\n",
      "Epoch 16, Batch 300: Loss = 0.7663, LR = 0.099881\n",
      "Epoch 16, Batch 350: Loss = 0.7855, LR = 0.099838\n",
      "Epoch 16/50 completed.\n",
      "  Train Loss: 0.0788\n",
      "  Val Loss: 0.5802\n",
      "  Val Accuracy: 0.7774\n",
      "  Current LR: 0.099798\n",
      "Epoch 17, Batch 50: Loss = 0.7520, LR = 0.099743\n",
      "Epoch 17, Batch 100: Loss = 0.7844, LR = 0.099681\n",
      "Epoch 17, Batch 150: Loss = 0.7736, LR = 0.099614\n",
      "Epoch 17, Batch 200: Loss = 0.7646, LR = 0.099539\n",
      "Epoch 17, Batch 250: Loss = 0.7187, LR = 0.099458\n",
      "Epoch 17, Batch 300: Loss = 0.7430, LR = 0.099370\n",
      "Epoch 17, Batch 350: Loss = 0.7479, LR = 0.099276\n",
      "Epoch 17/50 completed.\n",
      "  Train Loss: 0.0805\n",
      "  Val Loss: 0.5983\n",
      "  Val Accuracy: 0.7821\n",
      "  Current LR: 0.099195\n",
      "Epoch 18, Batch 50: Loss = 0.7838, LR = 0.099089\n",
      "Epoch 18, Batch 100: Loss = 0.7675, LR = 0.098976\n",
      "Epoch 18, Batch 150: Loss = 0.7383, LR = 0.098858\n",
      "Epoch 18, Batch 200: Loss = 0.7311, LR = 0.098732\n",
      "Epoch 18, Batch 250: Loss = 0.7494, LR = 0.098601\n",
      "Epoch 18, Batch 300: Loss = 0.7383, LR = 0.098463\n",
      "Epoch 18, Batch 350: Loss = 0.7475, LR = 0.098318\n",
      "Epoch 18/50 completed.\n",
      "  Train Loss: 0.0764\n",
      "  Val Loss: 0.5637\n",
      "  Val Accuracy: 0.7840\n",
      "  Current LR: 0.098195\n",
      "Epoch 19, Batch 50: Loss = 0.7392, LR = 0.098039\n",
      "Epoch 19, Batch 100: Loss = 0.7537, LR = 0.097877\n",
      "Epoch 19, Batch 150: Loss = 0.7367, LR = 0.097708\n",
      "Epoch 19, Batch 200: Loss = 0.7460, LR = 0.097534\n",
      "Epoch 19, Batch 250: Loss = 0.7643, LR = 0.097352\n",
      "Epoch 19, Batch 300: Loss = 0.7593, LR = 0.097165\n",
      "Epoch 19, Batch 350: Loss = 0.7545, LR = 0.096971\n",
      "Epoch 19/50 completed.\n",
      "  Train Loss: 0.0798\n",
      "  Val Loss: 0.5790\n",
      "  Val Accuracy: 0.7882\n",
      "  Current LR: 0.096808\n",
      "Epoch 20, Batch 50: Loss = 0.7697, LR = 0.096603\n",
      "Epoch 20, Batch 100: Loss = 0.7328, LR = 0.096392\n",
      "Epoch 20, Batch 150: Loss = 0.7293, LR = 0.096175\n",
      "Epoch 20, Batch 200: Loss = 0.7624, LR = 0.095952\n",
      "Epoch 20, Batch 250: Loss = 0.7721, LR = 0.095723\n",
      "Epoch 20, Batch 300: Loss = 0.7191, LR = 0.095487\n",
      "Epoch 20, Batch 350: Loss = 0.7489, LR = 0.095246\n",
      "Epoch 20/50 completed.\n",
      "  Train Loss: 0.0770\n",
      "  Val Loss: 0.5872\n",
      "  Val Accuracy: 0.7793\n",
      "  Current LR: 0.095044\n",
      "Epoch 21, Batch 50: Loss = 0.7725, LR = 0.094792\n",
      "Epoch 21, Batch 100: Loss = 0.7679, LR = 0.094534\n",
      "Epoch 21, Batch 150: Loss = 0.7728, LR = 0.094270\n",
      "Epoch 21, Batch 200: Loss = 0.7371, LR = 0.094000\n",
      "Epoch 21, Batch 250: Loss = 0.7397, LR = 0.093725\n",
      "Epoch 21, Batch 300: Loss = 0.7817, LR = 0.093444\n",
      "Epoch 21, Batch 350: Loss = 0.7522, LR = 0.093157\n",
      "Epoch 21/50 completed.\n",
      "  Train Loss: 0.0772\n",
      "  Val Loss: 0.5837\n",
      "  Val Accuracy: 0.7683\n",
      "  Current LR: 0.092917\n",
      "Epoch 22, Batch 50: Loss = 0.7466, LR = 0.092620\n",
      "Epoch 22, Batch 100: Loss = 0.7470, LR = 0.092317\n",
      "Epoch 22, Batch 150: Loss = 0.7339, LR = 0.092009\n",
      "Epoch 22, Batch 200: Loss = 0.7415, LR = 0.091695\n",
      "Epoch 22, Batch 250: Loss = 0.7432, LR = 0.091375\n",
      "Epoch 22, Batch 300: Loss = 0.7259, LR = 0.091050\n",
      "Epoch 22, Batch 350: Loss = 0.7638, LR = 0.090720\n",
      "Epoch 22/50 completed.\n",
      "  Train Loss: 0.0768\n",
      "  Val Loss: 0.5839\n",
      "  Val Accuracy: 0.7769\n",
      "  Current LR: 0.090445\n",
      "Epoch 23, Batch 50: Loss = 0.7321, LR = 0.090105\n",
      "Epoch 23, Batch 100: Loss = 0.7387, LR = 0.089760\n",
      "Epoch 23, Batch 150: Loss = 0.7448, LR = 0.089409\n",
      "Epoch 23, Batch 200: Loss = 0.7298, LR = 0.089053\n",
      "Epoch 23, Batch 250: Loss = 0.7531, LR = 0.088692\n",
      "Epoch 23, Batch 300: Loss = 0.7712, LR = 0.088326\n",
      "Epoch 23, Batch 350: Loss = 0.7164, LR = 0.087955\n",
      "Epoch 23/50 completed.\n",
      "  Train Loss: 0.0768\n",
      "  Val Loss: 0.5837\n",
      "  Val Accuracy: 0.7772\n",
      "  Current LR: 0.087647\n",
      "Epoch 24, Batch 50: Loss = 0.7494, LR = 0.087267\n",
      "Epoch 24, Batch 100: Loss = 0.7569, LR = 0.086882\n",
      "Epoch 24, Batch 150: Loss = 0.7235, LR = 0.086492\n",
      "Epoch 24, Batch 200: Loss = 0.7146, LR = 0.086097\n",
      "Epoch 24, Batch 250: Loss = 0.7659, LR = 0.085698\n",
      "Epoch 24, Batch 300: Loss = 0.7275, LR = 0.085294\n",
      "Epoch 24, Batch 350: Loss = 0.7363, LR = 0.084885\n",
      "Epoch 24/50 completed.\n",
      "  Train Loss: 0.0750\n",
      "  Val Loss: 0.5439\n",
      "  Val Accuracy: 0.8106\n",
      "  Current LR: 0.084546\n",
      "Epoch 25, Batch 50: Loss = 0.7233, LR = 0.084129\n",
      "Epoch 25, Batch 100: Loss = 0.7281, LR = 0.083708\n",
      "Epoch 25, Batch 150: Loss = 0.7333, LR = 0.083282\n",
      "Epoch 25, Batch 200: Loss = 0.7169, LR = 0.082851\n",
      "Epoch 25, Batch 250: Loss = 0.7337, LR = 0.082416\n",
      "Epoch 25, Batch 300: Loss = 0.7143, LR = 0.081977\n",
      "Epoch 25, Batch 350: Loss = 0.7121, LR = 0.081534\n",
      "Epoch 25/50 completed.\n",
      "  Train Loss: 0.0735\n",
      "  Val Loss: 0.5473\n",
      "  Val Accuracy: 0.7959\n",
      "  Current LR: 0.081167\n",
      "Epoch 26, Batch 50: Loss = 0.7442, LR = 0.080717\n",
      "Epoch 26, Batch 100: Loss = 0.7451, LR = 0.080262\n",
      "Epoch 26, Batch 150: Loss = 0.7021, LR = 0.079803\n",
      "Epoch 26, Batch 200: Loss = 0.6872, LR = 0.079340\n",
      "Epoch 26, Batch 250: Loss = 0.7048, LR = 0.078874\n",
      "Epoch 26, Batch 300: Loss = 0.7317, LR = 0.078403\n",
      "Epoch 26, Batch 350: Loss = 0.7092, LR = 0.077929\n",
      "Epoch 26/50 completed.\n",
      "  Train Loss: 0.0784\n",
      "  Val Loss: 0.5730\n",
      "  Val Accuracy: 0.7903\n",
      "  Current LR: 0.077538\n",
      "Epoch 27, Batch 50: Loss = 0.7334, LR = 0.077057\n",
      "Epoch 27, Batch 100: Loss = 0.6990, LR = 0.076572\n",
      "Epoch 27, Batch 150: Loss = 0.7002, LR = 0.076084\n",
      "Epoch 27, Batch 200: Loss = 0.7040, LR = 0.075593\n",
      "Epoch 27, Batch 250: Loss = 0.7262, LR = 0.075099\n",
      "Epoch 27, Batch 300: Loss = 0.6978, LR = 0.074601\n",
      "Epoch 27, Batch 350: Loss = 0.7038, LR = 0.074099\n",
      "Epoch 27/50 completed.\n",
      "  Train Loss: 0.0740\n",
      "  Val Loss: 0.5394\n",
      "  Val Accuracy: 0.8083\n",
      "  Current LR: 0.073686\n",
      "Epoch 28, Batch 50: Loss = 0.6934, LR = 0.073179\n",
      "Epoch 28, Batch 100: Loss = 0.7007, LR = 0.072669\n",
      "Epoch 28, Batch 150: Loss = 0.7366, LR = 0.072156\n",
      "Epoch 28, Batch 200: Loss = 0.7036, LR = 0.071640\n",
      "Epoch 28, Batch 250: Loss = 0.7107, LR = 0.071121\n",
      "Epoch 28, Batch 300: Loss = 0.6821, LR = 0.070600\n",
      "Epoch 28, Batch 350: Loss = 0.7212, LR = 0.070076\n",
      "Epoch 28/50 completed.\n",
      "  Train Loss: 0.0702\n",
      "  Val Loss: 0.5340\n",
      "  Val Accuracy: 0.7933\n",
      "  Current LR: 0.069644\n",
      "Epoch 29, Batch 50: Loss = 0.6905, LR = 0.069115\n",
      "Epoch 29, Batch 100: Loss = 0.7208, LR = 0.068583\n",
      "Epoch 29, Batch 150: Loss = 0.7081, LR = 0.068049\n",
      "Epoch 29, Batch 200: Loss = 0.7154, LR = 0.067513\n",
      "Epoch 29, Batch 250: Loss = 0.6890, LR = 0.066974\n",
      "Epoch 29, Batch 300: Loss = 0.6837, LR = 0.066433\n",
      "Epoch 29, Batch 350: Loss = 0.7164, LR = 0.065890\n",
      "Epoch 29/50 completed.\n",
      "  Train Loss: 0.0712\n",
      "  Val Loss: 0.5103\n",
      "  Val Accuracy: 0.8116\n",
      "  Current LR: 0.065443\n",
      "Epoch 30, Batch 50: Loss = 0.6912, LR = 0.064897\n",
      "Epoch 30, Batch 100: Loss = 0.7006, LR = 0.064348\n",
      "Epoch 30, Batch 150: Loss = 0.7015, LR = 0.063797\n",
      "Epoch 30, Batch 200: Loss = 0.7006, LR = 0.063245\n",
      "Epoch 30, Batch 250: Loss = 0.6749, LR = 0.062690\n",
      "Epoch 30, Batch 300: Loss = 0.6808, LR = 0.062135\n",
      "Epoch 30, Batch 350: Loss = 0.6808, LR = 0.061577\n",
      "Epoch 30/50 completed.\n",
      "  Train Loss: 0.0753\n",
      "  Val Loss: 0.5212\n",
      "  Val Accuracy: 0.8111\n",
      "  Current LR: 0.061119\n",
      "Epoch 31, Batch 50: Loss = 0.6987, LR = 0.060559\n",
      "Epoch 31, Batch 100: Loss = 0.6943, LR = 0.059997\n",
      "Epoch 31, Batch 150: Loss = 0.6972, LR = 0.059434\n",
      "Epoch 31, Batch 200: Loss = 0.6903, LR = 0.058870\n",
      "Epoch 31, Batch 250: Loss = 0.6644, LR = 0.058305\n",
      "Epoch 31, Batch 300: Loss = 0.6582, LR = 0.057738\n",
      "Epoch 31, Batch 350: Loss = 0.6807, LR = 0.057171\n",
      "Epoch 31/50 completed.\n",
      "  Train Loss: 0.0694\n",
      "  Val Loss: 0.5206\n",
      "  Val Accuracy: 0.8093\n",
      "  Current LR: 0.056705\n",
      "Epoch 32, Batch 50: Loss = 0.6547, LR = 0.056136\n",
      "Epoch 32, Batch 100: Loss = 0.6490, LR = 0.055566\n",
      "Epoch 32, Batch 150: Loss = 0.6912, LR = 0.054995\n",
      "Epoch 32, Batch 200: Loss = 0.6717, LR = 0.054424\n",
      "Epoch 32, Batch 250: Loss = 0.7093, LR = 0.053852\n",
      "Epoch 32, Batch 300: Loss = 0.6710, LR = 0.053279\n",
      "Epoch 32, Batch 350: Loss = 0.6881, LR = 0.052707\n",
      "Epoch 32/50 completed.\n",
      "  Train Loss: 0.0723\n",
      "  Val Loss: 0.5224\n",
      "  Val Accuracy: 0.8116\n",
      "  Current LR: 0.052237\n",
      "Epoch 33, Batch 50: Loss = 0.6752, LR = 0.051663\n",
      "Epoch 33, Batch 100: Loss = 0.6630, LR = 0.051089\n",
      "Epoch 33, Batch 150: Loss = 0.6737, LR = 0.050516\n",
      "Epoch 33, Batch 200: Loss = 0.6687, LR = 0.049942\n",
      "Epoch 33, Batch 250: Loss = 0.6868, LR = 0.049368\n",
      "Epoch 33, Batch 300: Loss = 0.6553, LR = 0.048794\n",
      "Epoch 33, Batch 350: Loss = 0.6829, LR = 0.048221\n",
      "Epoch 33/50 completed.\n",
      "  Train Loss: 0.0660\n",
      "  Val Loss: 0.5051\n",
      "  Val Accuracy: 0.8093\n",
      "  Current LR: 0.047751\n",
      "Epoch 34, Batch 50: Loss = 0.6876, LR = 0.047177\n",
      "Epoch 34, Batch 100: Loss = 0.6555, LR = 0.046605\n",
      "Epoch 34, Batch 150: Loss = 0.6610, LR = 0.046032\n",
      "Epoch 34, Batch 200: Loss = 0.6732, LR = 0.045461\n",
      "Epoch 34, Batch 250: Loss = 0.6607, LR = 0.044889\n",
      "Epoch 34, Batch 300: Loss = 0.6580, LR = 0.044319\n",
      "Epoch 34, Batch 350: Loss = 0.6894, LR = 0.043749\n",
      "Epoch 34/50 completed.\n",
      "  Train Loss: 0.0703\n",
      "  Val Loss: 0.5139\n",
      "  Val Accuracy: 0.8095\n",
      "  Current LR: 0.043283\n",
      "Epoch 35, Batch 50: Loss = 0.6546, LR = 0.042714\n",
      "Epoch 35, Batch 100: Loss = 0.6505, LR = 0.042147\n",
      "Epoch 35, Batch 150: Loss = 0.6397, LR = 0.041581\n",
      "Epoch 35, Batch 200: Loss = 0.6736, LR = 0.041016\n",
      "Epoch 35, Batch 250: Loss = 0.6512, LR = 0.040452\n",
      "Epoch 35, Batch 300: Loss = 0.6490, LR = 0.039889\n",
      "Epoch 35, Batch 350: Loss = 0.6868, LR = 0.039328\n",
      "Epoch 35/50 completed.\n",
      "  Train Loss: 0.0710\n",
      "  Val Loss: 0.5094\n",
      "  Val Accuracy: 0.8141\n",
      "  Current LR: 0.038869\n",
      "Epoch 36, Batch 50: Loss = 0.6536, LR = 0.038310\n",
      "Epoch 36, Batch 100: Loss = 0.6535, LR = 0.037753\n",
      "Epoch 36, Batch 150: Loss = 0.6586, LR = 0.037197\n",
      "Epoch 36, Batch 200: Loss = 0.6371, LR = 0.036644\n",
      "Epoch 36, Batch 250: Loss = 0.6490, LR = 0.036092\n",
      "Epoch 36, Batch 300: Loss = 0.6510, LR = 0.035541\n",
      "Epoch 36, Batch 350: Loss = 0.6575, LR = 0.034993\n",
      "Epoch 36/50 completed.\n",
      "  Train Loss: 0.0713\n",
      "  Val Loss: 0.5050\n",
      "  Val Accuracy: 0.8201\n",
      "  Current LR: 0.034545\n",
      "Epoch 37, Batch 50: Loss = 0.6191, LR = 0.034000\n",
      "Epoch 37, Batch 100: Loss = 0.6703, LR = 0.033457\n",
      "Epoch 37, Batch 150: Loss = 0.6434, LR = 0.032917\n",
      "Epoch 37, Batch 200: Loss = 0.6544, LR = 0.032379\n",
      "Epoch 37, Batch 250: Loss = 0.6549, LR = 0.031843\n",
      "Epoch 37, Batch 300: Loss = 0.6430, LR = 0.031310\n",
      "Epoch 37, Batch 350: Loss = 0.6507, LR = 0.030779\n",
      "Epoch 37/50 completed.\n",
      "  Train Loss: 0.0688\n",
      "  Val Loss: 0.4959\n",
      "  Val Accuracy: 0.8182\n",
      "  Current LR: 0.030345\n",
      "Epoch 38, Batch 50: Loss = 0.6379, LR = 0.029819\n",
      "Epoch 38, Batch 100: Loss = 0.6482, LR = 0.029295\n",
      "Epoch 38, Batch 150: Loss = 0.6441, LR = 0.028774\n",
      "Epoch 38, Batch 200: Loss = 0.6328, LR = 0.028256\n",
      "Epoch 38, Batch 250: Loss = 0.6669, LR = 0.027741\n",
      "Epoch 38, Batch 300: Loss = 0.6314, LR = 0.027229\n",
      "Epoch 38, Batch 350: Loss = 0.6406, LR = 0.026719\n",
      "Epoch 38/50 completed.\n",
      "  Train Loss: 0.0661\n",
      "  Val Loss: 0.4964\n",
      "  Val Accuracy: 0.8180\n",
      "  Current LR: 0.026304\n",
      "Epoch 39, Batch 50: Loss = 0.6475, LR = 0.025800\n",
      "Epoch 39, Batch 100: Loss = 0.6390, LR = 0.025300\n",
      "Epoch 39, Batch 150: Loss = 0.6361, LR = 0.024802\n",
      "Epoch 39, Batch 200: Loss = 0.6465, LR = 0.024308\n",
      "Epoch 39, Batch 250: Loss = 0.6359, LR = 0.023818\n",
      "Epoch 39, Batch 300: Loss = 0.6575, LR = 0.023331\n",
      "Epoch 39, Batch 350: Loss = 0.6376, LR = 0.022847\n",
      "Epoch 39/50 completed.\n",
      "  Train Loss: 0.0682\n",
      "  Val Loss: 0.4909\n",
      "  Val Accuracy: 0.8184\n",
      "  Current LR: 0.022453\n",
      "Epoch 40, Batch 50: Loss = 0.6254, LR = 0.021976\n",
      "Epoch 40, Batch 100: Loss = 0.6502, LR = 0.021503\n",
      "Epoch 40, Batch 150: Loss = 0.6617, LR = 0.021033\n",
      "Epoch 40, Batch 200: Loss = 0.6195, LR = 0.020568\n",
      "Epoch 40, Batch 250: Loss = 0.6367, LR = 0.020106\n",
      "Epoch 40, Batch 300: Loss = 0.6401, LR = 0.019648\n",
      "Epoch 40, Batch 350: Loss = 0.6319, LR = 0.019194\n",
      "Epoch 40/50 completed.\n",
      "  Train Loss: 0.0665\n",
      "  Val Loss: 0.4980\n",
      "  Val Accuracy: 0.8112\n",
      "  Current LR: 0.018825\n",
      "Epoch 41, Batch 50: Loss = 0.6226, LR = 0.018378\n",
      "Epoch 41, Batch 100: Loss = 0.6263, LR = 0.017936\n",
      "Epoch 41, Batch 150: Loss = 0.6525, LR = 0.017498\n",
      "Epoch 41, Batch 200: Loss = 0.6344, LR = 0.017064\n",
      "Epoch 41, Batch 250: Loss = 0.6231, LR = 0.016634\n",
      "Epoch 41, Batch 300: Loss = 0.6250, LR = 0.016209\n",
      "Epoch 41, Batch 350: Loss = 0.6338, LR = 0.015789\n",
      "Epoch 41/50 completed.\n",
      "  Train Loss: 0.0651\n",
      "  Val Loss: 0.4901\n",
      "  Val Accuracy: 0.8208\n",
      "  Current LR: 0.015447\n",
      "Epoch 42, Batch 50: Loss = 0.6192, LR = 0.015035\n",
      "Epoch 42, Batch 100: Loss = 0.6358, LR = 0.014627\n",
      "Epoch 42, Batch 150: Loss = 0.5957, LR = 0.014224\n",
      "Epoch 42, Batch 200: Loss = 0.6327, LR = 0.013825\n",
      "Epoch 42, Batch 250: Loss = 0.6285, LR = 0.013432\n",
      "Epoch 42, Batch 300: Loss = 0.6352, LR = 0.013043\n",
      "Epoch 42, Batch 350: Loss = 0.6209, LR = 0.012659\n",
      "Epoch 42/50 completed.\n",
      "  Train Loss: 0.0676\n",
      "  Val Loss: 0.4884\n",
      "  Val Accuracy: 0.8231\n",
      "  Current LR: 0.012348\n",
      "Epoch 43, Batch 50: Loss = 0.6221, LR = 0.011973\n",
      "Epoch 43, Batch 100: Loss = 0.6308, LR = 0.011603\n",
      "Epoch 43, Batch 150: Loss = 0.6220, LR = 0.011238\n",
      "Epoch 43, Batch 200: Loss = 0.6190, LR = 0.010878\n",
      "Epoch 43, Batch 250: Loss = 0.6170, LR = 0.010523\n",
      "Epoch 43, Batch 300: Loss = 0.6284, LR = 0.010174\n",
      "Epoch 43, Batch 350: Loss = 0.6230, LR = 0.009830\n",
      "Epoch 43/50 completed.\n",
      "  Train Loss: 0.0667\n",
      "  Val Loss: 0.4833\n",
      "  Val Accuracy: 0.8276\n",
      "  Current LR: 0.009551\n",
      "Epoch 44, Batch 50: Loss = 0.6180, LR = 0.009217\n",
      "Epoch 44, Batch 100: Loss = 0.6072, LR = 0.008888\n",
      "Epoch 44, Batch 150: Loss = 0.6391, LR = 0.008564\n",
      "Epoch 44, Batch 200: Loss = 0.6064, LR = 0.008246\n",
      "Epoch 44, Batch 250: Loss = 0.6217, LR = 0.007933\n",
      "Epoch 44, Batch 300: Loss = 0.6152, LR = 0.007626\n",
      "Epoch 44, Batch 350: Loss = 0.5921, LR = 0.007324\n",
      "Epoch 44/50 completed.\n",
      "  Train Loss: 0.0646\n",
      "  Val Loss: 0.4783\n",
      "  Val Accuracy: 0.8253\n",
      "  Current LR: 0.007081\n",
      "Epoch 45, Batch 50: Loss = 0.6030, LR = 0.006790\n",
      "Epoch 45, Batch 100: Loss = 0.6113, LR = 0.006504\n",
      "Epoch 45, Batch 150: Loss = 0.6091, LR = 0.006224\n",
      "Epoch 45, Batch 200: Loss = 0.6029, LR = 0.005950\n",
      "Epoch 45, Batch 250: Loss = 0.6215, LR = 0.005681\n",
      "Epoch 45, Batch 300: Loss = 0.6137, LR = 0.005419\n",
      "Epoch 45, Batch 350: Loss = 0.6084, LR = 0.005162\n",
      "Epoch 45/50 completed.\n",
      "  Train Loss: 0.0674\n",
      "  Val Loss: 0.4766\n",
      "  Val Accuracy: 0.8279\n",
      "  Current LR: 0.004956\n",
      "Epoch 46, Batch 50: Loss = 0.6048, LR = 0.004710\n",
      "Epoch 46, Batch 100: Loss = 0.5996, LR = 0.004470\n",
      "Epoch 46, Batch 150: Loss = 0.6135, LR = 0.004236\n",
      "Epoch 46, Batch 200: Loss = 0.6177, LR = 0.004008\n",
      "Epoch 46, Batch 250: Loss = 0.6178, LR = 0.003787\n",
      "Epoch 46, Batch 300: Loss = 0.5929, LR = 0.003571\n",
      "Epoch 46, Batch 350: Loss = 0.6114, LR = 0.003361\n",
      "Epoch 46/50 completed.\n",
      "  Train Loss: 0.0637\n",
      "  Val Loss: 0.4751\n",
      "  Val Accuracy: 0.8261\n",
      "  Current LR: 0.003194\n",
      "Epoch 47, Batch 50: Loss = 0.6078, LR = 0.002995\n",
      "Epoch 47, Batch 100: Loss = 0.5895, LR = 0.002803\n",
      "Epoch 47, Batch 150: Loss = 0.6018, LR = 0.002617\n",
      "Epoch 47, Batch 200: Loss = 0.6143, LR = 0.002437\n",
      "Epoch 47, Batch 250: Loss = 0.6199, LR = 0.002264\n",
      "Epoch 47, Batch 300: Loss = 0.6206, LR = 0.002097\n",
      "Epoch 47, Batch 350: Loss = 0.6053, LR = 0.001936\n",
      "Epoch 47/50 completed.\n",
      "  Train Loss: 0.0665\n",
      "  Val Loss: 0.4742\n",
      "  Val Accuracy: 0.8274\n",
      "  Current LR: 0.001809\n",
      "Epoch 48, Batch 50: Loss = 0.6179, LR = 0.001659\n",
      "Epoch 48, Batch 100: Loss = 0.5979, LR = 0.001516\n",
      "Epoch 48, Batch 150: Loss = 0.5995, LR = 0.001380\n",
      "Epoch 48, Batch 200: Loss = 0.6086, LR = 0.001249\n",
      "Epoch 48, Batch 250: Loss = 0.6177, LR = 0.001126\n",
      "Epoch 48, Batch 300: Loss = 0.6003, LR = 0.001008\n",
      "Epoch 48, Batch 350: Loss = 0.5785, LR = 0.000897\n",
      "Epoch 48/50 completed.\n",
      "  Train Loss: 0.0658\n",
      "  Val Loss: 0.4783\n",
      "  Val Accuracy: 0.8263\n",
      "  Current LR: 0.000811\n",
      "Epoch 49, Batch 50: Loss = 0.6133, LR = 0.000712\n",
      "Epoch 49, Batch 100: Loss = 0.6050, LR = 0.000620\n",
      "Epoch 49, Batch 150: Loss = 0.6067, LR = 0.000534\n",
      "Epoch 49, Batch 200: Loss = 0.6106, LR = 0.000454\n",
      "Epoch 49, Batch 250: Loss = 0.6079, LR = 0.000381\n",
      "Epoch 49, Batch 300: Loss = 0.6130, LR = 0.000314\n",
      "Epoch 49, Batch 350: Loss = 0.6179, LR = 0.000255\n",
      "Epoch 49/50 completed.\n",
      "  Train Loss: 0.0620\n",
      "  Val Loss: 0.4758\n",
      "  Val Accuracy: 0.8272\n",
      "  Current LR: 0.000210\n",
      "Epoch 50, Batch 50: Loss = 0.6117, LR = 0.000162\n",
      "Epoch 50, Batch 100: Loss = 0.5936, LR = 0.000121\n",
      "Epoch 50, Batch 150: Loss = 0.6045, LR = 0.000086\n",
      "Epoch 50, Batch 200: Loss = 0.6245, LR = 0.000058\n",
      "Epoch 50, Batch 250: Loss = 0.6277, LR = 0.000036\n",
      "Epoch 50, Batch 300: Loss = 0.5880, LR = 0.000021\n",
      "Epoch 50, Batch 350: Loss = 0.6034, LR = 0.000012\n",
      "Epoch 50/50 completed.\n",
      "  Train Loss: 0.0626\n",
      "  Val Loss: 0.4775\n",
      "  Val Accuracy: 0.8265\n",
      "  Current LR: 0.000010\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete! Results saved to 'onecycle_training_results.png'\n",
      "Model saved to 'onecycle_trained_model.pth'\n",
      "Best validation accuracy: 82.79%\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader, Subset\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np\n",
    "from torch.optim.lr_scheduler import OneCycleLR\n",
    "\n",
    "# Set up device for training\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \n",
    "                      \"mps\" if torch.backends.mps.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Define a simple neural network model\n",
    "class SimpleNN(nn.Module):\n",
    "    def __init__(self, hidden_size=128, dropout=0.2):\n",
    "        super().__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(28*28, hidden_size),\n",
    "            nn.BatchNorm1d(hidden_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(hidden_size, 10)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        return self.model(x)\n",
    "\n",
    "# Set up data paths and hyperparameters\n",
    "DATA_ROOT = os.path.join(os.path.expanduser(\"~\"), \"data\")\n",
    "os.makedirs(DATA_ROOT, exist_ok=True)\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "EPOCHS = 50\n",
    "MAX_LR = 0.1  # Maximum learning rate for OneCycleLR\n",
    "\n",
    "# Define transforms for the dataset\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.RandomRotation(10),\n",
    "    transforms.RandomAffine(degrees=0, translate=(0.1, 0.1)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "# Load Fashion MNIST dataset\n",
    "train_dataset = torchvision.datasets.FashionMNIST(\n",
    "    DATA_ROOT, train=True, download=True, transform=train_transform\n",
    ")\n",
    "test_dataset = torchvision.datasets.FashionMNIST(\n",
    "    DATA_ROOT, train=False, download=True, transform=test_transform\n",
    ")\n",
    "\n",
    "# Use smaller subset for faster training (optional)\n",
    "train_subset = Subset(train_dataset, list(range(50000)))\n",
    "test_subset = Subset(test_dataset, list(range(10000)))\n",
    "\n",
    "# Create data loaders\n",
    "train_loader = DataLoader(\n",
    "    train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True\n",
    ")\n",
    "test_loader = DataLoader(\n",
    "    test_subset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True\n",
    ")\n",
    "\n",
    "# Initialize model, loss function, and optimizer\n",
    "model = SimpleNN(hidden_size=128, dropout=0.2).to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=MAX_LR/10)  # Start with lower LR\n",
    "\n",
    "# Calculate total steps for OneCycleLR\n",
    "steps_per_epoch = len(train_loader)\n",
    "total_steps = steps_per_epoch * EPOCHS\n",
    "\n",
    "# Configure the OneCycleLR scheduler\n",
    "scheduler = OneCycleLR(\n",
    "    optimizer,\n",
    "    max_lr=MAX_LR,\n",
    "    total_steps=total_steps,\n",
    "    pct_start=0.3,  # Spend 30% of training time in the increasing LR phase\n",
    "    anneal_strategy='cos',  # Use cosine annealing\n",
    "    div_factor=10.0,  # Initial learning rate is max_lr/10\n",
    "    final_div_factor=1000.0  # Final learning rate is max_lr/1000\n",
    ")\n",
    "\n",
    "# Training function\n",
    "def train_model():\n",
    "    # Initialize history tracking\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    val_accuracies = []\n",
    "    lr_history = []\n",
    "    \n",
    "    # Training loop\n",
    "    for epoch in range(1, EPOCHS + 1):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        \n",
    "        # Training phase\n",
    "        for i, (inputs, labels) in enumerate(train_loader):\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            # Zero the parameter gradients\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            # Forward pass\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            # Backward pass and optimize\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            # Update learning rate\n",
    "            scheduler.step()\n",
    "            \n",
    "            # Update statistics\n",
    "            running_loss += loss.item()\n",
    "            \n",
    "            # Print batch progress every 50 batches\n",
    "            if i % 50 == 49:\n",
    "                print(f'Epoch {epoch}, Batch {i+1}: Loss = {running_loss/50:.4f}, '\n",
    "                      f'LR = {scheduler.get_last_lr()[0]:.6f}')\n",
    "                running_loss = 0.0\n",
    "        \n",
    "        # Validation phase\n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for inputs, labels in test_loader:\n",
    "                inputs, labels = inputs.to(device), labels.to(device)\n",
    "                outputs = model(inputs)\n",
    "                loss = criterion(outputs, labels)\n",
    "                val_loss += loss.item()\n",
    "                \n",
    "                _, predicted = torch.max(outputs.data, 1)\n",
    "                total += labels.size(0)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        # Calculate epoch statistics\n",
    "        epoch_train_loss = running_loss / len(train_loader) if running_loss > 0 else 0\n",
    "        epoch_val_loss = val_loss / len(test_loader)\n",
    "        epoch_val_acc = correct / total\n",
    "        \n",
    "        # Store history\n",
    "        train_losses.append(epoch_train_loss)\n",
    "        val_losses.append(epoch_val_loss)\n",
    "        val_accuracies.append(epoch_val_acc)\n",
    "        lr_history.append(optimizer.param_groups[0]['lr'])\n",
    "        \n",
    "        # Print epoch summary\n",
    "        print(f'Epoch {epoch}/{EPOCHS} completed.')\n",
    "        print(f'  Train Loss: {epoch_train_loss:.4f}')\n",
    "        print(f'  Val Loss: {epoch_val_loss:.4f}')\n",
    "        print(f'  Val Accuracy: {epoch_val_acc:.4f}')\n",
    "        print(f'  Current LR: {optimizer.param_groups[0][\"lr\"]:.6f}')\n",
    "    \n",
    "    return {\n",
    "        'train_losses': train_losses,\n",
    "        'val_losses': val_losses,\n",
    "        'val_accuracies': val_accuracies,\n",
    "        'lr_history': lr_history\n",
    "    }\n",
    "\n",
    "# Train the model\n",
    "print(f\"Starting training with OneCycleLR for {EPOCHS} epochs...\")\n",
    "print(f\"Initial learning rate: {optimizer.param_groups[0]['lr']}\")\n",
    "print(f\"Maximum learning rate: {MAX_LR}\")\n",
    "\n",
    "training_history1 = train_model()\n",
    "\n",
    "# Plot training results\n",
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "# Plot training and validation loss\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['train_losses'], 'b-', label='Train Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['val_losses'], 'r-', label='Val Loss')\n",
    "plt.title('Loss vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot validation accuracy\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['val_accuracies'], 'g-', marker='o')\n",
    "plt.title('Validation Accuracy vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot learning rate over time\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['lr_history'], 'k-', marker='.')\n",
    "plt.title('Learning Rate vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Learning Rate')\n",
    "plt.yscale('log')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Training complete! Results saved to 'onecycle_training_results.png'\")\n",
    "\n",
    "# Save model\n",
    "print(\"Model saved to 'onecycle_trained_model.pth'\")\n",
    "\n",
    "# Print best accuracy achieved\n",
    "best_accuracy = max(training_history1['val_accuracies'])\n",
    "print(f\"Best validation accuracy: {best_accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "02844728",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/1213289703.py:3: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"b-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history['train_losses'], 'b-',linestyle=\"dashed\", label='NARS: Train Loss')\n",
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/1213289703.py:4: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"r-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history['val_losses'], 'r-',linestyle=\"dashed\", label='NARS: Val Loss')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/1213289703.py:16: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"g-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history1['val_accuracies'], 'g-',linestyle=\"dashed\", marker='o', label=\"OneCycle\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/1213289703.py:26: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"k-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history1['lr_history'], 'k-',linestyle=\"dashed\", marker='.', label=\"OneCycle\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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NISIiC2BrT3iI8sHnGBGBUK9eveSbb74J9mYQERGRxUREIFSzZs0E1xohIiKiyBP0QGjlypXSqFEjTVxDE9e8efPilBk7dqwUKFBAUqdOLZUrV5b169cHZVuJPPXPP//I8uXL9drfZbwpR0Shq3379nqeHDZsmMP9OG+66iIqXry4pEqVSk6dOuWygQB/ExUVpefWYsWKydChQzV52d7cuXOlSpUqkilTJm1QKFmypLz22msSToIeCF2/fl3Kli2rwY4rM2fOlD59+sigQYNk8+bNWrZevXq2IXNQrlw5KVWqVJzLiRMnAvhOKFIkFHRMmjRJ8ufPL0888YRe47Y9JPV99tlnDmVGjBihB6tz5865fZ6JEycm6vU83W5PyxBR8CBo+eijj+TixYvxlvvzzz/l5s2bmhv79ddfuyzTuXNnnUpg37590r9/fxk4cKCMGzfO9vjSpUulZcuW0rx5c22A2LRpkwwZMiRJiclWFGU4h39BhMgU0WfTpk1t96EFqGLFivL555/rbUy8hLkfXnnlFenXr5/Hz/3HH3/oc8yePTvecrdv39aL6cqVK/p62Om8WX3+7Nmzkj17ds5MGgCBrG8EGd26ddPXxGvhoNGpUydZtWqV/P3337Jz50755JNP4vyqOnr0qDzwwAP6/yJFiuhkbq7ky5dPH0MgUrBgwTgTvmEmVfzCw4gXDBddvHixy3JPPfWU1gdGVOBy4MAB+fXXX23bjYMeAif8ysN+jesff/xRevToEee9OTt27Jhs3LhRKlSooNtL/sXjiW9hBmN8V/G9QVDhCk70+K4lBN9TfLeKFi1q+377U4cOHeT8+fNy8OBBadiwoXz88ce2FqFnnnnG4TjQsWNHyZkzp9SoUUNbcPbu3evwXLVq1dKGhdGjR9vuw3caP6bmzJmjt/F327dvl2XLlvn1fXla3+4+TxwzzV4jezh/Y6DU5cuX4z1/p7D67J+IQHHQNuFAUKdOHVm7dq1fXhNNg4MHD45zPw5EqHBPYGdExeNkyAOX//myvtGKiC8VDpLorrX/QiGYRhBkBjl43e7du0v58uXlhRdeiLcVBYGDOVzX3W8P/BDAY2jt3LBhg8tZb3HAMH+N4b26K7do0SK324Ly+FWHS3xlzPeG933o0CE9oOzfv19/TJh1/d577+kB17lZ3l09kvd4PPEtfH9Qp5i7BhdzYj4TbmOIPeakiW9E0rRp0zRQMH84IKB48cUXvdoW/EjxZtQTXgvl8b1r27atvPzyyxqAmVMC4P0AZmmeNWuWtgqhewz7D77Hjz/+uMP7NOfwMQxDVq9ercFS4cKFbc+DH1u7du2SrVu3ai+LP5j1ndgRYNhW1AsCROdgCvXgCUsHQugmQAUhqrWH287RbXwQOG3btk274bDTYAepWrWqy7IIutAV59wihF9j3rQI4QPlL7jA8FV9O7f2tGrVSr9k6JLFLzBXsH9eunRJqlevLqdPn5YsWbLYAgUTngu/tHBQgV9++UVKly7tEMDgoHv48GHbr0q0guLvnMvgwJY1a1b9kYDH0JLjXA63Ecwj8MIBHt8VdDE7w36N7cdBEt8Nd+/t999/l4ULF8Z5HK85YMAAadGihR484dNPP9Vfk2vWrNE6wOfy1ltvab1iGnznA1Wgf1GHIh5PfAs/aHGCxAR+uGDfR5Cf1M/o1Vdf1Ys3sB3p0qXzuDw+f1zQ3TVy5Eh5//339bhlTiSI9wM4BuE7hRYfQPcWuseQF2TCPoVW38mTJ+vxBAEiWlQwytp8Hvwf3+VHHnlEW4qQK/Tkk09KmzZttGXalxLbIoRtRZ3guOjcIuSuxS8Ow0KwOXPnzrXd/vfff/W+NWvWOJTr27evUalSpYBs0+XLl3UbcO2pe/fuGSdPntRr8r+k1veVK1eMr776yoiKitLP2t0lT548ce5Lnjy5cfz4cYfnmzhxot5vPo7bznxVxpNy2L5kyZLFu91HjhxxW2bq1KlG7969jbp167qsl+XLl9uep2bNmm7rD89/48YNW9nOnTvb6hyPjRgxwuVniG1YtmxZnHqOFDye+NbNmzeN3bt36zVcu3Yt3u+9Py94bW+0a9fOaNKkif5/xYoV+h3Fe8F50/50XqVKFf0+mTZs2GCkTZtWj3WmGjVqGO3btzcOHDhgrF+/3qhfv77x/vvvu3zdgwcP6jGyU6dORubMmY0yZcoY169fN3zh/v37xp07d/TaF59nYs7flg6Ebt++rR+0/X3Qtm1bo3HjxgHZJgZC1udJfdufTPGF27ZtmzFs2DA9cadIkcLtgQon6yVLlhjnzp3zKjjB6yBAiO/k7asynpTzReDlSUD13nvvuQ2CYmJiHJ7LVbmUKVMaRYsWNZ588kn9/mMbzNfENQ7G7t5/uAZLPJ74lvOJE8cDBCTm5erVq8bFixf12v5++8u+fftcfhdwv7u/cXXx9uRvHwhBgwYN9LZ9ILRr1y7b9wXbZF5w34QJExwCoV69etlu4/3mzJnT+O233+LdhsOHD+sxc/LkyYYvMBBKIBACtPz07NnTdhsHA/wyHzp0qF+35fPPPzceeugho1ixYgyELC6h+rY/maIFIlOmTHFOwAUKFIjTIuSqtceb4MRqfBF4JTZYOnr0qHH27FlbOQQt8f1SzpIli8vnwqVs2bJG69atjYEDBxrffPON0b9/f4dgyV1wGqp4PPGt+E6c3pyYPf1R5EvOgdD27dt1n3/zzTdtgVCfPn2M6tWrGzt27HC44P7KlSu7DYTgww8/1O9XfO8dj2XMmNH47LPPDF9gIPT/otAtW7boBRs8cuRI/T8OnDBjxgwjVapU2jyPN9ulSxdtmjt16lRAto8tQtbnrr5xe/r06S5PpqlTpzaefvppDXjR7BusA1sowndzzpw5tu+oM0/q0V3AhG5wBGLz5s1LMFiKrwsOQdKiRYt0G80DbKi2GvF4Ys1AKBg/ipwDIXjxxRf1eIZ9H9udPXt248svv4zzt3jPKLNz5063gdD58+eNNGnSGLNmzdLbgwYN0lQUvEe0BG3evFm701Bm7969PnlPDIQMQyvY1cEMH7gJkWe+fPmM6OhobSFat25dwLaPgZD14WQ3e/Zs24kZzdP/+9//jLx587o9WS5evNjlc4Vqa48VuyITqsfEtC7hNpr3P/roI+22xK/XhAKjdOnSGfnz53d4jvi6NK0WLPF4Yt1AKNBcBULI78O5Efs2joPYv901FKCXA/l+7gIh6Nq1q1GyZEnd3/BdaN68uR5L8RroOnvqqaeMVatWGb5ihUDIUvMIWRFGjWFkTkLzEDiPIMAQaIwS4igP/8KIiS5duthG1mC4NkZfmfCZYWSG/W6OERaYR4SjlBLHl/s3Ro1hRB7mVnL1eeDz7dq1q2048/jx4x3mNsLfYzSL/ag57AeYRwlzN2G4vzkU2J65D2AYMoYwY3QN9psPP/zQNmpwwoQJLudRCjQeT3zLnHfG3TxC5pByjEbiwqz+l9T6ju/z9Pj8nagQLAIwR8j63OWQ4D4kEc6cOVN/JbDLy7cCvX8nJW8JvzTRre6qpQhJoeYvaXddbO5eM5AtRzye+FYotwiFo/tsEbI+tghZD3ZZTPKFSS4xO7IzzGODWVa9aXkgz1lx/47v83XVaoQWIcyvhBnBMXMuJpvDpHHOsNwI5l7BPocJJs2le7DEQaBajqxY36GMLULWYligRcjSEyoSOZ8Qfv75Zz0JrVu3Tif6czXpYKVKleL8LU6ODIDCV3yfL+5HsOLcxYagCRd3wRL2LfNxdJvhb5yhPLpmMUGm837HySKJQgN/XpCl4WSyZMkSXZQUqx43a9ZMgyDMaorHcIIzZ1U1T3A86ZAztNggJwgtPLh2bsExgyX7fQm3zX0pTZo0uvAz1j50FQyNGjXKdhszFWM2bU8WwiWi4GPXWALYNRY8mP4da+nY76L4LHAfprLHkg3ARUADK5z374S6UF21HMGUKVOkffv2+v/vvvtO156zh3rCgrwPPfSQ161G4VzfwcCuMWsxLNA1xm+VG2PHjpUSJUromk8UeDhBYCV0+yAIXxIstouRPWYQBDiBPProo2wJoiTDPoScoIS62exbjiZOnGgLgmDfvn0ugxm0aOJ4gsUtAa1EbDUiCj4GQm7gJLx7925d3ZsCBwmsCH7wK9n5Vzfux8KmRFbuZkPOkKuWG+y/aLnEL1ME+ua0D4Br5DDhfiIKLAZCZAknT57UEwpa4ebNm6ddBc4nE/z6NpNXiazacuSu1ej48ePy/fffa8uQq0AfidyNGzeWYcOGyebNm22PIzhCKxKDJCL/YCBEQYMD+6JFizSxtFixYjJ58mT91YzuL1cnEyZCUyi3GmHfbd26tQb4rgJ92LJli/Tv31/Kly8vuXLlki+//FJzH5599lm9ZvcZke9x+DwFBX4ho2vAPgcIw48x+gb5PoCTR7169Tj/D0XEkP7BgwdrYidGSSKAyp49u/Ts2dOh+6xz5856/9NPP237kUAUypIlSyazZs2S5s2bB28bgvbKFLHM/Aj7IMj8MphBkKfJq0Th0mr09ttva+CDubIuXLggAwYMcJkn16RJE20tQoL27NmzdWQMvlN4HnafhT90sXbs2FFy584t0dHRmmjfq1cvOX/+vM9f68qVK7pfFi9eXEdkYZBKnTp15Mcff3Q4foc6tgjFM2oMF/xaI99CfoTzlwgHfExax+HvFEnctRqlTJlSHn/88TgThmLkZPr06XVW9a+//lovaBlCGXynrLRGGvkejpFVq1bVVILp06drdylm2e/bt68sXLhQ51jLkiWLT17r0qVLug9i6PkHH3ygIx4xxH3FihXy5ptv6mjHzJkzS1hI1OIeEYSrz/vGuXPnjKVLl7pdIwxrRCV23SbWd2CxvgPH1TpqWJcJa6+9/vrrRsGCBeOskYZy/fr1M/bv3x/szQ/7tcYCueYcYOX3Bx54wLhx44bD/fg+pk2b1ujWrZvezp8/vzFkyBCjQ4cORvr06XX1+PHjxzv8zbFjx4znnnvOyJQpkxETE2M0btxYV7I3de/e3UiXLp3x77//xtmOq1evGnfv3jUGDx6sK9U7K1u2rDFgwADb7UmTJhklSpTQtf1iY2ONHj162B7DPjtr1ixbfSe0Xf5Ya4yBUAIYCCXd/PnzdefPkCGDbYf25UKorO/AYn0H1tGjR405c+botTOchN0tGotLmTJl9GS1a9euoJy4rcjdifPatWt6wUn+4sWLem3e56rs2LFjbT/ocI3buN85SDGfw/nirfPnzxtRUVHGhx9+6PLxzp07a+CAgAKBUJYsWXSbDhw4YAwdOlS3ce/evVoWgR4WFe/YsaOxfft2rY/nn3/eePDBB43bt2/rdxvP1aVLl3i3yfxRu379ett9mzdv1u08dOiQ3v7iiy+M1KlTG6NHjzb27dunZUeNGuUyEEpou1xhIBQADIQSB18QBEAtW7a0HZSxg2Pn9nRVcU+xvgOL9W2d+nbVuorb1apVs/3QMC/4MYITlFkmKT8+Qpm7E2d8AWWDBg0cyuLE7q5sjRo1HMpmy5bNZTlvrVu3Tv9u7ty5Lh8fOXKkPn769GkNhF544QXbYwgycuTIYXz55Zd6e9q0aRpc2Ld63b5920iTJo2xePFifQ48F54zIfXr19fWI9Mrr7xi1KxZ03Y7d+7cxttvv+327+0DoYS2y1+BEJOlyefMGXMbNWokM2fO1PveeOMNnRsFi1OamAhNlDTu1khbuXKlLsuBpT8wwgw5R6dOnbLl5pkTOCLnhLwXzERhT1+7TJkyDrllSHTGPgHbtm3T0bgZMmTQnDNckFuE5SoOHTrk1fvDSEbkK+Fv79y5o3NlIZkb8HonTpyQ2rVre/RcCW2XvzBZmnw+ogFfDOcRYRjV4GpdHyJKGnfTTOAEgpFluMyfP18na7SHgSBYxBjl2rRpo3MVIfnVk/XPwtG1a9fcrn3lPFXB9u3bdd04+0R2lMFqBHnz5nUoixGBvoDPFtuzZ88e/dyc4f6YmBidXgEQ/NrD35rbi/eKuaqwLp4z/D0CEewLmOk/IfjBi0Ww586dq6PY7t69q/uSuVixNxLaLn9hIOQGR40lDg7GrkaE4f5IOqgSWWXOInj44YfjjEDDiXvHjh36ff3jjz90WSHMer1169aIHIGWLl06jxcBxagt53mgMOEr7nf3vEmVNWtWefLJJ+WLL76Q3r17OwQZaO1D8NC2bVuPFi595JFHtLUeC/lmdLMYaatWrWTatGkyaNAgHarvHLDghy3qCJd27dpp6yMCIfyduW0IqAoUKCBLly6VWrVq+WS7/CLBDsAIxxwhz5h9ur4eEeaJSKzvYGJ9h2Z9uxqggATsYcOGGaVLl3aZx+Lv7244jBrzRZ6jpzASEDlHyAFbsWKFjrBauHChUapUKaNo0aKaUA3IEbJPSDZHcg0aNEj/f/36dS2PXJ6VK1cahw8f1veB/B7zveC5ihcvrqPUvv76a024x+tjBFiRIkU0odx+u7Cv4IJcJntTp07VnKpPP/1Uy23atMkYM2aMyxwhT7bLGXOEyBLmzJmjeQjoH+bSGEShs+wH5u3CEjfo6vnqq6/i/A1aO9Cae/XqVZ1cz8QJHIOT54juSizcW6hQIWnRooUULlxYJ6dFawuWJvJ0DqG0adNqHhk+/2eeeUa7+bA/IBfHbInBc2FeohdeeEHnEUKrYrVq1TQfaPjw4ToLuv12YTJcTLxYuXJlh9dCa9Ho0aO1JQstjg0bNtSu18Rul194EIRGNLYIuffff/8Z/fv3t/16tI/yA/lLKVLq2ypY3+FZ3/G15n788cc6Tw3mpcEcRfbDxkNt9JkvW4To/6CuChcubHzyySeGt5Ja375oEWKOEHnFTKRE4hpmF8VspvD6669L9+7dPc5ZICJrcbX+mdmau2rVKrlx44bmgdgzR58hWZvf98h09uxZmTFjhuYpdejQQUIRAyHyalg8mmHtEy6RFIcFVJ9//vmgbhsR+W8E2k8//SRr1qyR999/XxYvXuzwNwia8OOIgVBkypEjh2TLlk2DaIxaC0UMhMirhVKdF4HE4ntPPfVU0LaLiHzLVWsuRiI99thj+qMHc4Q5r3+GHBETHsOIs0gdhh9pjDBYfJXJ0uQRHNCcgyDg3EBEkcN5MAQCnnfffdcW6GAuGyTwYh4ZBExYmBPXaE0msiq2CLnBeYQc4Vedq3lI0HxORJHDXfcZYOQZRqTZTyJo1TyicGjJIPHJ58gWITcwuRhmCd2wYYNE+k723nvv6YGNw+KJKL5h40OGDJG+ffvGKY8flJjd2grMYxim+6DQhyR+VzNpeyMKQ8d8uE1hB3NnYL6Ey5cvezyPAX4BYY0VJJGhFSVU4X1gaYzPP/9c62D//v168HD1SzCYwqW+QwXrO7BCrb6RG+ScRwRYsgHrTnm77IKv4ZR37NgxXQoCMyY716knM0uT7yS2vvF3CILw3cC+lStXrkSfv9k1Ri7hIIGhkJi2HTvn0KFD9UAMVgmAiMj6w/ARaFSpUkUnXTWDIJzEFixYoAuDYuHXQCZU43iGk+aRI0fk6NGjcR7HtpkJ3wyE/C+p9Y0gCAvKJgUDIYrj5s2bOmspDlSI0r/55htp3bp1sDeLiMIgjwgw/5j9IrCBXtcMa2Ih+HLVPYaT8vnz53Vtr1BogQt195NQ3+gOc14QNzEYCJFDk/aWLVu0n/+vv/7SEWGzZ8/WX3JERN6Ib1JV5yUWcDLE9BwYZVawYMGAbB9Ouq5GvWJbcILFYwyE/M8K9c1PmRSGt6JfH7/SzCBoyZIlDIKIyOfQJebqhIi5irDiOUfrUiAxECKXkyWiyThQv8yIKDKn43B28uRJ+fjjj+PkinCRV/InBkIkO3fujDPCA7fRv09E5O+JGXGNeds++ugjGTZsmC1IwqigV155hZMzkl8xRyjCXb9+Xd555x39BWY/kwInSySiYCZUA0arYvoOq0/OSKGNgVAEu337tjRr1kw2btyow1rRHea86jQRUTASqs2VzZ3hGIXgiccn8hV2jbmBZtoSJUpIxYoVJRxhAqtWrVrJb7/9JunSpZOlS5fq7NHoh8d1oIaxEhG5M2DAAJe5REiovnDhQlC2icIPA6EIXGIDzcuYLHHevHmSKlUq+emnn6Rq1apup80nIrJCLpGZRD158mQ9RhP5AgOhCIM8oJ49e8q3336rB5dZs2ZJ7dq1g71ZREQuoXXabK3G0hiY1uPhhx+WQYMG2cqgW//48eMcWUaJwhyhCIMWrnHjxukvKzQvN2rUKNibRETkcS4RruvUqeMwxP7JJ5+UlStXBmWWagp9DIQiBH4lYTZXzN8xffp0XYyOy2YQUSiyD4K2b99uC4KAI8vIW+wai6BZo815OK5duyadO3cO9mYRESUZ1qlyNbJsxowZQdkeCj0MhCKgJQhBjzlhovlrif3oRBTOs1T37dtX2rRpI+fOnQvKdlHoYCAU5ubOneswUaL9PBxEROE4SzUGgKD7bNGiRQ7HPy7VQa4wRyiMHTp0yGFkhYmzRhNRuM9SjYEhWLsse/bsthQBc01FJlSTPQZCYdxv3qBBA7l48aLmBeEXEGeNJqJImaXafjJcM0XAbB1iQjXZYyAUxktn7N+/X/Llyydr1661dYe5W9OHiChcYcSsuxQBHg+JgVAYeuutt2TVqlWSMWNG+eWXXyRXrlx6P7/wRBTJCdXmoBHT6tWrpUaNGg7D8SnyMFk6DGG0RKVKlWTOnDlSqlSpYG8OEZGlEqrt1zJDCgFyiShyMRAKQ3ny5NHuMMy+SkREjkt1HD16VEaPHq1rLWJk2aZNm4K9eRRE7BoLA0gExORhadKksS1E6GpeDSKiSGafUN2rVy9dmmP+/PnSsGFDWxmsWbZx40ZNtkaOJYU/BkJujB07Vi9IqLMy+yGhsHfvXvnss8+CvVlERJZXokQJvZhGjBihqQXAIfaRI8pwTqUnB1iTK1OmTHL58mVNPvYEgpIzZ85Ijhw5/Noyg5YgDI23TwBEHziafyMpMTpQ9U3/h/UdWKzvwMDxFC1A9qfESDyehtP+7en5m9+qEIbFBp1HQXDWaCIi3w2x37ZtW9C2iQKDgVCIwhd01KhRce7nrNFERL5bs+zVV1+V3bt3B2WbKDAYCIWoPn36yO+//y4pUqSwfXk5azQRkW+G2OO4GhMTI4cPH9bpSNasWRPsTSQ/YbJ0CELzLUaIwffffy9Vq1blrNFEREmExGiMJMOosQoVKkjq1KmlVatWcvbsWSlbtmywN4/8hIFQCMIsqMOGDZPnn39eypQpo/cxACIiSjocS6Ojo23Ju0uWLJFz585JunTpbD9Ed+7cqfehO43H3tDHrrEQsm/fPrl165btthkEERGRfyD9IDY21nb72Wef1WPvE088oaN2MYUJhTYGQiECQzixJg5mi8bK8kREFFiHDh2SH3/80XbbXMUeQ+8pdDEQCgGXLl2Sp59+Wk6fPi3Xrl3TZlsiIgqsY8eOuRzBi6H3FLoYCFkcRiygCRbDN3Pnzi0LFiyQDBkyBHuziIgijrsh9lOmTLH8KgTkHgMhC5s4caKOBNuyZYvefumll5iYR0RkkSH2GLgC06ZNkxYtWsSZkJFCAwMhi8LCf1hDzP6LNWTIEPZFExFZZBV7dJXNnj1bV7HHsHszMKLQwuHzFrV+/XqX071jviC2ChERWWMVe1w7r1SPYzeDotDBFiGLqly5cpy+aC6fQURkPfZBEEb11qpVSzZt2qQt+Gg5Yku+tTEQshCMCDOncXfui+byGURE1te/f39ZsWKFPProoxogcb4h62MgZBEXLlzQPmbME7Rq1ao4fdG4xm0iIrKuESNGyOOPPy537tyxpTdwviFrYyBkASdPntTJEtetW6dr26RMmdL2GFqAatasyZYgIqIQkDFjRnnnnXfi3G/meJL1MFnaAvMEoSUI17ly5dJ1bUqVKhXszSIiokQqUaKE5niiJciE28zxtKZkkTAMHS0q2DGxPsysWbPECtBEOnnyZF05HkFQoUKF5M8//2QQREQU4pxzPCFLliySPXv2oG4XRWiLEBbMGz16tJQrV05OnTol5cuXlwYNGthWEg4GJM1hjiDz1wJmjEYQhBYhIiIKfcjprFevni6WjbmG3nzzTZ1viKwn7AMhBBdmgIEVhLNly6aJycEKhNASZB8EAdYQ4/TsREThOd9Q7dq1He7HOQgtRGQNQe8aW7lypTRq1EhbRTAB1bx58+KUGTt2rBQoUEATiTG/DiYbTAzM64CAI2/evBIsWJzPPggCJtEREUUGnONwPlu8eHGwN4WsEghdv35dypYtq8GOKzNnzpQ+ffrIoEGDZPPmzVoWzY1nzpyxlUG3F3JrnC8nTpxwiMDbtm2r/bZWW7SPEyUSEUUGnNOuXr0qTZs2ld9//z3Ym0NW6BqrX7++XtwZOXKkdO7cWTp06KC3x40bJ7/88osmGvfr10/v27p1a7yvcfv2bd3pUB6TXCVUFhfTlStX9BqtOM4tOe6gHOaPcFUeLV94D927d9eWIARBX375pd7v6fOT5/VNvsf6DizWd3jVN1aqRwPA/PnzpXHjxnobKRv4kRyJ06Tc92N9e/qcQQ+E4oMJqdCdhZk6TWhNwaSDa9eu9eg5UMHt27fX2T1ffPHFBMsPHTpUBg8eHOf+s2fPyq1btzyu/MuXL+trO7f+ALoCkbSNSRLRRIogyL6Fi7yTUH2Tb7G+A4v1HX71/dlnn8nNmze1RahVq1Z6H15r+PDh8vzzz0skue/H+kbLmyeiDOeVPYMIOUJz587V1htA11aePHl02QkMMzch+x5TmP/1118JPidGY1WvXl2HzpumTZsmpUuX9rhFCDlFFy9e1ImyPP1gEThhqCQPXP7H+g4s1ndgsb7Ds74PHTokxYoVc7gPPQSYTiWSWobu+7G+cf6OiYnRQCu+87elW4R8AVOde9PkhuGNroY44gPy5kNCUOft31Disb4Di/UdWKzv8KtvV8ttIF0CgZD9Iq6RIMpP9e3p81n6W4V+U0TIGF5uD7cxFJ6IiCgUceCMdVg6EIqOjtZcmqVLl9ruQ+sObtt3lfkDRrFhNuqKFSv69XWIiCjyOM8+jevx48drOoiFMlYiQtC7xq5du+Ywh86RI0d0FBgmm0LzIIbOt2vXTipUqCCVKlXSWaKRcW+OIvOXHj166AV9jJkyZfLraxERUeTOPo1zIFqC0Avywgsv6DQxyIWlCAmENm7cKLVq1bLdRuADCH6mTp0qLVu21ESqgQMH6hIZmDNo0aJFkjNnziBuNRERke9mn4YffvhBvv/+e5k+fbp2nTVr1izYmxcRgh4IYUHUhJoBe/bsqRciIqJw1aJFC11tAakZaBlatWqVPPLII8HerLBn6RyhYGKOEBERBRrSP+rWrSs3btzQOefsV0gg/2Ag5Abyg3bv3i0bNmwI9qYQEVGESJEihXaR4Yc4giDMPo2giPyHgRAREZGFYIAOluBA8jRWV2jdurUsX77c5dxDlHQMhIiIiCymUKFCutICgqIFCxboMlH58+eXSZMmBXvTwg4DISIiIgvCWpRYL8tcHQHXXbt2ZcuQjzEQcoPJ0kREFEwHDhyIs0QUluGwn3uPko6BkBtMliYiIqstw4F1uQoXLhy0bQpHDISIiIhCYBkOwLx7CxcuDOp2hRsGQkRERBZehuPvv//WUWPvvPOO3te7d2/Zv39/sDctbDAQIiIisnjLEFZhePfdd6VOnTo6r1CbNm3k7t27wd60sMBAiIiIKAQgXwhrcGJRcqzTicCIko6BkBscNUZERFaTJ08ezRuCoUOHyrp164K9SSEv6IuuWnnUGC5XrlzRCa2IiIisoHnz5po7FBMTIw8//HCwNyfkMRAiIiIKMV999ZUOpaekY9cYERFRiLEPgv777z/Zvn17ULcnlDEQIiIiClFnz56VatWqyeOPPy7Tp0/n8huJwECIiIgoRCFP6MyZM7om2fPPP8+FWROBgZAbHDVGRERWd+rUKZ1w0cSFWb3HQMgNrjVGRERWx4VZk46BEBERUZgtzFqkSJGgbVOoYSBEREQURguzIhDCMhzkGQZCREREYbIwK9Ykq1Spkty5cyfYmxUyOKEiERFRGLQM4VKuXDnJmDFjnO4yco+BEBERUZjInDlzsDch5DBkJCIiCjOYV6h///66FAfFjy1C8cwjhAuGIRIREYWSGTNmyLBhwyRLliy6SCuuyTW2CLnBeYSIiChUdejQQUqVKiUXLlyQwYMHB3tzLI2BEBERUZhJkSKFjBo1Sv+P3o09e/YEe5Msi4EQERFRGKpTp440btxYUzz69OkT7M0Jz0Do1q1bvtsSIiKiIMMaXatXr453rS48hjl7ElrPy5NyvirjzogRIyRlypSyaNEi+fXXX73++0jgdSCENU3ef/99yZMnj6RPn14OHz6s97/zzjtc8ZaIiEIWzmEFCxaUZ599Vq/RpXTt2jWHC+7DCu9PPPGEXrsq42m5xJTx9jyLJTh69eql/x8wYIAYhuGn2gtdUYaXtfLee+/J119/rdedO3eWnTt3SqFChWTmzJkyevRoWbt2rYSTK1euSKZMmeTy5cs6SZWnweKZM2ckR44cnNQqAFjfgcX6DizWd2CgtQWBhvMCplaDpTQwizQmT/QUzl9vvfWWdOzYUa5fv67BkTd/H6r7t6fnb69f9ZtvvtF1Tdq0aeOwtknZsmVl7969id9iIiKiINm/f7/lg6DEriyPYKBixYpStWrVRLcshTOvA6F///3X5aq22IHu3r3rq+0iIiIKmBUrVsS5Dz/2ESBhAVNc8H/nVgvnMp6WS2wZQKuOt61dXbp0sQV6uO7atWuico7CkdeBUIkSJWTVqlVx7p89e7Y8/PDDvtouIiKigJg/f77mvport5tByfjx47UbKU2aNHrB/+1XendVxtNyiSljat26tcvzsDsHDhyI09qVmJalsGV4ad68eUamTJmMYcOGGWnTpjWGDx9uvPTSS0Z0dLSxZMkSI9xcvnwZOVR67al79+4ZJ0+e1GvyP9Z3YLG+A4v17V9btmwx0qVLp8f5Ll26GH///bcxZ84c4+jRo27/5vjx48by5cv1Oj6elPOmzJ49e4yaNWvqtqZJk8ZYuHChR+8Rf58sWTL9O/OC2wltf6jv356ev70OhGDlypVGnTp1jOzZs+uH8dhjjxmLFy82wsnnn39uPPTQQ0axYsUYCFkc6zuwWN+Bxfr2nxMnThgPPPCAHuNxTrtz547l6/vGjRvG008/rducMmVKY/bs2R793cSJE43kyZPbAqEcOXJY4j3es0Ag5PWosUjDUWPWx/oOLNZ3YLG+/QN5NjVq1JBNmzZJ8eLFdcQzVm4Phfq+c+eOvPjii/LDDz/oNk6ePFnatWuX4N8hJ2jHjh3SqlUrPbf99NNPOuFiMIXkqDEMlT9//nyc+y9duqSPERERWRlOvm3bttUgKGvWrLJgwQINgkJFdHS0fP/99zocHu+lffv2muPkyaSLqVOnlhdeeEH/P3LkSMtOBGnp1ecxf4GrFdlv376tI8qIiIis7H//+5/8+OOPGlDMmzdPChcuLKEGCdRfffWVZMiQQT799FMZOHCgLdn7pZdeklq1ajmUR0AyceJE24SKKIeRch9++KFOHumqnCfP5UkZtPQg6btTp05iRR53jf3888963bRpU51QEc1NJgRGS5culd9++0327dsn4YRdY9bH+g4s1ndghWp9oxUAo5Xim7zPkzK+fC6UGTNmjAwfPlxvT5s2zdY6Eqr1ffz4cZ0XyOpZLsndTARpha4xj1uEEAABoj/nvkisY1KgQAH55JNPkrLNREQUBjBZnzlvDU5ub7/9tu0cYkJLzJAhQ+It42k5T8t88MEHtoChYcOGcYKgUIQh8K6CoEceeURiYmL0/xcuXJAtW7bEKZM3b15tDTOH57sr58lzJVTGHK5vlRmt7XmdLI0mtA0bNki2bNkkErBFyPpY34HF+g6sUKvvUF+qIhzq2/m9eVLGl8/l6etZpUXI61c9cuRIxARBRETkHUz05yoIwnkDJ0Fc3J1D7Mt4Wi6xZcJlQkG8P1cTM9oHHAmVMQxD/vvvP588l6sygOU9rNgaBIkaPo9hh0iyOnbsmA7js/fqq69KOGGLkPWxvgOL9R1YoVTfyNGpXr26nDp1yuF+tlD4H94jAjssgRVfjpRzmTlz5sigQYOkR48e0r179yQ9l6syyC/Gc6O+sR6p8xJdVmgR8npCxc2bNxuxsbFGxowZdXImTKoYFRWlM3MWLFjQCDecWdr6WN+BxfoOrFCp7127dum5AcdLXJuT9+Eak/nFN8GfuzKelvNVmVCqb18ZM2aM1knRokX99p7r16+vr9G2bdvwmFCxZs2aUqxYMRk3bpxGWtu2bdNkaSSd9erVS5555hkJJ2wRsj7Wd2CxvgMrFOp769at8uSTT8q5c+ekdOnS8vvvv2tvgS9aFTwt56syoVDfvnTt2jWtC5zjsOYaksh9DXnFlSpV0vrcs2ePxhAh3SKEdcb27t1r+//u3bv1/+vWrTMefPBBI9ywRcj6WN+BxfoOLKvX919//WVkzpxZj5Ply5c3zp07Z4Qyq9e3P/Tt21c/v1q1avntNRo2bKiv0aZNG8u1CHkdfqH1x4zaEMEhTwgQdWE+AyIiigx//vmn1KlTR1cWQDIs5pPDTM0UWl555RXN4cEkiGjd84fBgwfr9fTp07VVyEq8DoQefvhhbeYCrNOC2Sy/++47ee2116RUqVL+2EYiooiF7pzVq1f7ZCkDXy6dgHnj0B129epVTZlYsmSJw0S7FDry5s0rzz33nP5/1KhRfnkNzDOEuZ3QFfbee++JlXidI7Rx40bd8TGdNvr1sF7LmjVrdDZPTKJVrlw5CSfMEbI+1ndgsb6DMzEhJrPt1q2b1K5d26EMWmGQs2kud+CqjKflvC0DJUqU0B/HadOmlXAQqfv3+vXrpXLlytrrg56e2NhYn78GcooRI2DfwuKvJUuWtESOEFefd2Ps2LF6wVwT+/fvZyBkYazvwGJ9+9/du3dlypQp0rVrV7E6d8PQQ1Uk7999+/aVunXrSvHixTWpPKGlTxLj2Wef1SH7aIH64YcfwisQ2rx5s3aTYRXfcMIWIetjfQdWqNa3Vde+si+DgAILaU6ePDnOXDwmjMoyV0pHbg5+WcdXxtNySSmDrjR0j4WDUN2//bU8ygQfL5aK/adMmTK2FiKk1ITUqLFFixYZr7/+utG/f3/j0KFDet+ePXuMJk2aGMmSJdO5AsINR41ZH+s7sEKxvjFnDI5R+C7j2t08MwmV8eVz2ZfBXGylSpXSa9zGJVu2bA63zflvjh8/bnsO/N98DndlPC3nqzKhLhT3b185HqDPt0WLFvrczzzzjCVGjXkcCOFLiy9l1qxZtaIwkeK0adN02GTXrl1tw+jDDQMh62N9B1ao1bergzuOZa1atTI6deqkF/zfOehwLuNpucSWMS916tQxZs+ebdy5c8enEwUGemLCUBVq+7cvLVu2zOU+uXz5cp9Pvmnu/xs3bgydQKh06dLGxx9/rP/HlxRvomrVqmH1S8AVBkLWx/oOrFCrb3cHdytevv322zjbf/ToUWPOnDl67Q6OwzhZJXQ89qScr8qEqlDbv33peABb/Fq3bq3P36hRo6AHQh7nCKVLl0527dolBQoU0NECqVKl0n7hxx57TMIZc4Ssj/UdWKFW3z/++KM0b97c4T6MWnnzzTdtw73x/f74449tI6FclfG0XGLLhNvaV6Eq0ut70qRJ8tJLL+n//ZEjZMK6Y+aosY8++khatWol+fLls3aOEFqATp8+bbudPn16W55QOGOLkPWxvgMrlOp7586dtlmPzab4UOtiCqX6Dgesb8Po16+f7pc1atTw6+tUqVLF1vIUX16eZVqEEBl+8MEHkj59er391ltv6VC7bNmyOZTj6vP8RRForO/ACpX6xkz3jz76qI7MwqzHX3/9tfz7779c+4rixfoW2bdvnw6hx5xCZ8+e9ctEmdj38+fPr/Xtr6kYfD58Hl1iaN6NDx4/fPiwhBMGQtbH+g6sUKjvCxcuyOOPP65T+T/00EO6FESWLFkkFIVCfYcT1vf/wfcG3Vfff/+9tG7dWnwNqTVPPPGEX6di8PT8ncLTJ0SURkRkdTdu3JBGjRppEJQnTx5ZvHhxyAZBRMHSrFkzGTp0qMydO9cvgRDmzkKg6dwihNbRQIvccJeIws5///2nSZdY9geTACIIwjpKROSdpk2bSq5cubT7yh/Q/YVEbAQ/gOvx48cHZYZyj1uEiIisDL38WBtr/vz5kjp1ar3GqBQi8l7FihU1j8ef3YMYjYaFe7GGaYUKFXw+asxTbBEiorBYDb1du3Y69BcH7hkzZmiOEBElTlRUVEBypNAChEENwVyrji1CRJTo1dDbtGkTZy4xBEnfffedbQVzV2U8LedtGXjhhRekSZMmfnn/RJHm3r17sm7dOqlSpYqtGyvccPX5BHDUmPWxvgMDXU2NGzcWq+Nq6JQUrO//H8IDjB7DcPpVq1b5pZXVCqvPp0jME7uCX2uYbTo6OtrbpyQiC1u7dq0MHjxYE49dqVatmmTPnl3/jzlHcMCMr4yn5RJbBr9gMTdPuARCRMESFRWluUIIhDB6LGy7m72dqRGzs2IGSHeXfPnyGQMHDgybWTk5s7T1hWp9Y/0erIOV0JpOCZXx5XPZl1m9erVRt25dh5lfuRp64IXq/h2qWN+OsM4dvleFChUy7t+/b/haSK0+b/r666+NBx54wBgwYIDx888/6wX/z5s3rzF+/Hjjgw8+0CnthwwZYoQDBkLWF4r1jankzRO4u6nlPSnjy+eyL2N/SZEihdGxY0fj4MGDXA09CEJx/w5lrG9H165dM1KnTq3fr23bthnhGAh5nSNUu3Zt6dq1q7Ro0cLh/h9++EHnAFi6dKlMmzZNhgwZorNShjrmCFlfqNW3q6nloXDhwjqlPdy9e1cOHToU52/ty3haLillMJEavssFCxa03Xfs2LEEh7tacamKUBVq+3eoY33HhcEHP//8s3aRDxw4UMItR8jrQChNmjSyfft2nRXS3oEDB6Rs2bI6q+uRI0d0/g78P9guXbokderU0YnWcOnVq5d07tzZ479nIGR9oVbf7qaWtyJX092HWn2HOtZ3YLG+45oyZYp07NhRypUrJ1u2bBGJ9GRpzNKKIbTDhg1zuB/3mTO4nj9/XmJiYsQKMmTIICtXrpS0adPK9evXpVSpUvLMM89I1qxZg71pFKFc/fbAAWDmzJl6MAAcGFq2bOnQauRcxtNyiS0TrOnuichaGjVqpMeMrVu36ohMrD0aTrwOhEaMGCHPPfecLFy4ULPJAc3k6AabPXu23t6wYYMeVK0AB3MEQXD79m09CXHGAAoWtJKiVdKeObX8s88+63A/pp9HNzRGQbkr42m5xJYJt24mIvJetmzZ5PPPP9cWoWDN/uxXiUlAOnz4sPHWW28ZzZo100u/fv2MI0eOJCqZacWKFUbDhg2NXLlyaVLT3Llz45T5/PPPjfz58xupUqUyKlWqZPz1119evcbFixeNMmXKGGnSpNHnskKydDBHA4WbUEpufOmll3R/io2NNTZv3mwsX748wc8toTKelvNVmVCq73DA+g4s1ndgWSFZOlEzSyNx0rlrLLHQXYXcIvQ/osvKGZrv+/TpI+PGjZPKlSvL6NGjpV69ejqvgdmsjygV+T/OlixZIrlz59bFF7dt2yanT5/W18Av4Zw5c0qwoBsReUrmbLkvvviizo3iDPOjIPE8vnLelkHzJn75Y40XCqzvv/9eJk6cqJ8TZkJ++OGHE/wbtMh40irjSTlflSEikkifWRoJyOvXr9e8AueRL23btk38xkRF6aRNWPXWhOAHXXBolgO8HnKRXnnlFenXr5/Xr/Hyyy9roqqrLgaz+wwX+2QrvN7Fixe9SpbGRG+Y9M05+QujWxBIOtdbIKHb4/Dhw2Fzwouvvq1i//79uh9fu3ZN3nnnHXn33XclVIVCfYcT1ndgsb7dW7NmjXzzzTc6AMndOdRK9Y3zN/KVfZ4sjWn2sd4PDuh4YgQvJvw/KYGQszt37simTZukf//+tvtQUfgQMNutJ9AKhBwhJE2jMpA43b17d7flhw4dqkMEneGDunXrlscfLF7LbIGxh/wpV0EQhiJnyZLFdvvChQuaexVfucSWQQ4I7guXWcDjq28rwH6DvDp8Z6pWrap5OPgREaqsXt/hhvUdWKxv9zCE/quvvpKjR49K9erVxer1ffXqVY/KeR0Ivf7669qN9eGHH9qSkP3l3LlzetJ27sbCbU/nKMIHhkUizSRptCSVLl3abXkEXeiKc24RQrTqTYsQgkJXES5aBXCf8+icOXPmOLTQuGo5ci6X2DJmd6L96KNQFl99W0HPnj1l586dmnCI+bZy5coloczq9R1uWN+Bxfp2D40gw4cPlxUrVuj5P3369GLl+k6dOrVnBb1NPkqbNq1x6NAhwx+ck6X//fdfvW/NmjUO5fr27atJ04Hgj2TpYM68a1769OljhAsrJzfOmjXLVucLFy40woGV6zscsb4Di/XtHpbYKFy4sB7PZs+ebYRLsrTX4RcSlV11x/gDfkGjhQPdW/ZwOzY2VkIVEpUxFwMmq8O1u8RlT8p5WwZDomHkyJHy7bff+uHdkQl5WObngXy2p556KtibRESUaFFRUbYcXuTzhguvu8aefvpp6du3r+zevVu7mOyn+4fGjRv7bOOQw1K+fHldtsOsfDSj4Ta6G/xp7NixekHXnD8EazQQZgnGEglYNgEj1x566CGt41CG7j/kXqHbMb4lHzD7OWZET2jJh4TKeVIGQVD9+vW1a/XRRx+V9957L5HvjojIOpo1ayaffPKJLFiwQPN4wyHX1OtAyFyewtWBHdGit4EDEkixRpAJy3Ng9kok++Kkhnyddu3aaQJwpUqVdPg8htx36NBB/KlHjx56MafoDidIBkcd//LLL7pTo4UvVPOFMBwdycdmPzPeD/YVe3h/+PViTjHgqoyn5Twt8+OPPzqs0+P8g4GIKBRVqVJFzxcY8PHpp5/qeoQhPwLZCDJM3ua82jUu7dq1s5X57LPPjHz58hnR0dGaG7Ru3bqAbV+4rj6PSSaLFSum76169erGnTt3jFDrq546darLfcdqF+RnhdNElqGwf4cT1ndgsb4TVq1aNdvxLVmyZG7zXEMlRyhR8whFknBedHXPnj06TxOGGKKr8bPPPhOrQ91iCOf7778vmzdvdlkGXVLmyKyTJ0/qcjDxlfG0XFLKuFq8NFSFyv4dLljfgcX6jh9SA/Lnzx9ntDLyUBPTMhQyi66OGTNGh6BjKBr+H59XX31VwoG/c4SsAPlBSJhG1w0mrMRCeo888kiS82M8LedNmcKFC+skngiAtm/fro+lSZNG5+ixj+XxhcTM2fbTB7j60tqX8bRcUspw8VIiCgcHDhyIMx0LzpNIcQnZLjJPmpcKFChgnDt3zvZ/d5eCBQsa4SZcu8bsDR482KErx11TJ+7DYwk1h3pSztsy9pcMGTIY//vf/4yzZ8/6bIoBT8v58vVCVajt36GO9R1YrO/4oZvf+biclO5/do2FgHDuGjNhFBlaMZylS5fOtv14T0hSj6+Mp+WSUqZ3794yYMAAh1m4sf1IUEbScnyjxvCLBS0zCbVkJVTOV2VCVajt36GO9R1YrG/v18vEbNOJXb8yZLrGKLwdOnTI5f2uApHElPHlc2F6BvsgCBBoYAhnfCPfuHgpEZFvdOrUSXbt2iWjRo3SNcdCfRFvrwMh9AVOnTpV5/JxtejqsmXLfLl9FADI0XFe9gO3MY26fRJwjRo14i3jabnElmGuDRGRNVSvXl0DIfvpbyImEOrVq5cGQphYsVSpUg6LroaTSEiWNqHlAgm/mI8H7xcBB2agfvzxx21lkKycUBlPyyWlDFtZiIiCr1y5cnqNlqFQn1jR6xwhLHvxzTffSIMGDSQSREKOkD9yXwKZaxOq9R2qWN+BxfoOLNa3ZxA6xMTE6Llx27ZtUqZMGYmYHCFEfeyeCE++zH1hrg0RUfiKioqSsmXLysqVK3WlgsQGQlbgdfj1+uuv67TaHGxGREQUucr9v+4xBEKhzOsWoT///FNnycXsuSVLloyzhpL9GktEREQUvgnTR44c0ZahiAqEMmfOrAtNEhERUeRq3ry5XkKdV4HQf//9J7Vq1ZK6detKbGyshLNIGjVGREQUqbzKEUqRIoV069ZNbt++LeGuR48esnv3btmwYUOwN4WIiMiSDMOQEydO6MiviEmWrlSpkmzZssU/W0NEREQh4+WXX5Y8efLoPG8RkyOEN42RY5jfpXz58ro+lL1QHkJHRERE3q1MEOojx7wOhFq1aqXXr776qsN8Aubia8ypISIiiqwh9Nu2bZOICYQwVI6IiIio7P8bOo/FuzGTs6crMIR0IJQ/f36JBBw1RkREFL+sWbPq7P9Il9m+fXuc9SfDMhAyYUTVsWPHdLE1e40bN5ZwGTWGi7lWCREREbnuHkMghDyhiAiEDh8+rBMq7tixw5YbBOYq9GxBISIiiqxAaMGCBSGbJ+T18PlevXpJwYIFdc6AtGnTyq5du3TRtQoVKsgff/zhn60kIiIiS6pbt6707t07ZFed8LpFaO3atbJs2TLJli2bJEuWTC9oChs6dKiOJOMcQ0RERJGjWrVqeglVXrcIoesrQ4YM+n8EQ5hR0kyi3rdvn++3kIiIiMgqLUKlSpXSfkB0j1WuXFk+/vhjiY6OlgkTJkihQoX8s5VERERkWZcuXdLYIGfOnFK8eHEJ6xahAQMGyP379/X/7733ns4rhCaxX3/9VcaMGeOPbSQiIiILGzBggNSsWVMmTZokYd8iVK9ePdv/ixQpInv37pULFy5ITEyMbeRYOOA8QkRERN7NMB2KS2143SJkOnjwoCxevFhu3rwpWbJkkXDD1eeJiIi8X2rDnFYnbAOh8+fPS+3ataVYsWLSoEEDOXnypN7fqVMnXYyViIiIIkvJkiUlefLkcvbsWVtcELaBEOYKSJkypc4qjXmETC1btpRFixb5evuIiIjI4tKkSSMPPvhgSHaPeR0ILVmyRD766CNdW8Re0aJF5ejRo77cNiIiIgoRoZon5HUgdP36dYeWIBMSplOlSuWr7SIiIqIQUi5EAyGvR41hqPw333wj77//vt7GSDEMp8d8QrVq1fLHNhIREZHFNWjQQNKnTy9VqlSRsA6EEPAgWXrjxo268vybb76p642hRWj16tX+2UoiIiKyfMJ0yZIlJdQkS8zM0vv379f1xZo0aaJdZc8884yuMVa4cGH/bCURERGRFVqEIFOmTPL222873PfPP/9Ily5ddKkNIiIiijz79++XP//8UwdQhcpCrImeUNHV/EKhOLU2ERER+cbUqVN1XsFvv/1WQoXPAqFwg+U1SpQoIRUrVgz2phAREYWEciE4coyBkBtcYoOIiChxgdCOHTtCZq1OBkJERETkExg0lS5dOl2H9MCBAxJWydIYGRafS5cu+WJ7iIiIKEQlT55cSpcuLevWrdPuseLFi0vYtAhhpFh8l/z580vbtm39u7VERERkaeVCLE/I4xahKVOm+HdLiIiIKGwCoW3btknYziNERERE5ErDhg1l8eLFtoDI6hgIERERkc/kyZNHL6GCo8aIiIgoYjEQIiIiIp9au3at9O/fX2bPni1Wx0CIiIiIfGrFihUybNgw+eKLL3QtUitjIEREREQ+dfr0ab1evny5Tq9j5bVIGQgRERGRz6AFaMyYMbbb9+/fl65du1q2ZYiBEBEREfkMltZA8GMP644dPHhQrIiBEBEREflM0aJFJVmyZHGW3ihSpIhYEQMhN8aOHSslSpSQihUrBntTiIiIQsYDDzwgEyZMsN1GUDR+/Hi934oYCLnRo0cP2b17t2zYsCHYm0JERBRSOnXqJDVq1ND/Dx8+XG9bFWeWJiIiIp/DGqXoEsuZM6dYGQMhIiIi8rmCBQtKKGDXGBEREUUsBkJERETkczt27JA333xTRo8eLVbGQIiIiIh87u+//9ZE6e+//16sjIEQERER+Vz27Nn1+uzZs2JlDISIiIjI5xgIERERkUR6IHT9+nW5ceOGWBUDISIiIvK5DBkySHR0tOVbhRgIERERkc9FRUVJjhw59P8MhIiIiCjiZA+BPCHOLE1ERER+MXPmTEmVKpXExsaKVTEQIiIiIr8oWrSoWB27xoiIiChiMRAiIiIiv1i3bp0uszFp0iSxKgZCRERE5Lf1xrDMxty5c8WqGAgRERGRX3D4vIVgVsv8+fPLG2+8EexNISIiigjZQ2D4fMQEQkOGDJEqVaoEezOIiIgiRnYGQtZw4MAB2bt3r9SvXz/Ym0JERBRxgdC1a9fk1q1bYkVBD4RWrlwpjRo1kty5c+t03PPmzYtTZuzYsVKgQAFJnTq1VK5cWdavX+/Va6A7bOjQoT7caiIiIkpIpkyZJGXKlJZuFQp6IIRVacuWLavBjrtZKfv06SODBg2SzZs3a9l69erJmTNnbGXKlSsnpUqVinM5ceKE/PTTT1KsWDG9EBERUeBERUVZvnss6DNLo7sqvi6rkSNHSufOnaVDhw56e9y4cfLLL7/I5MmTpV+/fnrf1q1b453DYMaMGTJr1ixtmrt7965kzJhRBg4c6LL87du39WK6cuWKXt+/f18vnkA5wzA8Lk9Jw/oOLNZ3YLG+A4v17XtLliyR9OnTS65cueLUqz/r29PnDHogFJ87d+7Ipk2bpH///rb7kiVLJnXq1JG1a9d69BzoEjO7xaZOnSo7d+50GwSZ5QcPHhznfkSynvZvovIvX76sHy62l/yL9R1YrO/AYn0HFuvb97JmzarXFy5cCGh9X716NfQDoXPnzsm9e/ckZ86cDvfjNpKf/QFBF7ri7FuE8ubNq017aEnyBD5YszmQXyT/Y30HFus7sFjfgcX6Dp/6Rl5xyAdCvta+ffsEy2CVXFyc4QPy5kPCB+vt31Disb4Di/UdWKzvwGJ9+9bSpUtl0aJFUqlSJXnuuecCVt+ePp+lP+Vs2bJJ8uTJ5fTp0w7343ZsbGzQtouIiIg8g1SWESNGaDBkRZYOhKKjo6V8+fIaTdo3o+F21apV/fraGMVWokQJqVixol9fh4iIKJzlsPgyG0HvGsNIroMHD9puHzlyREeBZcmSRfLly6f5Ou3atZMKFSpos9ro0aN1yL05isxfevTooRfkCGEeBCIiIvIeh88nYOPGjVKrVi3bbTNRGcEPRnm1bNlSKw8jvU6dOqVzBqF5zTmBmoiIiKwnOwOh+NWsWVOHzcWnZ8+eeiEiIqLQDITO2E2EbCWWzhEKJuYIERER+S4Qwrw+9hMWWwUDITeQH7R7927ZsGFDsDeFiIgoZGXOnFlSpEhh2e6xoHeNERERUfhKliyZrhUaExOjy2xYDQMhIiIi8qvSpUuLVbFrjIiIiCIWAyE3mCxNRETkG/Pnz5c33nhDFi9eLFbDQMgNJksTERH5xm+//SaffPKJrFixQqyGgRARERFF7KSKDISIiIgoYtcbYyBEREREfsUWISIiIopY2S28zAYDITc4aoyIiMg32CIUgjhqjIiIyLc5QpcvX5Y7d+6IlXBmaSIiIvL7emNbtmzRlqGUKVOKlTAQIiIiIr+vN1auXDmxInaNERERUcRiIERERER+N2PGDHn99ddlzZo1YiUMhNzgqDEiIiLfmTt3rowcOVLWr18vVsJAyA2OGiMiIgr/IfQMhIiIiChil9lgIERERER+xxYhIiIiiljZGQgRERFRpMrOQIiIiIgiVQ6L5ghxZmkiIiLyu0KFCsnWrVttLUNWwUAonnmEcLl3716wN4WIiCjkpUqVSsqWLStWw64xNziPEBERUfhjIEREREQBMWnSJOnTp4/s3LlTrIKBEBEREQXEtGnTZNSoUbJjxw6xCgZCREREFLFD6BkIERERUcQOoWcgRERERAHBFiEiIiKKWNkZCBEREVGkB0JnzpwRq2AgRERERBHbIsSZpYmIiCggKlasqMts5MyZU6yCgZAbXGKDiIjIt9KnT2+5ZTbYNeYGl9ggIiIKfwyEiIiIKGDGjBkjvXv3lmPHjokVMBAiIiKigBk3bpyMHj1aDh06JFbAQIiIiIgidgg9AyEiIiKK2CH0DISIiIgoYtcbYyBEREREAcMWISIiIopY2RkIERERUaTKbrFkac4sTURERAHz5JNPyrZt2yQ2NlasgIEQERERBUyWLFn0Avfv35dgY9cYERERRSwGQkRERBRQQ4cOlddee00uXbokwcZAiIiIiAJqxIgR8umnn8q///4rwcZAyI2xY8dKiRIlpGLFisHeFCIiorCS3UJD6BkIudGjRw/ZvXu3bNiwIdibQkREFFayMxAiIiKiSJXDQstsMBAiIiKigGKLEBEREUWs7AyEiIiIKFJlt1AgxJmliYiIKKBatmwpNWvW1GU2gj27NAMhIiIiCqicOXPqBUFQsBdfZdcYERERRSwGQkRERBRQN2/elCFDhkjv3r3l3r17EkwMhIiIiCigUqRIIQMGDJAxY8YEfb0xBkJEREQUUClTppTMmTPr/8+fPy/BxECIiIiIgjaE/ty5cxJMDISIiIgoaIHQhQsXJJgYCBEREVHQ1htj1xgRERFFbIvQeQZCREREFGmyWyRHiDNLExERUcB1795dWrRoIdHR0RJMDISIiIgo4B544AHJnTt30JfYiIhAqECBApIxY0ZJliyZxMTEyPLly4O9SURERGQBEREIwZo1ayR9+vTB3gwiIiKS/xs2P3bsWM0RGjVqVNC2g8nSREREFHC3bt2SgQMHajCEVegjNhBauXKlNGrUSPsJo6KiZN68eXHKoJLQvZU6dWqpXLmyrF+/3qvXwPPWqFFDKlasKN99950Pt56IiIgSI1u2bHqNRVd3794tERsIXb9+XcqWLavBjiszZ86UPn36yKBBg2Tz5s1atl69eg7JVeXKlZNSpUrFuZw4cUIf//PPP2XTpk3y888/y4cffijbt28P2PsjIiKiuKZNm2b7/8MPPyyTJk2SYIgyDMMQi0DLzdy5c6Vp06a2+9AChJaczz//XG+j+Sxv3rzyyiuvSL9+/bx+jb59+0rJkiWlffv2Lh+/ffu2XkxXrlzR17t48aImXHsC23j27FmdIwEJ2uRfrO/AYn0HFus7sFjfgfHPP/9IwYIFHbrEkidPLocPH9bRZL6A8zcGSF2+fDne87elk6Xv3LmjLTn9+/e33Ycds06dOrJ27VqPW5xQ0RkyZJBr167JsmXLdN4Cd4YOHSqDBw+Ocz++GOjP9AReDxWPGJNfJP9jfQcW6zuwWN+BxfoOjA0bNsTJC0IX2caNG302r9DVq1c9KmfpQAiZ5KiYnDlzOtyP23v37vXoOU6fPi3NmjXT/+O5OnfurC1M7iDoQlecc4sQfh140yKE1i3+oggM1ndgsb4Di/UdWKzvwMB5GPXr3CJUoUIF2xpkSYW84pAPhHyhUKFCsm3bNo/Lp0qVSi/O8IF586XAF8nbv6HEY30HFus7sFjfgcX69r98+fLJhAkTpGvXrtpIgSBo/Pjxer+vePr5pbB6RjkqB6069nA7NjY2aNtFRERESdOpUyd58skntTsMLUG+DIK8YelwF/2E5cuXl6VLl9ruQzMabletWtWvr41RbCVKlIi3G42IiIgSD4nRjz76qM8SpBMj6C1CSGA+ePCg7faRI0dk69atkiVLFo0Oka/Trl07jRYrVaoko0eP1gToDh06+HW7evTooRfkCGXKlMmvr0VEREQRGgihSaxWrVq222aiMoKfqVOnSsuWLXXEFmafPHXqlM4ZtGjRojgJ1EREREQhFwjVrFlThynGp2fPnnohIiIiipgcoWBijhAREVH4YyDkBvKDsPYJJn0iIiKi8MRAiIiIiCIWAyEiIiKKWAyEiIiIKGIxEHKDydJEREThj4GQG0yWJiIiCn8MhIiIiChiBX1CRaszJ3vEUhuewnpoV69eldSpU3P14gBgfQcW6zuwWN+BxfoOn/o2z9sJTdrMQCgB+IAgb968wd4UIiIiSsR5PL41Q6OMhEKlCIdo9cSJE5IhQwaJioryOApF4HT8+HHJmDGj37cx0rG+A4v1HVis78BifYdPfSO8QRCUO3fueFub2CKUAFTeAw88kKi/xYfKL1LgsL4Di/UdWKzvwGJ9h0d9x9cSZGIHKBEREUUsBkJEREQUsRgI+UGqVKlk0KBBek3+x/oOLNZ3YLG+A4v1HXn1zWRpIiIiilhsESIiIqKIxUCIiIiIIhYDISIiIopYDISIiIgoYjEQ8oOxY8dKgQIFdO2UypUry/r164O9SWFh5cqV0qhRI50lFLN8z5s3z+Fx5P0PHDhQcuXKJWnSpJE6derIgQMHgra9oWzo0KFSsWJFnVE9R44c0rRpU9m3b59DmVu3bkmPHj0ka9askj59emnevLmcPn06aNscyr788kspU6aMbVK5qlWrysKFC22Ps679a9iwYXpMee2112z3sc59691339U6tr8UL17cEvXNQMjHZs6cKX369NHhgJs3b5ayZctKvXr15MyZM8HetJB3/fp1rU8Emq58/PHHMmbMGBk3bpz89ddfki5dOq17fMHIOytWrNCD0rp16+S3336Tu3fvSt26dfUzMPXu3Vvmz58vs2bN0vJYiuaZZ54J6naHKsxej5Pxpk2bZOPGjfLEE09IkyZNZNeuXfo469p/NmzYIOPHj9dA1B7r3PdKliwpJ0+etF3+/PNPa9Q3hs+T71SqVMno0aOH7fa9e/eM3LlzG0OHDg3qdoUb7Lpz58613b5//74RGxtrDB8+3HbfpUuXjFSpUhnTp08P0laGjzNnzmidr1ixwla3KVOmNGbNmmUrs2fPHi2zdu3aIG5p+IiJiTEmTpzIuvajq1evGkWLFjV+++03o0aNGkavXr30fta57w0aNMgoW7asy8eCXd9sEfKhO3fu6C86dMnYr1WG22vXrg3qtoW7I0eOyKlTpxzqHmvMoGuSdZ90ly9f1ussWbLoNfZztBLZ1zeaufPly8f6TqJ79+7JjBkztPUNXWSsa/9Bq+fTTz/tULfAOvcPpCogtaFQoULSpk0bOXbsmCXqm4uu+tC5c+f0IJYzZ06H+3F77969QduuSIAgCFzVvfkYJc79+/c1d+Kxxx6TUqVK6X2o0+joaMmcObNDWdZ34u3YsUMDH3TlIkdi7ty5UqJECdm6dSvr2g8QbCJ9AV1jzrh/+x5+lE6dOlUefPBB7RYbPHiwVKtWTXbu3Bn0+mYgREQJ/mrGwcq+P598DycIBD1ofZs9e7a0a9dOcyXI944fPy69evXS/DcMaiH/q1+/vu3/yMdCYJQ/f3754YcfdHBLMLFrzIeyZcsmyZMnj5PpjtuxsbFB265IYNYv6963evbsKQsWLJDly5drQq8JdYqu4EuXLjmUZ30nHn4RFylSRMqXL6+j9jAw4NNPP2Vd+wG6YjCA5ZFHHpEUKVLoBUEnBlvg/2iJYJ37F1p/ihUrJgcPHgz6Ps5AyMcHMhzEli5d6tCtgNto8ib/KViwoH5h7Ov+ypUrOnqMde895KMjCEL3zLJly7R+7WE/T5kypUN9Y3g9+vxZ376BY8ft27dZ135Qu3Zt7YpEC5x5qVChguatmP9nnfvXtWvX5NChQzrdSdD3cb+nY0eYGTNm6EilqVOnGrt37za6dOliZM6c2Th16lSwNy0sRnhs2bJFL9h1R44cqf8/evSoPj5s2DCt659++snYvn270aRJE6NgwYLGzZs3g73pIad79+5GpkyZjD/++MM4efKk7XLjxg1bmW7duhn58uUzli1bZmzcuNGoWrWqXsh7/fr10xF5R44c0X0Xt6OioowlS5bo46xr/7MfNQasc996/fXX9XiCfXz16tVGnTp1jGzZsumI1GDXNwMhP/jss8/0A42Ojtbh9OvWrQv2JoWF5cuXawDkfGnXrp1tCP0777xj5MyZU4PR2rVrG/v27Qv2ZockV/WMy5QpU2xlEGC+/PLLOsw7bdq0RrNmzTRYIu917NjRyJ8/vx4zsmfPrvuuGQQB6zrwgRDr3Ldatmxp5MqVS/fxPHny6O2DBw9aor6j8I//252IiIiIrIc5QkRERBSxGAgRERFRxGIgRERERBGLgRARERFFLAZCREREFLEYCBEREVHEYiBEREREEYuBEBFRAqKiomTevHnB3gwi8gMGQkRkae3bt9dAxPny1FNPBXvTiCgMpAj2BhARJQRBz5QpUxzuS5UqVdC2h4jCB1uEiMjyEPTExsY6XGJiYvQxtA59+eWXUr9+fUmTJo0UKlRIZs+e7fD3WGn8iSee0MezZs0qXbp00dWv7U2ePFlKliypr4UVsXv27Onw+Llz56RZs2aSNm1aKVq0qPz888+2xy5evKgrl2fPnl1fA487B25EZE0MhIgo5L3zzjvSvHlz2bZtmwYkrVq1kj179uhj169fl3r16mngtGHDBpk1a5b8/vvvDoEOAqkePXpogISgCUFOkSJFHF5j8ODB0qJFC9m+fbs0aNBAX+fChQu219+9e7csXLhQXxfPly1btgDXAhElSkCWdiUiSqR27doZyZMnN9KlS+dwGTJkiD6Ow1i3bt0c/qZy5cpG9+7d9f8TJkzQFa2vXbtme/yXX34xkiVLZpw6dUpv586d23j77bfdbgNeY8CAAbbbeC7ct3DhQr3dqFEjo0OHDj5+50QUCMwRIiLLq1Wrlray2MuSJYvt/1WrVnV4DLe3bt2q/0cLTdmyZSVdunS2xx977DG5f/++7Nu3T7vWTpw4IbVr1453G8qUKWP7P54rY8aMcubMGb3dvXt3bZHavHmz1K1bV5o2bSqPPvpoEt81EQUCAyEisjwEHs5dVb6CnB5PpEyZ0uE2AigEU4D8pKNHj8qvv/4qv/32mwZV6GobMWKEX7aZiHyHOUJEFPLWrVsX5/ZDDz2k/8c1coeQK2RavXq1JEuWTB588EHJkCGDFChQQJYuXZqkbUCidLt27eTbb7+V0aNHy4QJE5L0fEQUGGwRIiLLu337tpw6dcrhvhQpUtgSkpEAXaFCBXn88cflu+++k/Xr18ukSZP0MSQ1Dxo0SIOUd999V86ePSuvvPKKvPjii5IzZ04tg/u7desmOXLk0Nadq1evarCEcp4YOHCglC9fXkedYVsXLFhgC8SIyNoYCBGR5S1atEiHtNtDa87evXttI7pmzJghL7/8spabPn26lChRQh/DcPfFixdLr169pGLFinob+TwjR460PReCpFu3bsmoUaPkjTfe0ADr2Wef9Xj7oqOjpX///vL3339rV1u1atV0e4jI+qKQMR3sjSAiSizk6sydO1cTlImIvMUcISIiIopYDISIiIgoYjFHiIhCGnv3iSgp2CJEREREEYuBEBEREUUsBkJEREQUsRgIERERUcRiIEREREQRi4EQERERRSwGQkRERBSxGAgRERFRxGIgRERERBKp/j9hOE91LiroXQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot training and validation loss\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['train_losses'], 'b-',linestyle=\"dashed\", label='NARS: Train Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history['val_losses'], 'r-',linestyle=\"dashed\", label='NARS: Val Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['train_losses'], 'b', label='OneCycle: Train Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['val_losses'], 'r', label='OneCycle: Val Loss')\n",
    "plt.title('Loss vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "# Plot validation accuracy\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['val_accuracies'], 'g-', marker='o', label=\"NARS\")\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['val_accuracies'], 'g-',linestyle=\"dashed\", marker='o', label=\"OneCycle\")\n",
    "plt.title('Validation Accuracy vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "# Plot learning rate over time\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['lr_history'], 'k-', marker='.', label=\"NARS\")\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['lr_history'], 'k-',linestyle=\"dashed\", marker='.', label=\"OneCycle\")\n",
    "plt.title('Learning Rate vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Learning Rate')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2d32ac66",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(training_history1['val_accuracies'])):\n",
    "    training_history['val_accuracies'][i]=100*training_history['val_accuracies'][i]\n",
    "    training_history1['val_accuracies'][i]=100*training_history1['val_accuracies'][i]\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbd98458",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(max(training_history['train_losses']))\n",
    "print(max(training_history['val_losses']))\n",
    "\n",
    "print(max(training_history1['train_losses']))\n",
    "print(max(training_history1['val_losses']))\n",
    "\n",
    "print(max(training_history2['train_losses']))\n",
    "print(max(training_history2['val_losses']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fc20baa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/1755058589.py:3: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"b-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history['train_losses'], 'b-',linestyle=\"dashed\", label='NARS: Train Loss')\n",
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/1755058589.py:4: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"r-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history['val_losses'], 'r-',linestyle=\"dashed\", label='NARS: Val Loss')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/1755058589.py:16: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"g-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history1['val_accuracies'], 'g-',linestyle=\"dashed\", marker='o', label=\"OneCycle\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/1755058589.py:26: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"k-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history1['lr_history'], 'k-',linestyle=\"dashed\", marker='.', label=\"OneCycle\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot training and validation loss\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['train_losses'], 'b-',linestyle=\"dashed\", label='NARS: Train Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history['val_losses'], 'r-',linestyle=\"dashed\", label='NARS: Val Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['train_losses'], 'b', label='OneCycle: Train Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['val_losses'], 'r', label='OneCycle: Val Loss')\n",
    "plt.title('Loss vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "# Plot validation accuracy\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['val_accuracies'], 'g-', marker='o', label=\"NARS\")\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['val_accuracies'], 'g-',linestyle=\"dashed\", marker='o', label=\"OneCycle\")\n",
    "plt.title('Validation Accuracy vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "# Plot learning rate over time\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['lr_history'], 'k-', marker='.', label=\"NARS\")\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['lr_history'], 'k-',linestyle=\"dashed\", marker='.', label=\"OneCycle\")\n",
    "plt.title('Learning Rate vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Learning Rate')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8d8e5e25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: mps\n",
      "Starting training with fixed learning rate Adam for 50 epochs...\n",
      "Learning rate: 1e-05\n",
      "Epoch 1, Batch 50: Loss = 2.3061, LR = 0.000010\n",
      "Epoch 1, Batch 100: Loss = 2.1210, LR = 0.000010\n",
      "Epoch 1, Batch 150: Loss = 1.9956, LR = 0.000010\n",
      "Epoch 1, Batch 200: Loss = 1.8739, LR = 0.000010\n",
      "Epoch 1, Batch 250: Loss = 1.7797, LR = 0.000010\n",
      "Epoch 1, Batch 300: Loss = 1.7223, LR = 0.000010\n",
      "Epoch 1, Batch 350: Loss = 1.6638, LR = 0.000010\n",
      "Epoch 1/50 completed.\n",
      "  Train Loss: 1.6154\n",
      "  Val Loss: 1.4644\n",
      "  Val Accuracy: 0.5706\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 2, Batch 50: Loss = 1.5761, LR = 0.000010\n",
      "Epoch 2, Batch 100: Loss = 1.5527, LR = 0.000010\n",
      "Epoch 2, Batch 150: Loss = 1.5175, LR = 0.000010\n",
      "Epoch 2, Batch 200: Loss = 1.4785, LR = 0.000010\n",
      "Epoch 2, Batch 250: Loss = 1.4582, LR = 0.000010\n",
      "Epoch 2, Batch 300: Loss = 1.4317, LR = 0.000010\n",
      "Epoch 2, Batch 350: Loss = 1.4271, LR = 0.000010\n",
      "Epoch 2/50 completed.\n",
      "  Train Loss: 1.3965\n",
      "  Val Loss: 1.2427\n",
      "  Val Accuracy: 0.6519\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 3, Batch 50: Loss = 1.3802, LR = 0.000010\n",
      "Epoch 3, Batch 100: Loss = 1.3656, LR = 0.000010\n",
      "Epoch 3, Batch 150: Loss = 1.3609, LR = 0.000010\n",
      "Epoch 3, Batch 200: Loss = 1.3457, LR = 0.000010\n",
      "Epoch 3, Batch 250: Loss = 1.3179, LR = 0.000010\n",
      "Epoch 3, Batch 300: Loss = 1.3134, LR = 0.000010\n",
      "Epoch 3, Batch 350: Loss = 1.3037, LR = 0.000010\n",
      "Epoch 3/50 completed.\n",
      "  Train Loss: 1.2986\n",
      "  Val Loss: 1.1161\n",
      "  Val Accuracy: 0.6854\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 4, Batch 50: Loss = 1.2721, LR = 0.000010\n",
      "Epoch 4, Batch 100: Loss = 1.2731, LR = 0.000010\n",
      "Epoch 4, Batch 150: Loss = 1.2551, LR = 0.000010\n",
      "Epoch 4, Batch 200: Loss = 1.2339, LR = 0.000010\n",
      "Epoch 4, Batch 250: Loss = 1.2399, LR = 0.000010\n",
      "Epoch 4, Batch 300: Loss = 1.2462, LR = 0.000010\n",
      "Epoch 4, Batch 350: Loss = 1.2305, LR = 0.000010\n",
      "Epoch 4/50 completed.\n",
      "  Train Loss: 1.2112\n",
      "  Val Loss: 1.0324\n",
      "  Val Accuracy: 0.7014\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 5, Batch 50: Loss = 1.2111, LR = 0.000010\n",
      "Epoch 5, Batch 100: Loss = 1.1984, LR = 0.000010\n",
      "Epoch 5, Batch 150: Loss = 1.1842, LR = 0.000010\n",
      "Epoch 5, Batch 200: Loss = 1.1881, LR = 0.000010\n",
      "Epoch 5, Batch 250: Loss = 1.1729, LR = 0.000010\n",
      "Epoch 5, Batch 300: Loss = 1.1551, LR = 0.000010\n",
      "Epoch 5, Batch 350: Loss = 1.1564, LR = 0.000010\n",
      "Epoch 5/50 completed.\n",
      "  Train Loss: 1.1579\n",
      "  Val Loss: 0.9744\n",
      "  Val Accuracy: 0.7122\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 6, Batch 50: Loss = 1.1589, LR = 0.000010\n",
      "Epoch 6, Batch 100: Loss = 1.1405, LR = 0.000010\n",
      "Epoch 6, Batch 150: Loss = 1.1467, LR = 0.000010\n",
      "Epoch 6, Batch 200: Loss = 1.1127, LR = 0.000010\n",
      "Epoch 6, Batch 250: Loss = 1.1294, LR = 0.000010\n",
      "Epoch 6, Batch 300: Loss = 1.1161, LR = 0.000010\n",
      "Epoch 6, Batch 350: Loss = 1.1128, LR = 0.000010\n",
      "Epoch 6/50 completed.\n",
      "  Train Loss: 1.0911\n",
      "  Val Loss: 0.9314\n",
      "  Val Accuracy: 0.7193\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 7, Batch 50: Loss = 1.0993, LR = 0.000010\n",
      "Epoch 7, Batch 100: Loss = 1.0931, LR = 0.000010\n",
      "Epoch 7, Batch 150: Loss = 1.1190, LR = 0.000010\n",
      "Epoch 7, Batch 200: Loss = 1.1021, LR = 0.000010\n",
      "Epoch 7, Batch 250: Loss = 1.0791, LR = 0.000010\n",
      "Epoch 7, Batch 300: Loss = 1.0681, LR = 0.000010\n",
      "Epoch 7, Batch 350: Loss = 1.0762, LR = 0.000010\n",
      "Epoch 7/50 completed.\n",
      "  Train Loss: 1.0601\n",
      "  Val Loss: 0.8935\n",
      "  Val Accuracy: 0.7260\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 8, Batch 50: Loss = 1.0625, LR = 0.000010\n",
      "Epoch 8, Batch 100: Loss = 1.0623, LR = 0.000010\n",
      "Epoch 8, Batch 150: Loss = 1.0650, LR = 0.000010\n",
      "Epoch 8, Batch 200: Loss = 1.0532, LR = 0.000010\n",
      "Epoch 8, Batch 250: Loss = 1.0648, LR = 0.000010\n",
      "Epoch 8, Batch 300: Loss = 1.0409, LR = 0.000010\n",
      "Epoch 8, Batch 350: Loss = 1.0327, LR = 0.000010\n",
      "Epoch 8/50 completed.\n",
      "  Train Loss: 1.0346\n",
      "  Val Loss: 0.8614\n",
      "  Val Accuracy: 0.7290\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 9, Batch 50: Loss = 1.0183, LR = 0.000010\n",
      "Epoch 9, Batch 100: Loss = 1.0264, LR = 0.000010\n",
      "Epoch 9, Batch 150: Loss = 1.0200, LR = 0.000010\n",
      "Epoch 9, Batch 200: Loss = 1.0239, LR = 0.000010\n",
      "Epoch 9, Batch 250: Loss = 1.0289, LR = 0.000010\n",
      "Epoch 9, Batch 300: Loss = 1.0187, LR = 0.000010\n",
      "Epoch 9, Batch 350: Loss = 1.0138, LR = 0.000010\n",
      "Epoch 9/50 completed.\n",
      "  Train Loss: 1.0253\n",
      "  Val Loss: 0.8348\n",
      "  Val Accuracy: 0.7324\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 10, Batch 50: Loss = 1.0037, LR = 0.000010\n",
      "Epoch 10, Batch 100: Loss = 0.9993, LR = 0.000010\n",
      "Epoch 10, Batch 150: Loss = 1.0040, LR = 0.000010\n",
      "Epoch 10, Batch 200: Loss = 0.9977, LR = 0.000010\n",
      "Epoch 10, Batch 250: Loss = 0.9872, LR = 0.000010\n",
      "Epoch 10, Batch 300: Loss = 0.9941, LR = 0.000010\n",
      "Epoch 10, Batch 350: Loss = 0.9842, LR = 0.000010\n",
      "Epoch 10/50 completed.\n",
      "  Train Loss: 0.9893\n",
      "  Val Loss: 0.8151\n",
      "  Val Accuracy: 0.7351\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 11, Batch 50: Loss = 0.9707, LR = 0.000010\n",
      "Epoch 11, Batch 100: Loss = 0.9791, LR = 0.000010\n",
      "Epoch 11, Batch 150: Loss = 0.9790, LR = 0.000010\n",
      "Epoch 11, Batch 200: Loss = 0.9884, LR = 0.000010\n",
      "Epoch 11, Batch 250: Loss = 0.9751, LR = 0.000010\n",
      "Epoch 11, Batch 300: Loss = 0.9733, LR = 0.000010\n",
      "Epoch 11, Batch 350: Loss = 0.9563, LR = 0.000010\n",
      "Epoch 11/50 completed.\n",
      "  Train Loss: 0.9664\n",
      "  Val Loss: 0.7979\n",
      "  Val Accuracy: 0.7395\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 12, Batch 50: Loss = 0.9611, LR = 0.000010\n",
      "Epoch 12, Batch 100: Loss = 0.9549, LR = 0.000010\n",
      "Epoch 12, Batch 150: Loss = 0.9601, LR = 0.000010\n",
      "Epoch 12, Batch 200: Loss = 0.9464, LR = 0.000010\n",
      "Epoch 12, Batch 250: Loss = 0.9478, LR = 0.000010\n",
      "Epoch 12, Batch 300: Loss = 0.9401, LR = 0.000010\n",
      "Epoch 12, Batch 350: Loss = 0.9452, LR = 0.000010\n",
      "Epoch 12/50 completed.\n",
      "  Train Loss: 0.9317\n",
      "  Val Loss: 0.7813\n",
      "  Val Accuracy: 0.7410\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 13, Batch 50: Loss = 0.9475, LR = 0.000010\n",
      "Epoch 13, Batch 100: Loss = 0.9382, LR = 0.000010\n",
      "Epoch 13, Batch 150: Loss = 0.9327, LR = 0.000010\n",
      "Epoch 13, Batch 200: Loss = 0.9281, LR = 0.000010\n",
      "Epoch 13, Batch 250: Loss = 0.9219, LR = 0.000010\n",
      "Epoch 13, Batch 300: Loss = 0.9513, LR = 0.000010\n",
      "Epoch 13, Batch 350: Loss = 0.9379, LR = 0.000010\n",
      "Epoch 13/50 completed.\n",
      "  Train Loss: 0.9243\n",
      "  Val Loss: 0.7666\n",
      "  Val Accuracy: 0.7437\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 14, Batch 50: Loss = 0.9344, LR = 0.000010\n",
      "Epoch 14, Batch 100: Loss = 0.9162, LR = 0.000010\n",
      "Epoch 14, Batch 150: Loss = 0.9462, LR = 0.000010\n",
      "Epoch 14, Batch 200: Loss = 0.9239, LR = 0.000010\n",
      "Epoch 14, Batch 250: Loss = 0.8939, LR = 0.000010\n",
      "Epoch 14, Batch 300: Loss = 0.9244, LR = 0.000010\n",
      "Epoch 14, Batch 350: Loss = 0.8898, LR = 0.000010\n",
      "Epoch 14/50 completed.\n",
      "  Train Loss: 0.8998\n",
      "  Val Loss: 0.7562\n",
      "  Val Accuracy: 0.7451\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 15, Batch 50: Loss = 0.9124, LR = 0.000010\n",
      "Epoch 15, Batch 100: Loss = 0.8941, LR = 0.000010\n",
      "Epoch 15, Batch 150: Loss = 0.9036, LR = 0.000010\n",
      "Epoch 15, Batch 200: Loss = 0.8868, LR = 0.000010\n",
      "Epoch 15, Batch 250: Loss = 0.9115, LR = 0.000010\n",
      "Epoch 15, Batch 300: Loss = 0.9150, LR = 0.000010\n",
      "Epoch 15, Batch 350: Loss = 0.9028, LR = 0.000010\n",
      "Epoch 15/50 completed.\n",
      "  Train Loss: 0.9037\n",
      "  Val Loss: 0.7430\n",
      "  Val Accuracy: 0.7472\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 16, Batch 50: Loss = 0.8777, LR = 0.000010\n",
      "Epoch 16, Batch 100: Loss = 0.8896, LR = 0.000010\n",
      "Epoch 16, Batch 150: Loss = 0.8863, LR = 0.000010\n",
      "Epoch 16, Batch 200: Loss = 0.8931, LR = 0.000010\n",
      "Epoch 16, Batch 250: Loss = 0.8948, LR = 0.000010\n",
      "Epoch 16, Batch 300: Loss = 0.8923, LR = 0.000010\n",
      "Epoch 16, Batch 350: Loss = 0.8914, LR = 0.000010\n",
      "Epoch 16/50 completed.\n",
      "  Train Loss: 0.8871\n",
      "  Val Loss: 0.7338\n",
      "  Val Accuracy: 0.7500\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 17, Batch 50: Loss = 0.8679, LR = 0.000010\n",
      "Epoch 17, Batch 100: Loss = 0.8779, LR = 0.000010\n",
      "Epoch 17, Batch 150: Loss = 0.8863, LR = 0.000010\n",
      "Epoch 17, Batch 200: Loss = 0.8756, LR = 0.000010\n",
      "Epoch 17, Batch 250: Loss = 0.8841, LR = 0.000010\n",
      "Epoch 17, Batch 300: Loss = 0.8811, LR = 0.000010\n",
      "Epoch 17, Batch 350: Loss = 0.8687, LR = 0.000010\n",
      "Epoch 17/50 completed.\n",
      "  Train Loss: 0.8593\n",
      "  Val Loss: 0.7246\n",
      "  Val Accuracy: 0.7507\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 18, Batch 50: Loss = 0.8633, LR = 0.000010\n",
      "Epoch 18, Batch 100: Loss = 0.8665, LR = 0.000010\n",
      "Epoch 18, Batch 150: Loss = 0.8696, LR = 0.000010\n",
      "Epoch 18, Batch 200: Loss = 0.8462, LR = 0.000010\n",
      "Epoch 18, Batch 250: Loss = 0.8555, LR = 0.000010\n",
      "Epoch 18, Batch 300: Loss = 0.8703, LR = 0.000010\n",
      "Epoch 18, Batch 350: Loss = 0.8636, LR = 0.000010\n",
      "Epoch 18/50 completed.\n",
      "  Train Loss: 0.8566\n",
      "  Val Loss: 0.7151\n",
      "  Val Accuracy: 0.7506\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 19, Batch 50: Loss = 0.8727, LR = 0.000010\n",
      "Epoch 19, Batch 100: Loss = 0.8629, LR = 0.000010\n",
      "Epoch 19, Batch 150: Loss = 0.8599, LR = 0.000010\n",
      "Epoch 19, Batch 200: Loss = 0.8566, LR = 0.000010\n",
      "Epoch 19, Batch 250: Loss = 0.8514, LR = 0.000010\n",
      "Epoch 19, Batch 300: Loss = 0.8385, LR = 0.000010\n",
      "Epoch 19, Batch 350: Loss = 0.8341, LR = 0.000010\n",
      "Epoch 19/50 completed.\n",
      "  Train Loss: 0.8339\n",
      "  Val Loss: 0.7090\n",
      "  Val Accuracy: 0.7533\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 20, Batch 50: Loss = 0.8488, LR = 0.000010\n",
      "Epoch 20, Batch 100: Loss = 0.8625, LR = 0.000010\n",
      "Epoch 20, Batch 150: Loss = 0.8493, LR = 0.000010\n",
      "Epoch 20, Batch 200: Loss = 0.8485, LR = 0.000010\n",
      "Epoch 20, Batch 250: Loss = 0.8381, LR = 0.000010\n",
      "Epoch 20, Batch 300: Loss = 0.8221, LR = 0.000010\n",
      "Epoch 20, Batch 350: Loss = 0.8514, LR = 0.000010\n",
      "Epoch 20/50 completed.\n",
      "  Train Loss: 0.8342\n",
      "  Val Loss: 0.7007\n",
      "  Val Accuracy: 0.7555\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 21, Batch 50: Loss = 0.8596, LR = 0.000010\n",
      "Epoch 21, Batch 100: Loss = 0.8320, LR = 0.000010\n",
      "Epoch 21, Batch 150: Loss = 0.8445, LR = 0.000010\n",
      "Epoch 21, Batch 200: Loss = 0.8319, LR = 0.000010\n",
      "Epoch 21, Batch 250: Loss = 0.8162, LR = 0.000010\n",
      "Epoch 21, Batch 300: Loss = 0.8303, LR = 0.000010\n",
      "Epoch 21, Batch 350: Loss = 0.8246, LR = 0.000010\n",
      "Epoch 21/50 completed.\n",
      "  Train Loss: 0.8343\n",
      "  Val Loss: 0.6942\n",
      "  Val Accuracy: 0.7579\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 22, Batch 50: Loss = 0.8365, LR = 0.000010\n",
      "Epoch 22, Batch 100: Loss = 0.8350, LR = 0.000010\n",
      "Epoch 22, Batch 150: Loss = 0.8312, LR = 0.000010\n",
      "Epoch 22, Batch 200: Loss = 0.8289, LR = 0.000010\n",
      "Epoch 22, Batch 250: Loss = 0.8299, LR = 0.000010\n",
      "Epoch 22, Batch 300: Loss = 0.8176, LR = 0.000010\n",
      "Epoch 22, Batch 350: Loss = 0.8214, LR = 0.000010\n",
      "Epoch 22/50 completed.\n",
      "  Train Loss: 0.8189\n",
      "  Val Loss: 0.6864\n",
      "  Val Accuracy: 0.7595\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 23, Batch 50: Loss = 0.8182, LR = 0.000010\n",
      "Epoch 23, Batch 100: Loss = 0.8363, LR = 0.000010\n",
      "Epoch 23, Batch 150: Loss = 0.8213, LR = 0.000010\n",
      "Epoch 23, Batch 200: Loss = 0.8168, LR = 0.000010\n",
      "Epoch 23, Batch 250: Loss = 0.8265, LR = 0.000010\n",
      "Epoch 23, Batch 300: Loss = 0.8175, LR = 0.000010\n",
      "Epoch 23, Batch 350: Loss = 0.8174, LR = 0.000010\n",
      "Epoch 23/50 completed.\n",
      "  Train Loss: 0.8226\n",
      "  Val Loss: 0.6784\n",
      "  Val Accuracy: 0.7608\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 24, Batch 50: Loss = 0.8059, LR = 0.000010\n",
      "Epoch 24, Batch 100: Loss = 0.8160, LR = 0.000010\n",
      "Epoch 24, Batch 150: Loss = 0.7981, LR = 0.000010\n",
      "Epoch 24, Batch 200: Loss = 0.8241, LR = 0.000010\n",
      "Epoch 24, Batch 250: Loss = 0.8175, LR = 0.000010\n",
      "Epoch 24, Batch 300: Loss = 0.8157, LR = 0.000010\n",
      "Epoch 24, Batch 350: Loss = 0.8071, LR = 0.000010\n",
      "Epoch 24/50 completed.\n",
      "  Train Loss: 0.8155\n",
      "  Val Loss: 0.6747\n",
      "  Val Accuracy: 0.7618\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 25, Batch 50: Loss = 0.8230, LR = 0.000010\n",
      "Epoch 25, Batch 100: Loss = 0.8082, LR = 0.000010\n",
      "Epoch 25, Batch 150: Loss = 0.8017, LR = 0.000010\n",
      "Epoch 25, Batch 200: Loss = 0.8233, LR = 0.000010\n",
      "Epoch 25, Batch 250: Loss = 0.8105, LR = 0.000010\n",
      "Epoch 25, Batch 300: Loss = 0.8049, LR = 0.000010\n",
      "Epoch 25, Batch 350: Loss = 0.8052, LR = 0.000010\n",
      "Epoch 25/50 completed.\n",
      "  Train Loss: 0.7974\n",
      "  Val Loss: 0.6687\n",
      "  Val Accuracy: 0.7623\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 26, Batch 50: Loss = 0.8200, LR = 0.000010\n",
      "Epoch 26, Batch 100: Loss = 0.8223, LR = 0.000010\n",
      "Epoch 26, Batch 150: Loss = 0.7935, LR = 0.000010\n",
      "Epoch 26, Batch 200: Loss = 0.7880, LR = 0.000010\n",
      "Epoch 26, Batch 250: Loss = 0.7986, LR = 0.000010\n",
      "Epoch 26, Batch 300: Loss = 0.7928, LR = 0.000010\n",
      "Epoch 26, Batch 350: Loss = 0.7879, LR = 0.000010\n",
      "Epoch 26/50 completed.\n",
      "  Train Loss: 0.8049\n",
      "  Val Loss: 0.6661\n",
      "  Val Accuracy: 0.7642\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 27, Batch 50: Loss = 0.7936, LR = 0.000010\n",
      "Epoch 27, Batch 100: Loss = 0.7885, LR = 0.000010\n",
      "Epoch 27, Batch 150: Loss = 0.7717, LR = 0.000010\n",
      "Epoch 27, Batch 200: Loss = 0.8234, LR = 0.000010\n",
      "Epoch 27, Batch 250: Loss = 0.7698, LR = 0.000010\n",
      "Epoch 27, Batch 300: Loss = 0.7978, LR = 0.000010\n",
      "Epoch 27, Batch 350: Loss = 0.7865, LR = 0.000010\n",
      "Epoch 27/50 completed.\n",
      "  Train Loss: 0.8046\n",
      "  Val Loss: 0.6631\n",
      "  Val Accuracy: 0.7648\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 28, Batch 50: Loss = 0.7807, LR = 0.000010\n",
      "Epoch 28, Batch 100: Loss = 0.7965, LR = 0.000010\n",
      "Epoch 28, Batch 150: Loss = 0.7969, LR = 0.000010\n",
      "Epoch 28, Batch 200: Loss = 0.8009, LR = 0.000010\n",
      "Epoch 28, Batch 250: Loss = 0.7913, LR = 0.000010\n",
      "Epoch 28, Batch 300: Loss = 0.7797, LR = 0.000010\n",
      "Epoch 28, Batch 350: Loss = 0.7861, LR = 0.000010\n",
      "Epoch 28/50 completed.\n",
      "  Train Loss: 0.7786\n",
      "  Val Loss: 0.6556\n",
      "  Val Accuracy: 0.7654\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 29, Batch 50: Loss = 0.7911, LR = 0.000010\n",
      "Epoch 29, Batch 100: Loss = 0.7926, LR = 0.000010\n",
      "Epoch 29, Batch 150: Loss = 0.7884, LR = 0.000010\n",
      "Epoch 29, Batch 200: Loss = 0.7833, LR = 0.000010\n",
      "Epoch 29, Batch 250: Loss = 0.7852, LR = 0.000010\n",
      "Epoch 29, Batch 300: Loss = 0.7949, LR = 0.000010\n",
      "Epoch 29, Batch 350: Loss = 0.7676, LR = 0.000010\n",
      "Epoch 29/50 completed.\n",
      "  Train Loss: 0.7888\n",
      "  Val Loss: 0.6526\n",
      "  Val Accuracy: 0.7668\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 30, Batch 50: Loss = 0.7761, LR = 0.000010\n",
      "Epoch 30, Batch 100: Loss = 0.7843, LR = 0.000010\n",
      "Epoch 30, Batch 150: Loss = 0.7744, LR = 0.000010\n",
      "Epoch 30, Batch 200: Loss = 0.7789, LR = 0.000010\n",
      "Epoch 30, Batch 250: Loss = 0.7918, LR = 0.000010\n",
      "Epoch 30, Batch 300: Loss = 0.7756, LR = 0.000010\n",
      "Epoch 30, Batch 350: Loss = 0.7858, LR = 0.000010\n",
      "Epoch 30/50 completed.\n",
      "  Train Loss: 0.7596\n",
      "  Val Loss: 0.6475\n",
      "  Val Accuracy: 0.7684\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 31, Batch 50: Loss = 0.7689, LR = 0.000010\n",
      "Epoch 31, Batch 100: Loss = 0.7845, LR = 0.000010\n",
      "Epoch 31, Batch 150: Loss = 0.7683, LR = 0.000010\n",
      "Epoch 31, Batch 200: Loss = 0.7645, LR = 0.000010\n",
      "Epoch 31, Batch 250: Loss = 0.7947, LR = 0.000010\n",
      "Epoch 31, Batch 300: Loss = 0.7675, LR = 0.000010\n",
      "Epoch 31, Batch 350: Loss = 0.7654, LR = 0.000010\n",
      "Epoch 31/50 completed.\n",
      "  Train Loss: 0.7600\n",
      "  Val Loss: 0.6448\n",
      "  Val Accuracy: 0.7675\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 32, Batch 50: Loss = 0.7794, LR = 0.000010\n",
      "Epoch 32, Batch 100: Loss = 0.7585, LR = 0.000010\n",
      "Epoch 32, Batch 150: Loss = 0.7641, LR = 0.000010\n",
      "Epoch 32, Batch 200: Loss = 0.7672, LR = 0.000010\n",
      "Epoch 32, Batch 250: Loss = 0.7599, LR = 0.000010\n",
      "Epoch 32, Batch 300: Loss = 0.7699, LR = 0.000010\n",
      "Epoch 32, Batch 350: Loss = 0.7775, LR = 0.000010\n",
      "Epoch 32/50 completed.\n",
      "  Train Loss: 0.7839\n",
      "  Val Loss: 0.6398\n",
      "  Val Accuracy: 0.7696\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 33, Batch 50: Loss = 0.7496, LR = 0.000010\n",
      "Epoch 33, Batch 100: Loss = 0.7750, LR = 0.000010\n",
      "Epoch 33, Batch 150: Loss = 0.7693, LR = 0.000010\n",
      "Epoch 33, Batch 200: Loss = 0.7525, LR = 0.000010\n",
      "Epoch 33, Batch 250: Loss = 0.7767, LR = 0.000010\n",
      "Epoch 33, Batch 300: Loss = 0.7734, LR = 0.000010\n",
      "Epoch 33, Batch 350: Loss = 0.7510, LR = 0.000010\n",
      "Epoch 33/50 completed.\n",
      "  Train Loss: 0.7651\n",
      "  Val Loss: 0.6363\n",
      "  Val Accuracy: 0.7714\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 34, Batch 50: Loss = 0.7636, LR = 0.000010\n",
      "Epoch 34, Batch 100: Loss = 0.7516, LR = 0.000010\n",
      "Epoch 34, Batch 150: Loss = 0.7526, LR = 0.000010\n",
      "Epoch 34, Batch 200: Loss = 0.7520, LR = 0.000010\n",
      "Epoch 34, Batch 250: Loss = 0.7639, LR = 0.000010\n",
      "Epoch 34, Batch 300: Loss = 0.7417, LR = 0.000010\n",
      "Epoch 34, Batch 350: Loss = 0.7617, LR = 0.000010\n",
      "Epoch 34/50 completed.\n",
      "  Train Loss: 0.7738\n",
      "  Val Loss: 0.6340\n",
      "  Val Accuracy: 0.7705\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 35, Batch 50: Loss = 0.7543, LR = 0.000010\n",
      "Epoch 35, Batch 100: Loss = 0.7357, LR = 0.000010\n",
      "Epoch 35, Batch 150: Loss = 0.7711, LR = 0.000010\n",
      "Epoch 35, Batch 200: Loss = 0.7344, LR = 0.000010\n",
      "Epoch 35, Batch 250: Loss = 0.7624, LR = 0.000010\n",
      "Epoch 35, Batch 300: Loss = 0.7691, LR = 0.000010\n",
      "Epoch 35, Batch 350: Loss = 0.7636, LR = 0.000010\n",
      "Epoch 35/50 completed.\n",
      "  Train Loss: 0.7667\n",
      "  Val Loss: 0.6288\n",
      "  Val Accuracy: 0.7731\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 36, Batch 50: Loss = 0.7543, LR = 0.000010\n",
      "Epoch 36, Batch 100: Loss = 0.7597, LR = 0.000010\n",
      "Epoch 36, Batch 150: Loss = 0.7584, LR = 0.000010\n",
      "Epoch 36, Batch 200: Loss = 0.7283, LR = 0.000010\n",
      "Epoch 36, Batch 250: Loss = 0.7513, LR = 0.000010\n",
      "Epoch 36, Batch 300: Loss = 0.7443, LR = 0.000010\n",
      "Epoch 36, Batch 350: Loss = 0.7577, LR = 0.000010\n",
      "Epoch 36/50 completed.\n",
      "  Train Loss: 0.7498\n",
      "  Val Loss: 0.6270\n",
      "  Val Accuracy: 0.7732\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 37, Batch 50: Loss = 0.7539, LR = 0.000010\n",
      "Epoch 37, Batch 100: Loss = 0.7538, LR = 0.000010\n",
      "Epoch 37, Batch 150: Loss = 0.7389, LR = 0.000010\n",
      "Epoch 37, Batch 200: Loss = 0.7477, LR = 0.000010\n",
      "Epoch 37, Batch 250: Loss = 0.7500, LR = 0.000010\n",
      "Epoch 37, Batch 300: Loss = 0.7378, LR = 0.000010\n",
      "Epoch 37, Batch 350: Loss = 0.7512, LR = 0.000010\n",
      "Epoch 37/50 completed.\n",
      "  Train Loss: 0.7413\n",
      "  Val Loss: 0.6224\n",
      "  Val Accuracy: 0.7754\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 38, Batch 50: Loss = 0.7463, LR = 0.000010\n",
      "Epoch 38, Batch 100: Loss = 0.7381, LR = 0.000010\n",
      "Epoch 38, Batch 150: Loss = 0.7523, LR = 0.000010\n",
      "Epoch 38, Batch 200: Loss = 0.7325, LR = 0.000010\n",
      "Epoch 38, Batch 250: Loss = 0.7598, LR = 0.000010\n",
      "Epoch 38, Batch 300: Loss = 0.7482, LR = 0.000010\n",
      "Epoch 38, Batch 350: Loss = 0.7393, LR = 0.000010\n",
      "Epoch 38/50 completed.\n",
      "  Train Loss: 0.7467\n",
      "  Val Loss: 0.6194\n",
      "  Val Accuracy: 0.7744\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 39, Batch 50: Loss = 0.7295, LR = 0.000010\n",
      "Epoch 39, Batch 100: Loss = 0.7539, LR = 0.000010\n",
      "Epoch 39, Batch 150: Loss = 0.7447, LR = 0.000010\n",
      "Epoch 39, Batch 200: Loss = 0.7377, LR = 0.000010\n",
      "Epoch 39, Batch 250: Loss = 0.7503, LR = 0.000010\n",
      "Epoch 39, Batch 300: Loss = 0.7484, LR = 0.000010\n",
      "Epoch 39, Batch 350: Loss = 0.7374, LR = 0.000010\n",
      "Epoch 39/50 completed.\n",
      "  Train Loss: 0.7422\n",
      "  Val Loss: 0.6166\n",
      "  Val Accuracy: 0.7764\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 40, Batch 50: Loss = 0.7628, LR = 0.000010\n",
      "Epoch 40, Batch 100: Loss = 0.7339, LR = 0.000010\n",
      "Epoch 40, Batch 150: Loss = 0.7312, LR = 0.000010\n",
      "Epoch 40, Batch 200: Loss = 0.7324, LR = 0.000010\n",
      "Epoch 40, Batch 250: Loss = 0.7366, LR = 0.000010\n",
      "Epoch 40, Batch 300: Loss = 0.7430, LR = 0.000010\n",
      "Epoch 40, Batch 350: Loss = 0.7365, LR = 0.000010\n",
      "Epoch 40/50 completed.\n",
      "  Train Loss: 0.7220\n",
      "  Val Loss: 0.6131\n",
      "  Val Accuracy: 0.7774\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 41, Batch 50: Loss = 0.7296, LR = 0.000010\n",
      "Epoch 41, Batch 100: Loss = 0.7361, LR = 0.000010\n",
      "Epoch 41, Batch 150: Loss = 0.7413, LR = 0.000010\n",
      "Epoch 41, Batch 200: Loss = 0.7319, LR = 0.000010\n",
      "Epoch 41, Batch 250: Loss = 0.7297, LR = 0.000010\n",
      "Epoch 41, Batch 300: Loss = 0.7442, LR = 0.000010\n",
      "Epoch 41, Batch 350: Loss = 0.7293, LR = 0.000010\n",
      "Epoch 41/50 completed.\n",
      "  Train Loss: 0.7353\n",
      "  Val Loss: 0.6122\n",
      "  Val Accuracy: 0.7782\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 42, Batch 50: Loss = 0.7212, LR = 0.000010\n",
      "Epoch 42, Batch 100: Loss = 0.7554, LR = 0.000010\n",
      "Epoch 42, Batch 150: Loss = 0.7304, LR = 0.000010\n",
      "Epoch 42, Batch 200: Loss = 0.7323, LR = 0.000010\n",
      "Epoch 42, Batch 250: Loss = 0.7192, LR = 0.000010\n",
      "Epoch 42, Batch 300: Loss = 0.7293, LR = 0.000010\n",
      "Epoch 42, Batch 350: Loss = 0.7353, LR = 0.000010\n",
      "Epoch 42/50 completed.\n",
      "  Train Loss: 0.7276\n",
      "  Val Loss: 0.6098\n",
      "  Val Accuracy: 0.7778\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 43, Batch 50: Loss = 0.7344, LR = 0.000010\n",
      "Epoch 43, Batch 100: Loss = 0.7287, LR = 0.000010\n",
      "Epoch 43, Batch 150: Loss = 0.7182, LR = 0.000010\n",
      "Epoch 43, Batch 200: Loss = 0.7377, LR = 0.000010\n",
      "Epoch 43, Batch 250: Loss = 0.7276, LR = 0.000010\n",
      "Epoch 43, Batch 300: Loss = 0.7271, LR = 0.000010\n",
      "Epoch 43, Batch 350: Loss = 0.7388, LR = 0.000010\n",
      "Epoch 43/50 completed.\n",
      "  Train Loss: 0.7079\n",
      "  Val Loss: 0.6055\n",
      "  Val Accuracy: 0.7800\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 44, Batch 50: Loss = 0.7353, LR = 0.000010\n",
      "Epoch 44, Batch 100: Loss = 0.7316, LR = 0.000010\n",
      "Epoch 44, Batch 150: Loss = 0.7159, LR = 0.000010\n",
      "Epoch 44, Batch 200: Loss = 0.7167, LR = 0.000010\n",
      "Epoch 44, Batch 250: Loss = 0.7349, LR = 0.000010\n",
      "Epoch 44, Batch 300: Loss = 0.7212, LR = 0.000010\n",
      "Epoch 44, Batch 350: Loss = 0.7229, LR = 0.000010\n",
      "Epoch 44/50 completed.\n",
      "  Train Loss: 0.7129\n",
      "  Val Loss: 0.6033\n",
      "  Val Accuracy: 0.7800\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 45, Batch 50: Loss = 0.7077, LR = 0.000010\n",
      "Epoch 45, Batch 100: Loss = 0.7423, LR = 0.000010\n",
      "Epoch 45, Batch 150: Loss = 0.7145, LR = 0.000010\n",
      "Epoch 45, Batch 200: Loss = 0.7367, LR = 0.000010\n",
      "Epoch 45, Batch 250: Loss = 0.7227, LR = 0.000010\n",
      "Epoch 45, Batch 300: Loss = 0.7058, LR = 0.000010\n",
      "Epoch 45, Batch 350: Loss = 0.7142, LR = 0.000010\n",
      "Epoch 45/50 completed.\n",
      "  Train Loss: 0.7391\n",
      "  Val Loss: 0.6017\n",
      "  Val Accuracy: 0.7809\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 46, Batch 50: Loss = 0.7201, LR = 0.000010\n",
      "Epoch 46, Batch 100: Loss = 0.7209, LR = 0.000010\n",
      "Epoch 46, Batch 150: Loss = 0.7067, LR = 0.000010\n",
      "Epoch 46, Batch 200: Loss = 0.7316, LR = 0.000010\n",
      "Epoch 46, Batch 250: Loss = 0.7173, LR = 0.000010\n",
      "Epoch 46, Batch 300: Loss = 0.7164, LR = 0.000010\n",
      "Epoch 46, Batch 350: Loss = 0.7180, LR = 0.000010\n",
      "Epoch 46/50 completed.\n",
      "  Train Loss: 0.7288\n",
      "  Val Loss: 0.5995\n",
      "  Val Accuracy: 0.7814\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 47, Batch 50: Loss = 0.7296, LR = 0.000010\n",
      "Epoch 47, Batch 100: Loss = 0.7072, LR = 0.000010\n",
      "Epoch 47, Batch 150: Loss = 0.7011, LR = 0.000010\n",
      "Epoch 47, Batch 200: Loss = 0.7176, LR = 0.000010\n",
      "Epoch 47, Batch 250: Loss = 0.7394, LR = 0.000010\n",
      "Epoch 47, Batch 300: Loss = 0.7182, LR = 0.000010\n",
      "Epoch 47, Batch 350: Loss = 0.7279, LR = 0.000010\n",
      "Epoch 47/50 completed.\n",
      "  Train Loss: 0.7071\n",
      "  Val Loss: 0.5959\n",
      "  Val Accuracy: 0.7835\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 48, Batch 50: Loss = 0.7092, LR = 0.000010\n",
      "Epoch 48, Batch 100: Loss = 0.7180, LR = 0.000010\n",
      "Epoch 48, Batch 150: Loss = 0.7218, LR = 0.000010\n",
      "Epoch 48, Batch 200: Loss = 0.7229, LR = 0.000010\n",
      "Epoch 48, Batch 250: Loss = 0.7171, LR = 0.000010\n",
      "Epoch 48, Batch 300: Loss = 0.7155, LR = 0.000010\n",
      "Epoch 48, Batch 350: Loss = 0.7196, LR = 0.000010\n",
      "Epoch 48/50 completed.\n",
      "  Train Loss: 0.7178\n",
      "  Val Loss: 0.5948\n",
      "  Val Accuracy: 0.7843\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 49, Batch 50: Loss = 0.7238, LR = 0.000010\n",
      "Epoch 49, Batch 100: Loss = 0.7080, LR = 0.000010\n",
      "Epoch 49, Batch 150: Loss = 0.7029, LR = 0.000010\n",
      "Epoch 49, Batch 200: Loss = 0.7078, LR = 0.000010\n",
      "Epoch 49, Batch 250: Loss = 0.6912, LR = 0.000010\n",
      "Epoch 49, Batch 300: Loss = 0.7343, LR = 0.000010\n",
      "Epoch 49, Batch 350: Loss = 0.7189, LR = 0.000010\n",
      "Epoch 49/50 completed.\n",
      "  Train Loss: 0.7126\n",
      "  Val Loss: 0.5930\n",
      "  Val Accuracy: 0.7848\n",
      "  Learning Rate: 0.000010\n",
      "Epoch 50, Batch 50: Loss = 0.7199, LR = 0.000010\n",
      "Epoch 50, Batch 100: Loss = 0.7157, LR = 0.000010\n",
      "Epoch 50, Batch 150: Loss = 0.7132, LR = 0.000010\n",
      "Epoch 50, Batch 200: Loss = 0.6961, LR = 0.000010\n",
      "Epoch 50, Batch 250: Loss = 0.7115, LR = 0.000010\n",
      "Epoch 50, Batch 300: Loss = 0.7122, LR = 0.000010\n",
      "Epoch 50, Batch 350: Loss = 0.7038, LR = 0.000010\n",
      "Epoch 50/50 completed.\n",
      "  Train Loss: 0.7220\n",
      "  Val Loss: 0.5908\n",
      "  Val Accuracy: 0.7859\n",
      "  Learning Rate: 0.000010\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training complete! Results saved to 'adam_fixed_lr_results.png'\n",
      "Best validation accuracy: 78.59%\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "from torch.utils.data import DataLoader, Subset\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "# Set up device for training\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \n",
    "                      \"mps\" if torch.backends.mps.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# Define a simple neural network model\n",
    "class SimpleNN(nn.Module):\n",
    "    def __init__(self, hidden_size=128, dropout=0.2):\n",
    "        super().__init__()\n",
    "        self.flatten = nn.Flatten()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(28*28, hidden_size),\n",
    "            nn.BatchNorm1d(hidden_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(hidden_size, 10)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.flatten(x)\n",
    "        return self.model(x)\n",
    "\n",
    "# Set up data paths and hyperparameters\n",
    "DATA_ROOT = os.path.join(os.path.expanduser(\"~\"), \"data\")\n",
    "os.makedirs(DATA_ROOT, exist_ok=True)\n",
    "\n",
    "BATCH_SIZE = 128\n",
    "EPOCHS = 50\n",
    "INIT_LR = 0.00001  # Fixed learning rate\n",
    "\n",
    "# Define transforms for the dataset\n",
    "train_transform = transforms.Compose([\n",
    "    transforms.RandomRotation(10),\n",
    "    transforms.RandomAffine(degrees=0, translate=(0.1, 0.1)),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "test_transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "# Load Fashion MNIST dataset\n",
    "train_dataset = torchvision.datasets.FashionMNIST(\n",
    "    DATA_ROOT, train=True, download=True, transform=train_transform\n",
    ")\n",
    "test_dataset = torchvision.datasets.FashionMNIST(\n",
    "    DATA_ROOT, train=False, download=True, transform=test_transform\n",
    ")\n",
    "\n",
    "# Use smaller subset for faster training (optional)\n",
    "train_subset = Subset(train_dataset, list(range(50000)))\n",
    "test_subset = Subset(test_dataset, list(range(10000)))\n",
    "\n",
    "# Create data loaders\n",
    "train_loader = DataLoader(\n",
    "    train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True\n",
    ")\n",
    "test_loader = DataLoader(\n",
    "    test_subset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2, pin_memory=True\n",
    ")\n",
    "\n",
    "# Initialize model, loss function, and optimizer\n",
    "model = SimpleNN(hidden_size=128, dropout=0.2).to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=INIT_LR)  # Fixed learning rate\n",
    "\n",
    "# Training function\n",
    "def train_model():\n",
    "    # Initialize history tracking\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    val_accuracies = []\n",
    "    lr_history = []\n",
    "    \n",
    "    # Training loop\n",
    "    for epoch in range(1, EPOCHS + 1):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        batch_count = 0\n",
    "        \n",
    "        # Training phase\n",
    "        for i, (inputs, labels) in enumerate(train_loader):\n",
    "            inputs, labels = inputs.to(device), labels.to(device)\n",
    "            \n",
    "            # Zero the parameter gradients\n",
    "            optimizer.zero_grad()\n",
    "            \n",
    "            # Forward pass\n",
    "            outputs = model(inputs)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            # Backward pass and optimize\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            # Update statistics\n",
    "            running_loss += loss.item()\n",
    "            batch_count += 1\n",
    "            \n",
    "            # Print batch progress every 50 batches\n",
    "            if i % 50 == 49:\n",
    "                print(f'Epoch {epoch}, Batch {i+1}: Loss = {running_loss/50:.4f}, '\n",
    "                      f'LR = {optimizer.param_groups[0][\"lr\"]:.6f}')\n",
    "                running_loss = 0.0\n",
    "                batch_count = 0\n",
    "        \n",
    "        # Calculate epoch training loss\n",
    "        epoch_train_loss = running_loss / batch_count if batch_count > 0 else running_loss / len(train_loader)\n",
    "        \n",
    "        # Validation phase\n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for inputs, labels in test_loader:\n",
    "                inputs, labels = inputs.to(device), labels.to(device)\n",
    "                outputs = model(inputs)\n",
    "                loss = criterion(outputs, labels)\n",
    "                val_loss += loss.item()\n",
    "                \n",
    "                _, predicted = torch.max(outputs.data, 1)\n",
    "                total += labels.size(0)\n",
    "                correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        # Calculate epoch statistics\n",
    "        epoch_val_loss = val_loss / len(test_loader)\n",
    "        epoch_val_acc = correct / total\n",
    "        \n",
    "        # Store history\n",
    "        train_losses.append(epoch_train_loss)\n",
    "        val_losses.append(epoch_val_loss)\n",
    "        val_accuracies.append(epoch_val_acc)\n",
    "        lr_history.append(optimizer.param_groups[0]['lr'])\n",
    "        \n",
    "        # Print epoch summary\n",
    "        print(f'Epoch {epoch}/{EPOCHS} completed.')\n",
    "        print(f'  Train Loss: {epoch_train_loss:.4f}')\n",
    "        print(f'  Val Loss: {epoch_val_loss:.4f}')\n",
    "        print(f'  Val Accuracy: {epoch_val_acc:.4f}')\n",
    "        print(f'  Learning Rate: {optimizer.param_groups[0][\"lr\"]:.6f}')\n",
    "    \n",
    "    return {\n",
    "        'train_losses': train_losses,\n",
    "        'val_losses': val_losses,\n",
    "        'val_accuracies': val_accuracies,\n",
    "        'lr_history': lr_history\n",
    "    }\n",
    "\n",
    "# Train the model\n",
    "print(f\"Starting training with fixed learning rate Adam for {EPOCHS} epochs...\")\n",
    "print(f\"Learning rate: {INIT_LR}\")\n",
    "\n",
    "training_history2 = train_model()\n",
    "\n",
    "# Plot training results\n",
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "# Plot training and validation loss\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(range(1, EPOCHS+1), training_history2['train_losses'], 'b-', label='Train Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history2['val_losses'], 'r-', label='Val Loss')\n",
    "plt.title('Loss vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot validation accuracy\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(range(1, EPOCHS+1), training_history2['val_accuracies'], 'g-', marker='o')\n",
    "plt.title('Validation Accuracy vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "# Plot learning rate over time (will be constant)\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(range(1, EPOCHS+1), training_history2['lr_history'], 'k-')\n",
    "plt.title('Learning Rate vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Learning Rate')\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('adam_fixed_lr_results.png')\n",
    "plt.show()\n",
    "\n",
    "print(\"Training complete! Results saved to 'adam_fixed_lr_results.png'\")\n",
    "\n",
    "# Print best accuracy achieved\n",
    "best_accuracy = max(training_history2['val_accuracies'])\n",
    "print(f\"Best validation accuracy: {best_accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "0b2a883c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(training_history2['val_accuracies'])):\n",
    "    training_history2['val_accuracies'][i]=100*training_history2['val_accuracies'][i]\n",
    "                   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "283667df",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/2887544624.py:3: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"y-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history['train_losses'], 'y-',linestyle=\"dashed\", marker='o', label='NARS: Train Loss')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/2887544624.py:14: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"y-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history['val_losses'], 'y-', marker='o',linestyle=\"dashed\", label='NARS: Val Loss')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/2887544624.py:26: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"r-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history1['val_accuracies'], 'r-',linestyle=\"dashed\", marker='o', label=\"OneCycle\")\n",
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/2887544624.py:27: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"b-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history2['val_accuracies'], 'b-',linestyle=\"dotted\", marker='o', label=\"Fix\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/2887544624.py:38: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"r-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history1['lr_history'], 'r-',linestyle=\"dashed\", marker='.', label=\"OneCycle\")\n",
      "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_55395/2887544624.py:39: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string \"b-\" (-> linestyle='-'). The keyword argument will take precedence.\n",
      "  plt.plot(range(1, EPOCHS+1), training_history2['lr_history'], 'b-',linestyle=\"dotted\", marker='.', label=\"Fixed\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plot training and validation loss\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['train_losses'], 'y-',linestyle=\"dashed\", marker='o', label='NARS: Train Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['train_losses'], 'r', label='OneCycle: Train Loss', marker='o')\n",
    "plt.plot(range(1, EPOCHS+1), training_history2['train_losses'], 'b',linestyle=\"dotted\", marker='o', label='Fix: Train Loss')\n",
    "#plt.title('Loss vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Training Loss')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['val_losses'], 'y-', marker='o',linestyle=\"dashed\", label='NARS: Val Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['val_losses'], 'r', marker='o', label='OneCycle: Val Loss')\n",
    "plt.plot(range(1, EPOCHS+1), training_history2['val_losses'], 'b',linestyle=\"dotted\", marker='o', label='Fix: Val Loss')\n",
    "#plt.title('Loss vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation Loss')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "# Plot validation accuracy\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['val_accuracies'], 'y-', marker='o', label=\"NARS\")\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['val_accuracies'], 'r-',linestyle=\"dashed\", marker='o', label=\"OneCycle\")\n",
    "plt.plot(range(1, EPOCHS+1), training_history2['val_accuracies'], 'b-',linestyle=\"dotted\", marker='o', label=\"Fix\")\n",
    "\n",
    "#plt.title('Validation Accuracy vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation Accuracy (%)')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()\n",
    "# Plot learning rate over time\n",
    "plt.figure()\n",
    "plt.plot(range(1, EPOCHS+1), training_history['lr_history'], 'y-', marker='.', label=\"NARS\")\n",
    "plt.plot(range(1, EPOCHS+1), training_history1['lr_history'], 'r-',linestyle=\"dashed\", marker='.', label=\"OneCycle\")\n",
    "plt.plot(range(1, EPOCHS+1), training_history2['lr_history'], 'b-',linestyle=\"dotted\", marker='.', label=\"Fixed\")\n",
    "#plt.title('Learning Rate vs. Epochs')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Learning Rate')\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6315ae5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4773808454765993\n",
      "0.3944603876222538\n",
      "0.06196968802405745\n",
      "0.4742286024214346\n",
      "0.7070896029472351\n",
      "0.5907959738109685\n"
     ]
    }
   ],
   "source": [
    "print(min(training_history['train_losses']))\n",
    "print(min(training_history['val_losses']))\n",
    "\n",
    "print(min(training_history1['train_losses']))\n",
    "print(min(training_history1['val_losses']))\n",
    "\n",
    "print(min(training_history2['train_losses']))\n",
    "print(min(training_history2['val_losses']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6a20c28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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