{ "cells": [ { "cell_type": "code", "execution_count": 3, "id": "61c58faa", "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": 20, "id": "2bd5a689", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using device: mps\n", "Starting training with Enhanced NAL LR Tuner for 20 epochs...\n", "Initial learning rate: 0.0001\n", "Epoch 1, Batch 0: Loss=2.3129, LR=0.000001\n", "Epoch 1, Batch 100: Loss=1.8099, LR=0.000100\n", "Epoch 1, Batch 200: Loss=1.5114, LR=0.010075\n", "Epoch 1, Batch 300: Loss=1.4411, LR=0.010075\n", "Epoch 1: train_loss=1.6739, val_loss=1.2766, accuracy=55.33%, lr=0.010075\n", "Epoch 2, Batch 0: Loss=1.4028, LR=0.010075\n", "Epoch 2, Batch 100: Loss=1.2240, LR=0.010075\n", "Epoch 2, Batch 200: Loss=1.2284, LR=0.010075\n", "Epoch 2, Batch 300: Loss=1.0941, LR=0.010075\n", "Epoch 2: train_loss=1.2076, val_loss=1.0295, accuracy=62.81%, lr=0.010075\n", "Epoch 3, Batch 0: Loss=1.1421, LR=0.010075\n", "Epoch 3, Batch 100: Loss=1.1482, LR=0.010075\n", "Epoch 3, Batch 200: Loss=1.0721, LR=0.010075\n", "Epoch 3, Batch 300: Loss=0.9009, LR=0.010075\n", "Epoch 3: train_loss=1.0620, val_loss=0.8803, accuracy=68.90%, lr=0.010075\n", "Epoch 4, Batch 0: Loss=1.0193, LR=0.010075\n", "Epoch 4, Batch 100: Loss=0.8252, LR=0.010075\n", "Epoch 4, Batch 200: Loss=0.9200, LR=0.010075\n", "Epoch 4, Batch 300: Loss=0.9589, LR=0.010075\n", "Epoch 4: train_loss=0.9851, val_loss=0.8526, accuracy=70.01%, lr=0.010075\n", "Epoch 5, Batch 0: Loss=1.1294, LR=0.009873\n", "Epoch 5, Batch 100: Loss=0.8714, LR=0.013177\n", "Epoch 5, Batch 200: Loss=1.0221, LR=0.012544\n", "Epoch 5, Batch 300: Loss=1.0915, LR=0.011942\n", "Epoch 5: train_loss=0.9536, val_loss=0.8259, accuracy=72.02%, lr=0.010503\n", "Epoch 6, Batch 0: Loss=0.9326, LR=0.010503\n", "Epoch 6, Batch 100: Loss=1.0177, LR=0.010503\n", "Epoch 6, Batch 200: Loss=0.9139, LR=0.010503\n", "Epoch 6, Batch 300: Loss=0.7992, LR=0.010503\n", "Epoch 6: train_loss=0.8989, val_loss=0.7361, accuracy=74.15%, lr=0.010503\n", "Epoch 7, Batch 0: Loss=1.0432, LR=0.010503\n", "Epoch 7, Batch 100: Loss=0.8369, LR=0.010503\n", "Epoch 7, Batch 200: Loss=0.8311, LR=0.010503\n", "Epoch 7, Batch 300: Loss=0.9935, LR=0.010503\n", "Epoch 7: train_loss=0.8650, val_loss=0.7434, accuracy=73.77%, lr=0.010503\n", "Epoch 8, Batch 0: Loss=0.8880, LR=0.010503\n", "Epoch 8, Batch 100: Loss=0.7865, LR=0.010503\n", "Epoch 8, Batch 200: Loss=0.8358, LR=0.010503\n", "Epoch 8, Batch 300: Loss=0.7836, LR=0.010503\n", "Epoch 8: train_loss=0.8422, val_loss=0.7326, accuracy=75.31%, lr=0.010503\n", "Epoch 9, Batch 0: Loss=0.7349, LR=0.010503\n", "Epoch 9, Batch 100: Loss=0.8085, LR=0.010503\n", "Epoch 9, Batch 200: Loss=0.6471, LR=0.010503\n", "Epoch 9, Batch 300: Loss=0.9299, LR=0.010503\n", "Epoch 9: train_loss=0.8193, val_loss=0.7041, accuracy=75.62%, lr=0.010503\n", "Epoch 10, Batch 0: Loss=0.9045, LR=0.010293\n", "Epoch 10, Batch 100: Loss=0.8806, LR=0.014560\n", "Epoch 10, Batch 200: Loss=0.9469, LR=0.014704\n", "Epoch 10, Batch 300: Loss=0.8418, LR=0.011913\n", "Epoch 10: train_loss=0.8349, val_loss=0.7317, accuracy=75.15%, lr=0.013836\n", "Epoch 11, Batch 0: Loss=0.6200, LR=0.013836\n", "Epoch 11, Batch 100: Loss=0.7924, LR=0.013836\n", "Epoch 11, Batch 200: Loss=0.8706, LR=0.013836\n", "Epoch 11, Batch 300: Loss=0.8086, LR=0.013836\n", "Epoch 11: train_loss=0.8152, val_loss=0.7438, accuracy=74.15%, lr=0.013836\n", "Epoch 12, Batch 0: Loss=0.6414, LR=0.013836\n", "Epoch 12, Batch 100: Loss=0.9536, LR=0.013836\n", "Epoch 12, Batch 200: Loss=0.8160, LR=0.013836\n", "Epoch 12, Batch 300: Loss=0.8158, LR=0.013836\n", "Epoch 12: train_loss=0.8040, val_loss=0.6842, accuracy=76.86%, lr=0.013836\n", "Epoch 13, Batch 0: Loss=1.0206, LR=0.013836\n", "Epoch 13, Batch 100: Loss=0.8806, LR=0.013836\n", "Epoch 13, Batch 200: Loss=0.7845, LR=0.013836\n", "Epoch 13, Batch 300: Loss=0.6524, LR=0.013836\n", "Epoch 13: train_loss=0.7928, val_loss=0.7052, accuracy=75.87%, lr=0.013836\n", "Epoch 14, Batch 0: Loss=0.9114, LR=0.013836\n", "Epoch 14, Batch 100: Loss=0.7075, LR=0.013836\n", "Epoch 14, Batch 200: Loss=0.5882, LR=0.013836\n", "Epoch 14, Batch 300: Loss=0.7979, LR=0.013836\n", "Epoch 14: train_loss=0.7745, val_loss=0.6925, accuracy=76.27%, lr=0.013836\n", "Epoch 15, Batch 0: Loss=0.6410, LR=0.013836\n", "Epoch 15, Batch 100: Loss=0.6911, LR=0.012908\n", "Epoch 15, Batch 200: Loss=0.7284, LR=0.013041\n", "Epoch 15, Batch 300: Loss=0.6633, LR=0.012161\n", "Epoch 15: train_loss=0.7523, val_loss=0.6515, accuracy=77.60%, lr=0.010892\n", "Epoch 16, Batch 0: Loss=0.7199, LR=0.010892\n", "Epoch 16, Batch 100: Loss=0.6644, LR=0.010892\n", "Epoch 16, Batch 200: Loss=0.5577, LR=0.010892\n", "Epoch 16, Batch 300: Loss=0.8260, LR=0.010892\n", "Epoch 16: train_loss=0.7226, val_loss=0.6556, accuracy=77.71%, lr=0.010892\n", "Epoch 17, Batch 0: Loss=0.7766, LR=0.010892\n", "Epoch 17, Batch 100: Loss=0.6755, LR=0.010892\n", "Epoch 17, Batch 200: Loss=0.7336, LR=0.010892\n", "Epoch 17, Batch 300: Loss=0.7248, LR=0.010892\n", "Epoch 17: train_loss=0.7121, val_loss=0.6479, accuracy=77.47%, lr=0.010892\n", "Epoch 18, Batch 0: Loss=0.5742, LR=0.010892\n", "Epoch 18, Batch 100: Loss=0.7997, LR=0.010892\n", "Epoch 18, Batch 200: Loss=0.6888, LR=0.010892\n", "Epoch 18, Batch 300: Loss=0.8426, LR=0.010892\n", "Epoch 18: train_loss=0.7056, val_loss=0.6155, accuracy=79.38%, lr=0.010892\n", "Epoch 19, Batch 0: Loss=0.6067, LR=0.010892\n", "Epoch 19, Batch 100: Loss=0.5526, LR=0.010892\n", "Epoch 19, Batch 200: Loss=0.6518, LR=0.010892\n", "Epoch 19, Batch 300: Loss=0.7002, LR=0.010892\n", "Epoch 19: train_loss=0.7025, val_loss=0.6075, accuracy=79.24%, lr=0.010892\n", "Epoch 20, Batch 0: Loss=0.8725, LR=0.010892\n", "Epoch 20, Batch 100: Loss=0.7472, LR=0.012140\n", "Epoch 20, Batch 200: Loss=0.6878, LR=0.012509\n", "Epoch 20, Batch 300: Loss=0.6361, LR=0.009925\n", "Epoch 20: train_loss=0.6954, val_loss=0.6029, accuracy=79.55%, lr=0.009085\n" ] }, { "data": { "image/png": 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" ] }, "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: 79.55%\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", " # Simple CNN layers to handle the 2D structure and color channels\n", " self.conv_layers = nn.Sequential(\n", " nn.Conv2d(3, 32, kernel_size=3, padding=1), # 3 input channels for RGB\n", " nn.ReLU(),\n", " nn.MaxPool2d(2), # 32x32 -> 16x16\n", " nn.Conv2d(32, 64, kernel_size=3, padding=1),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2), # 16x16 -> 8x8\n", " )\n", " \n", " # Fully connected layers\n", " self.fc_layers = nn.Sequential(\n", " nn.Flatten(),\n", " nn.Linear(64 * 8 * 8, hidden_size), # 64 channels * 8*8 size\n", " nn.BatchNorm1d(hidden_size),\n", " nn.ReLU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(hidden_size, 10) # 10 classes in CIFAR-10\n", " )\n", " \n", " def forward(self, x):\n", " x = self.conv_layers(x)\n", " x = self.fc_layers(x)\n", " return x\n", "\n", "# Set up data paths and hyperparameters\n", "DATA_ROOT = os.path.join(os.path.expanduser(\"~\"), \"data2\")\n", "os.makedirs(DATA_ROOT, exist_ok=True)\n", "\n", "BATCH_SIZE = 128\n", "EPOCHS = 20\n", "INIT_LR = 0.0001\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.RandomHorizontalFlip(),\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # RGB channels\n", "])\n", "\n", "test_transform = transforms.Compose([\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # RGB channels\n", "])\n", "\n", "# Load Fashion MNIST dataset\n", "train_dataset = torchvision.datasets.CIFAR10(\n", " DATA_ROOT, train=True, download=True, transform=train_transform\n", ")\n", "test_dataset = torchvision.datasets.CIFAR10(\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": 10, "id": "3aea34e9", "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: 67.72%\n", " Avg Accuracy: 62.09%\n", " LR Range: 0.001007 - 0.001007\n", "\n", "Pattern 2: mid_medium\n", " Count: 7\n", " Max Accuracy: 76.44%\n", " Avg Accuracy: 73.67%\n", " LR Range: 0.000973 - 0.001007\n", "\n", "Pattern 3: late_medium\n", " Count: 10\n", " Max Accuracy: 78.83%\n", " Avg Accuracy: 77.58%\n", " LR Range: 0.000973 - 0.001998\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": 12, "id": "552d2e32", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using device: mps\n", "Starting training with OneCycleLR for 20 epochs...\n", "Initial learning rate: 0.009999999999999995\n", "Maximum learning rate: 0.1\n", "Epoch 1, Batch 50: Loss = 1.8664, LR = 0.010101\n", "Epoch 1, Batch 100: Loss = 1.6288, LR = 0.010403\n", "Epoch 1, Batch 150: Loss = 1.5105, LR = 0.010906\n", "Epoch 1, Batch 200: Loss = 1.4953, LR = 0.011606\n", "Epoch 1, Batch 250: Loss = 1.4505, LR = 0.012500\n", "Epoch 1, Batch 300: Loss = 1.3973, LR = 0.013586\n", "Epoch 1, Batch 350: Loss = 1.3761, LR = 0.014857\n", "Epoch 1/20 completed.\n", " Train Loss: 0.1389\n", " Val Loss: 1.1712\n", " Val Accuracy: 0.5728\n", " Current LR: 0.016034\n", "Epoch 2, Batch 50: Loss = 1.3028, LR = 0.017628\n", "Epoch 2, Batch 100: Loss = 1.2677, LR = 0.019390\n", "Epoch 2, Batch 150: Loss = 1.2800, LR = 0.021311\n", "Epoch 2, Batch 200: Loss = 1.2682, LR = 0.023383\n", "Epoch 2, Batch 250: Loss = 1.2411, LR = 0.025598\n", "Epoch 2, Batch 300: Loss = 1.2934, LR = 0.027944\n", "Epoch 2, Batch 350: Loss = 1.2377, LR = 0.030411\n", "Epoch 2/20 completed.\n", " Train Loss: 0.1283\n", " Val Loss: 1.2290\n", " Val Accuracy: 0.5719\n", " Current LR: 0.032517\n", "Epoch 3, Batch 50: Loss = 1.2363, LR = 0.035177\n", "Epoch 3, Batch 100: Loss = 1.2385, LR = 0.037926\n", "Epoch 3, Batch 150: Loss = 1.2352, LR = 0.040751\n", "Epoch 3, Batch 200: Loss = 1.2307, LR = 0.043640\n", "Epoch 3, Batch 250: Loss = 1.2279, LR = 0.046580\n", "Epoch 3, Batch 300: Loss = 1.2228, LR = 0.049557\n", "Epoch 3, Batch 350: Loss = 1.2087, LR = 0.052560\n", "Epoch 3/20 completed.\n", " Train Loss: 0.1252\n", " Val Loss: 1.0624\n", " Val Accuracy: 0.6249\n", " Current LR: 0.055030\n", "Epoch 4, Batch 50: Loss = 1.2125, LR = 0.058042\n", "Epoch 4, Batch 100: Loss = 1.2058, LR = 0.061040\n", "Epoch 4, Batch 150: Loss = 1.2305, LR = 0.064012\n", "Epoch 4, Batch 200: Loss = 1.2292, LR = 0.066943\n", "Epoch 4, Batch 250: Loss = 1.2222, LR = 0.069820\n", "Epoch 4, Batch 300: Loss = 1.2352, LR = 0.072631\n", "Epoch 4, Batch 350: Loss = 1.2284, LR = 0.075362\n", "Epoch 4/20 completed.\n", " Train Loss: 0.1271\n", " Val Loss: 1.2606\n", " Val Accuracy: 0.5784\n", " Current LR: 0.077535\n", "Epoch 5, Batch 50: Loss = 1.2530, LR = 0.080091\n", "Epoch 5, Batch 100: Loss = 1.2552, LR = 0.082536\n", "Epoch 5, Batch 150: Loss = 1.2351, LR = 0.084856\n", "Epoch 5, Batch 200: Loss = 1.2413, LR = 0.087043\n", "Epoch 5, Batch 250: Loss = 1.2544, LR = 0.089086\n", "Epoch 5, Batch 300: Loss = 1.2568, LR = 0.090976\n", "Epoch 5, Batch 350: Loss = 1.2530, LR = 0.092705\n", "Epoch 5/20 completed.\n", " Train Loss: 0.1303\n", " Val Loss: 1.1354\n", " Val Accuracy: 0.5948\n", " Current LR: 0.093996\n", "Epoch 6, Batch 50: Loss = 1.2688, LR = 0.095412\n", "Epoch 6, Batch 100: Loss = 1.2500, LR = 0.096646\n", "Epoch 6, Batch 150: Loss = 1.2343, LR = 0.097694\n", "Epoch 6, Batch 200: Loss = 1.2524, LR = 0.098550\n", "Epoch 6, Batch 250: Loss = 1.2161, LR = 0.099211\n", "Epoch 6, Batch 300: Loss = 1.2702, LR = 0.099673\n", "Epoch 6, Batch 350: Loss = 1.2082, LR = 0.099935\n", "Epoch 6/20 completed.\n", " Train Loss: 0.1295\n", " Val Loss: 0.9967\n", " Val Accuracy: 0.6455\n", " Current LR: 0.100000\n", "Epoch 7, Batch 50: Loss = 1.1879, LR = 0.099979\n", "Epoch 7, Batch 100: Loss = 1.2302, LR = 0.099916\n", "Epoch 7, Batch 150: Loss = 1.2028, LR = 0.099812\n", "Epoch 7, Batch 200: Loss = 1.2268, LR = 0.099668\n", "Epoch 7, Batch 250: Loss = 1.2074, LR = 0.099482\n", "Epoch 7, Batch 300: Loss = 1.2004, LR = 0.099256\n", "Epoch 7, Batch 350: Loss = 1.2409, LR = 0.098989\n", "Epoch 7/20 completed.\n", " Train Loss: 0.1247\n", " Val Loss: 1.0650\n", " Val Accuracy: 0.6341\n", " Current LR: 0.098740\n", "Epoch 8, Batch 50: Loss = 1.1810, LR = 0.098400\n", "Epoch 8, Batch 100: Loss = 1.1588, LR = 0.098020\n", "Epoch 8, Batch 150: Loss = 1.1511, LR = 0.097601\n", "Epoch 8, Batch 200: Loss = 1.1729, LR = 0.097142\n", "Epoch 8, Batch 250: Loss = 1.1792, LR = 0.096645\n", "Epoch 8, Batch 300: Loss = 1.1717, LR = 0.096109\n", "Epoch 8, Batch 350: Loss = 1.1632, LR = 0.095535\n", "Epoch 8/20 completed.\n", " Train Loss: 0.1199\n", " Val Loss: 1.0220\n", " Val Accuracy: 0.6560\n", " Current LR: 0.095036\n", "Epoch 9, Batch 50: Loss = 1.1376, LR = 0.094395\n", "Epoch 9, Batch 100: Loss = 1.1076, LR = 0.093717\n", "Epoch 9, Batch 150: Loss = 1.1235, LR = 0.093002\n", "Epoch 9, Batch 200: Loss = 1.1318, LR = 0.092253\n", "Epoch 9, Batch 250: Loss = 1.1384, LR = 0.091468\n", "Epoch 9, Batch 300: Loss = 1.1238, LR = 0.090650\n", "Epoch 9, Batch 350: Loss = 1.1113, LR = 0.089798\n", "Epoch 9/20 completed.\n", " Train Loss: 0.1126\n", " Val Loss: 0.9099\n", " Val Accuracy: 0.6863\n", " Current LR: 0.089075\n", "Epoch 10, Batch 50: Loss = 1.0742, LR = 0.088164\n", "Epoch 10, Batch 100: Loss = 1.1030, LR = 0.087221\n", "Epoch 10, Batch 150: Loss = 1.0889, LR = 0.086248\n", "Epoch 10, Batch 200: Loss = 1.0754, LR = 0.085245\n", "Epoch 10, Batch 250: Loss = 1.0339, LR = 0.084213\n", "Epoch 10, Batch 300: Loss = 1.0825, LR = 0.083153\n", "Epoch 10, Batch 350: Loss = 1.0944, LR = 0.082065\n", "Epoch 10/20 completed.\n", " Train Loss: 0.1174\n", " Val Loss: 0.9347\n", " Val Accuracy: 0.6860\n", " Current LR: 0.081154\n", "Epoch 11, Batch 50: Loss = 1.0698, LR = 0.080019\n", "Epoch 11, Batch 100: Loss = 1.0505, LR = 0.078860\n", "Epoch 11, Batch 150: Loss = 1.0345, LR = 0.077676\n", "Epoch 11, Batch 200: Loss = 1.0535, LR = 0.076470\n", "Epoch 11, Batch 250: Loss = 1.0243, LR = 0.075242\n", "Epoch 11, Batch 300: Loss = 1.0537, LR = 0.073994\n", "Epoch 11, Batch 350: Loss = 1.0454, LR = 0.072725\n", "Epoch 11/20 completed.\n", " Train Loss: 0.1101\n", " Val Loss: 0.8486\n", " Val Accuracy: 0.7066\n", " Current LR: 0.071671\n", "Epoch 12, Batch 50: Loss = 1.0080, LR = 0.070370\n", "Epoch 12, Batch 100: Loss = 1.0265, LR = 0.069051\n", "Epoch 12, Batch 150: Loss = 1.0457, LR = 0.067717\n", "Epoch 12, Batch 200: Loss = 1.0482, LR = 0.066368\n", "Epoch 12, Batch 250: Loss = 1.0230, LR = 0.065006\n", "Epoch 12, Batch 300: Loss = 0.9919, LR = 0.063632\n", "Epoch 12, Batch 350: Loss = 1.0215, LR = 0.062246\n", "Epoch 12/20 completed.\n", " Train Loss: 0.1073\n", " Val Loss: 0.8149\n", " Val Accuracy: 0.7190\n", " Current LR: 0.061102\n", "Epoch 13, Batch 50: Loss = 0.9825, LR = 0.059699\n", "Epoch 13, Batch 100: Loss = 0.9838, LR = 0.058288\n", "Epoch 13, Batch 150: Loss = 0.9818, LR = 0.056869\n", "Epoch 13, Batch 200: Loss = 0.9906, LR = 0.055446\n", "Epoch 13, Batch 250: Loss = 0.9933, LR = 0.054018\n", "Epoch 13, Batch 300: Loss = 0.9744, LR = 0.052586\n", "Epoch 13, Batch 350: Loss = 0.9698, LR = 0.051153\n", "Epoch 13/20 completed.\n", " Train Loss: 0.1007\n", " Val Loss: 0.7684\n", " Val Accuracy: 0.7320\n", " Current LR: 0.049976\n", "Epoch 14, Batch 50: Loss = 0.9508, LR = 0.048542\n", "Epoch 14, Batch 100: Loss = 0.9428, LR = 0.047109\n", "Epoch 14, Batch 150: Loss = 0.9720, LR = 0.045678\n", "Epoch 14, Batch 200: Loss = 0.9793, LR = 0.044251\n", "Epoch 14, Batch 250: Loss = 0.9441, LR = 0.042828\n", "Epoch 14, Batch 300: Loss = 0.9466, LR = 0.041411\n", "Epoch 14, Batch 350: Loss = 0.9419, LR = 0.040002\n", "Epoch 14/20 completed.\n", " Train Loss: 0.0987\n", " Val Loss: 0.7599\n", " Val Accuracy: 0.7387\n", " Current LR: 0.038852\n", "Epoch 15, Batch 50: Loss = 0.9514, LR = 0.037458\n", "Epoch 15, Batch 100: Loss = 0.9300, LR = 0.036075\n", "Epoch 15, Batch 150: Loss = 0.9300, LR = 0.034703\n", "Epoch 15, Batch 200: Loss = 0.9248, LR = 0.033344\n", "Epoch 15, Batch 250: Loss = 0.9251, LR = 0.031998\n", "Epoch 15, Batch 300: Loss = 0.8917, LR = 0.030667\n", "Epoch 15, Batch 350: Loss = 0.8797, LR = 0.029353\n", "Epoch 15/20 completed.\n", " Train Loss: 0.0953\n", " Val Loss: 0.7362\n", " Val Accuracy: 0.7432\n", " Current LR: 0.028287\n", "Epoch 16, Batch 50: Loss = 0.8900, LR = 0.027004\n", "Epoch 16, Batch 100: Loss = 0.8928, LR = 0.025740\n", "Epoch 16, Batch 150: Loss = 0.8865, LR = 0.024496\n", "Epoch 16, Batch 200: Loss = 0.8903, LR = 0.023273\n", "Epoch 16, Batch 250: Loss = 0.8751, LR = 0.022071\n", "Epoch 16, Batch 300: Loss = 0.8933, LR = 0.020893\n", "Epoch 16, Batch 350: Loss = 0.8972, LR = 0.019739\n", "Epoch 16/20 completed.\n", " Train Loss: 0.0914\n", " Val Loss: 0.7141\n", " Val Accuracy: 0.7484\n", " Current LR: 0.018811\n", "Epoch 17, Batch 50: Loss = 0.8641, LR = 0.017703\n", "Epoch 17, Batch 100: Loss = 0.8740, LR = 0.016622\n", "Epoch 17, Batch 150: Loss = 0.8819, LR = 0.015567\n", "Epoch 17, Batch 200: Loss = 0.8352, LR = 0.014542\n", "Epoch 17, Batch 250: Loss = 0.8550, LR = 0.013545\n", "Epoch 17, Batch 300: Loss = 0.8742, LR = 0.012579\n", "Epoch 17, Batch 350: Loss = 0.8478, LR = 0.011643\n", "Epoch 17/20 completed.\n", " Train Loss: 0.0890\n", " Val Loss: 0.7033\n", " Val Accuracy: 0.7535\n", " Current LR: 0.010899\n", "Epoch 18, Batch 50: Loss = 0.8392, LR = 0.010022\n", "Epoch 18, Batch 100: Loss = 0.8632, LR = 0.009177\n", "Epoch 18, Batch 150: Loss = 0.8215, LR = 0.008366\n", "Epoch 18, Batch 200: Loss = 0.8386, LR = 0.007589\n", "Epoch 18, Batch 250: Loss = 0.8460, LR = 0.006847\n", "Epoch 18, Batch 300: Loss = 0.8528, LR = 0.006141\n", "Epoch 18, Batch 350: Loss = 0.8384, LR = 0.005471\n", "Epoch 18/20 completed.\n", " Train Loss: 0.0889\n", " Val Loss: 0.6876\n", " Val Accuracy: 0.7621\n", " Current LR: 0.004949\n", "Epoch 19, Batch 50: Loss = 0.8227, LR = 0.004346\n", "Epoch 19, Batch 100: Loss = 0.8488, LR = 0.003780\n", "Epoch 19, Batch 150: Loss = 0.8143, LR = 0.003253\n", "Epoch 19, Batch 200: Loss = 0.8323, LR = 0.002764\n", "Epoch 19, Batch 250: Loss = 0.8241, LR = 0.002314\n", "Epoch 19, Batch 300: Loss = 0.8337, LR = 0.001903\n", "Epoch 19, Batch 350: Loss = 0.8283, LR = 0.001532\n", "Epoch 19/20 completed.\n", " Train Loss: 0.0862\n", " Val Loss: 0.6791\n", " Val Accuracy: 0.7652\n", " Current LR: 0.001257\n", "Epoch 20, Batch 50: Loss = 0.7978, LR = 0.000959\n", "Epoch 20, Batch 100: Loss = 0.8086, LR = 0.000701\n", "Epoch 20, Batch 150: Loss = 0.8343, LR = 0.000484\n", "Epoch 20, Batch 200: Loss = 0.7946, LR = 0.000307\n", "Epoch 20, Batch 250: Loss = 0.8212, LR = 0.000171\n", "Epoch 20, Batch 300: Loss = 0.8306, LR = 0.000077\n", "Epoch 20, Batch 350: Loss = 0.8342, LR = 0.000023\n", "Epoch 20/20 completed.\n", " Train Loss: 0.0837\n", " Val Loss: 0.6772\n", " Val Accuracy: 0.7668\n", " Current LR: 0.000010\n" ] }, { "data": { "image/png": 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" ] }, "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: 76.68%\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", " # Simple CNN layers to handle the 2D structure and color channels\n", " self.conv_layers = nn.Sequential(\n", " nn.Conv2d(3, 32, kernel_size=3, padding=1), # 3 input channels for RGB\n", " nn.ReLU(),\n", " nn.MaxPool2d(2), # 32x32 -> 16x16\n", " nn.Conv2d(32, 64, kernel_size=3, padding=1),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2), # 16x16 -> 8x8\n", " )\n", " \n", " # Fully connected layers\n", " self.fc_layers = nn.Sequential(\n", " nn.Flatten(),\n", " nn.Linear(64 * 8 * 8, hidden_size), # 64 channels * 8*8 size\n", " nn.BatchNorm1d(hidden_size),\n", " nn.ReLU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(hidden_size, 10) # 10 classes in CIFAR-10\n", " )\n", " \n", " def forward(self, x):\n", " x = self.conv_layers(x)\n", " x = self.fc_layers(x)\n", " return x\n", "\n", "# Set up data paths and hyperparameters\n", "DATA_ROOT = os.path.join(os.path.expanduser(\"~\"), \"data2\")\n", "os.makedirs(DATA_ROOT, exist_ok=True)\n", "\n", "BATCH_SIZE = 128\n", "EPOCHS = 20\n", "MAX_LR = 0.1\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.RandomHorizontalFlip(),\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # RGB channels\n", "])\n", "\n", "test_transform = transforms.Compose([\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # RGB channels\n", "])\n", "\n", "# Load Fashion MNIST dataset\n", "train_dataset = torchvision.datasets.CIFAR10(\n", " DATA_ROOT, train=True, download=True, transform=train_transform\n", ")\n", "test_dataset = torchvision.datasets.CIFAR10(\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", "\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": 13, "id": "d302e1e3", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/1213289703.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_5224/1213289703.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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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/1213289703.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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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/1213289703.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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UsmVLyZIlizz++OPhLgoREVmA01ta3CIiCMfRFYHQ888/L7NmzQp3MYiIiMhiXBEI1apVK861RoiIiMh9wh4IrV27Vpo2baqJa2jiWrRoUYz7TJgwQQoVKiSpU6eWqlWryubNm8NSVqL4OHbsmKxevVov7bh/IrKmTp066XnyrbfeinI7zpu+uohKlSolqVKlkpMnT/psIMBjIiIi9NxaokQJGT16tCYve1u4cKE8+OCDkilTJm1QKFOmjPTr10+cJOyB0NWrV6V8+fIa7Pgyb948eeGFF2TYsGGyfft2vW/Dhg09Q+bg/vvvl7Jly8bYjh8/noQ1IRL55JNPpGDBglKnTh29nDRpkq6AfP36dR0ZEf1LJlBTp06Nsn9cJyL3QNDy9ttvy/nz52O93/r16/V7B7mxM2fO9HmfZ599VqcS2Lt3rwwePFiGDh2q31mmlStXStu2baV169baALFt2zYZOXJkohKTrSjsw+cbNWqkmz9jx47Vg9W5c2e9joO0ZMkSmTZtmgwaNEhv27lzZ9DKc/PmTd1MOIkBJnyy6uRbwYC6mRNbOd2RI0dk69atUqlSJSlQoEDAj8f7bePGjforC18iuDx16pS20Hj/8sJr2atXL+nZs2eM2VDN7ZtvvpGHHnpIb//ss8/0F5n3380NQ0v79u0r3bp18xwjXOKzgfqgHvjFZm7FixfXy0CPLerw119/6ePz5csnduOm97Gb6hrM+pr7MbfECOXnxSxb9DLWq1dP9u3bJ6NGjZJ33nnH733xI+nJJ5+UmjVragvOyy+/7HP4ec6cOT2tTR999JH88MMP0qNHD71t8eLF+v300ksveR6DujZv3jzRr1186hrfx5rvi+jvjfi+V8IeCMU1+yciUESqJkx+hTcCTkShgBPRiBEjYtx+5swZnQLcqfCGuXjxor6hrDjBWLD897//lQEDBmh9Uc8xY8bIU089pb+Idu3apS2NvjYE3/gCgDlz5si7774br+fz9cHGrynzF9WFCxc8rZuY6XbPnj1+94Vu4egfbOz/9ddf99ky9dhjj+n/v/32W/3RkDFjRs+GJm7zEr8Yy5UrF+O1wX67dOliq9E1bnkfu62uwawvPnvYF1pozVZazCsUqNmzZ2uAYX5exo0bJx06dAh46LevzxfKZA7z9/47ngvX8dl85pln9IcWAjDzvqgPYJbmBQsWaKsQusfwuv3444/y8MMPR3kOcw4fwzBkw4YN+v1TtGhRz34wN8/vv/+uP/7QyxIK/uoaXygrXpdz587pj0ZveB1sHwidPXtWXyAzYjXhemwnjOgQOOEkh244vGnwBqlWrZrP+yLoQlecd4sQZh/Nnj27njicyvyAoZ5O/VJFUI1fNmZwgjrjVxICAXxJDB8+PNagHF8KUL16dWnRooVO6+694XXD7d7BCib4+uOPP/SxZgDkveG9ZU4KhlbPGjVqeP6GD7j3fUuXLq2/Ar33j2OGHDuUD+9VfOFhK1KkiKe8eCy+JLD5+3zg8WYQZL42r776qrzxxhuSN29enbUVW58+fbSMZhD3999/6+2YniI+X2KhbnFyw/vYjXUNZn3xgxYnSLSyYsN5Ae/fxJbtueee0y0QKEe6dOn8/j36iR31xobvLPSW4POJlh9zIkHUB7744gv9jCGVBNC9he4x5AWZ8Fqih2XatGn6+cf3BLrdMMra3A/+/9NPP0mFChW0Kx65QvXr15f27dtr7lEwRa9rfKGseE3uueceLb+36Nf97kNcYMWKFfG+Lw6urwNsvgGdDB8MJ9UTX04IftAighYd/D86BNoHDhzQXzv4gOOkjqDGvDT/jy8B83XBvFTYfJkyZYp0795d94svp8mTJ3takuKC7q24uup87b9r166xPqZdu3ZSsmRJvT++eM1gydzuu+8+2b9/v89mZHQT4/XBBmhqN18H5A+0adNG/4/PDF4nDHowLzt27CgVK1bUv+MXN1qp8CPD/AWNusRV9oRw2vs4Nm6qa7Dqi8eaScLmFi7+nh8/1szbff0dtyFPCLmC+AET/b7Tp0+Xp59+2nMdLVXoIvvwww+jjKBGQDNkyBDNN0IeLn7kmV31kD59ev3uxPcDBmhs2rRJf0x+8MEH2isTjJm546prfF9DX++L+L5PLB0IZcuWTb+8kX/hDddxgiKKq+ka/d6xwfurWLFi+ksJCYGJhRM7kvnRh4/9BrvVIyH7R64QWpPQQuTviwFfjvhb9NYsjOoEDDxAPpQZ2JhBUtasWeWff/7R/x86dEg3E15T8/6Yx8t7pImZ34QWIuRqodsPn3eipIaT+ZUrVwJ6DFpC77333hifl927d2sLaiDPnVCPPPKIfhegF8P7ew5lQMCC5OaBAwd6bsePp7lz5+rnzvu7oVixYvr/+fPn6//R6oNWYm/oLsP2n//8RwMnjDDDQCYzd9fuLB0IYQp0fJHilye6HABvPFxHE30oYRQbNrPvkqwLv1bMVh+01JjJyQ0aNNDuTFw2adJEk/Jxv+gtKsEOVrC/UCYah2L/2J+v1ib8QvQHvzixoasBSeIIlsyACZdoaTL56srGL0GzOxLd1ebM7/il+fnnn+uXMlrTsBUuXDjBTedEsUFrQmzdU74gEPD1ecHtSQnD6DFqGi2+JnSVIUiKPhIbrUT4m3cg5C19+vTaFYYWnx07dvhtncFUNgjg0KXoGEaYXb582dixY4duKM7YsWP1/4cPH9a/z50710iVKpUxY8YMY/fu3Ua3bt2MzJkzGydPnkyS8l28eFHLhUsnu3PnjnHixAm9tLpbt24Zq1evNl566SWjVKlSenzMrX79+lHue/v27RiPx3vryy+/9LzHnC6QY3v06FF9bXEZTNhfsmTJohyriIgIo3Hjxkb58uWNX3/91XPfMWPGRLkftuTJkxtFihQxGjZsaGzbts1z32vXrun7wYRj+sUXX7ji2NrpM2ul+l6/fl3PJbhMrFB9Xu7evavva1x669ixo9G8efMot3Xo0MFInTq1fk7wmOzZsxsTJ06MsU/UGff57bff9HrNmjWN559/Psp9zp07Z6RJk8ZYsGCBXh82bJgxYMAAreOBAweM7du3G506ddL77NmzJ6R1DcbxjO/5O+yBEF7g6F962HDATR9++KFRoEABIzIy0qhSpYqxadOmJCsfA6HwwBfLqlWrYnzB4AOTM2fOKO+VFClSGLVr1zbefffdeH04rVbXULNKfT/99FMNaMzABtd9Wb9+vTFo0CCjdevWRrly5Yy0adNGOd7egRB+OGFfRYsWNcqWLavBFe6DoOvjjz82nMwqx9XNgVCoBBIIHTx4UM+NeN/jRwDe+/4aCu69916jf//+fgMh6N69u1GmTBl9nfEdjM9h/vz59Tnw3fvoo48a69atM4LFCoFQBP4Jd6uUlWEkDvpRkVTq9FFjGMYdWx5JUkHzrTlfDppn0T2DYaAm9Itjck10dWGIOLq+MmfObMu6JgUr1Rc5QYHmT+ErCt1tGG2GxyL52+zKwCgdJID6g65QdIsCuu/QvYbRJU5gpeNqp/qiKxdTVaC7Nb6jipKaOawdI6LsNH1FOOoa2/GM7/nb0jlC4cQcofDAiQ592N4TbGF+iy1btkjlypU9SdA4mZlDRsk+EpLfhC9HjELDhpEv3saPH68JoUj09J72wvux3nOEYbQLpix44IEHomy4zeknHCLyzfk/IxKod+/emn2PEzCFHn4RoCUIrT++Gim9Rw7iFyGDIAIELxil88QTT8RoJcB7BBNFmsyJK48ePaoz5mLiVAzCwNQIGLGGX40mjITz9SOI67wROQ8DIbIEzGqMoZmYwTs6nNAwMoIorlFvZoDsa0QgZgTHJJBr1qzRWYAxzxECJTTJYx4kc0kSc94VTCmAocQYhYh9Y4JJrvNG5DzsGqOwQKsPgh5z9mNM1Ddx4kT9ZY+hmZgeIZRD3Ml5MMcSJsWMbR05BDsYWozNhDmQorfw/Pnnn7pg5c8//6ybr3wVDJ1Gvhrfm0T2xkCIkhymbMckYDj5YPkJzBeFJFbcbuZpIAk6VJMSknPhvYL3kxlgxwdagzBZnDfMe4T3H+ZTwYa5y6LPTI5AHffBvCtY9gf5SwiwODEkkb0wEPKDydLB98svv+ispBjJY56AkINlTununawa6kkJiWKDlkhMUocNo9QQtKM7LPpMwkjaxwy7gERsKFOmjAZECIywcRZ8ImtjjpAfTJYO7szPWNMGeT4IgnACQT4QhkN7r2tDZLccJLQkIRDCKuAIgACrdaObFwGU9xIHCKKYZE1kPWwRopBC1wHW5MGoMMAina+//nqUKeGJ7MDfOm94T5uLzyLvbd26dbpGG5Kya9euHaVFFEP1ixQpEqXFCEsWoDUUQRJ+HGBJEbaGEiUdBkIUdLdv3/asC4UTBkbZYGjzyJEjpUKFCuEuHlGCxdVlmz17dmnVqpVu0aGFGZ+DAwcO6DZjxgy9HXMYYY4ktD6j1Qj3QesTAi8iCj12jVHQYAVnBDuY4dN73p+FCxfK0qVLGQSRqz311FNy/vx5/SwMGjRIqlWrpkP3Ma8RRqaZ+Ue4xMzq+NwwR5GcLlmyZPL111+HtwxhfXZyBAw/xjIHyJfAXCt///13lDlWMByeiESn+X/00Ud1lmuMksS8RmPGjIlxPwRDaFXKnTu3tgxhAshr166FpcxkLQicu3Tpoq2IGCGJJH6sGn/u3LmgP9elS5d0gEupUqV0+Qok/terV0+++uornxPf2hUDIT8wYqx06dKeZR0oKuQzrFixQsaOHav5PljzCTP3IhjC5Ij4xUtEscOaaUiqjj4rNnKGEDQh52jatGnSvHlzHZaPmbCnT5+u3c/kPuhSxRxZyCXDBKHIV5s0aZJO74AWRsyIHiwXLlzQmf5nzZql051gfUfkvrVt21ZefvnlKDOx216Clnt1Ea4+HxNWDccKx94rgufOnduYNGmSriJsdVy127nsWld8ppInT66fJVziOj5LK1asMPr27WsUKFDA81nLkyeP1s+s6+nTpw03sOLq80ePHtUV2nEZTP5WZMfK7/ny5TOuXbsW5Xa8LmnTpjV69Oih1wsWLGiMHDnS6Ny5s5E+fXpdPX7y5MlRHnPkyBHjiSeeMDJlymRkyZLFaNasma5kb+rZs6eRLl064++//45RvsuXLxu3b982RowYoSvVR1e+fHnj1Vdf9VyfOnWqUbp0aV3BPleuXEbv3r09f8N7esGCBZ66xlWuUKw+z0AoDgyEosIHPnoQFBERYfz555+GXdj1ZJlQbqqvneuKz9bq1at9nlRxktixY4cxfPhw4+2339bbUMdjx44Z2bNnN+69915j8ODBxqZNm2xZdysEQleuXPG7+brvhAkTPN+FuMR13B49SPG3z0ADoXPnzul37ahRo3w+5tlnn9XAAY9BIJQ1a1Yt019//WWMHj1ay7hnzx69L/aN90yXLl2MX375RV+Pp556yihZsqRx8+ZNfY2xr27dusXrfLB582bPbdu3b9dy7t+/X69//PHHRurUqY1x48YZe/fu1fu+//77PgOhuMrlCwOhJMBA6P87efKkvmG9gyBzwxe4Xdj5ZJkQbqqv2+qK1ogUKVLEaJ3FCWzJkiVRTg6har1wSiDk63vN3Bo3bhzlvjix+7tvzZo1o9w3W7ZsPu8XaCCEIBePW7hwoc/HjB07Vv9+6tQpDYSefvrpKPvLkSOHMXHiRL0+e/ZsDS6893/z5k0jTZo0xvLly3Uf2Bf2GZdGjRpp65EJLZi1atXyXEcL5pAhQ/w+3jsQiqtcoQqEmCNE8WKu34RRYb5W+cYweSJKWpij6+TJk/L555/rXEZYKPbEiRM6/L5Jkybyxhtv6P0weIELxgZPOBOF4/vcWFDYO+cMic7I44Rdu3ZpfhHeL+nTp9cta9ascuPGDZ0AN5D6Pfvss5qvhMfeunVLc0SRzA14vuPHj0vdunXjta+4yhUqnEeI4vTZZ5/pmx1vRiR3vvXWW5o8x0VRicIvS5YsOjQfG0Zw/vjjjzocGSPNmjVrpgMbMBzfe3g+F4z1Pf2HP+aM4t6TYyIIjb7kCuaKwrxQ3g4dOhSU8uHHJgIarM/YsmXLGH/H7XgvYC4rMOdyM+GxZnlR14oVK2oAHR0ej0Akc+bMuuZeXJo2barLJWG6B4xiQyL/448/rn9LkyaNBCKucoUKW4T84Kgx0dmgX3zxRenQoYMGQVgIFfOdDBgwQD/cq1ev1ktO/EZkDTghIcD5+OOPdZh1lSpVdISR9wkb8COmb9++uqAs/R/8yPO3Yei4txIlSvhccgW3Rz/5+9tnoLCuXf369fXYXr9+PcrfzFZBjOjyXrPRH8zphvcFFicuVqxYlC1Tpkza6o/RjNgnWnR8BSzmagGYC6tjx446mhEbHme+BgioMHM6RrXFR1zlCpk4OwBdzq05QkjMq1+/vqc/G328Tsm7cFMeidvqy7rGb4CD9/bAAw8YH330keVHfFp11Ji/BPdQjBrDoBTkHNWoUcNYs2aNjrBaunSpUbZsWaN48eL6vQ3IEfJOSDZHcg0bNkz/f/XqVb0/cnnWrl1rHDhwQOuB/B6zLthXqVKldJTazJkzjd9//12fHyPAihUrZpw/fz5KuTDaERtymbzNmDFDc6rGjx+v99u2bZvxwQcf+MwRik+5omOOEIVM586d5YcfftDJEOfPny9vvvlmjNwgIrLngrH9+/fXnCJ0ZaBV6IMPPtBf9hT4a1urVq0k62LEOnTI18R6dTh+mLcN3Z5Y027jxo2aTxMf+F7HnEAFChTQiTvRzYeWfbT8Y/4qwL42bdokTz/9tH7/Y528GjVqaD4QJgH1bqFBuTDnECZerFq1apTnQmvRuHHjtCULCxOjZwGtPgktVyhE/C8io1hm1sQBx+RRoTwQ4YamcyS2oUkSAQ8S09DMiqTK8uXLi5Pr6nRuqi/r6h9yhaIvGHv27Fnt/kBuyTPPPKO34aSDEx4mb+zUqZPkzZtXnHRsUb+DBw/qUkDRu7ysAqdldD0hOI1PV5cVylu8eHHp1auXvPDCC0la19iOZ3zP3/wJQJ4vGeT/IPEN8EsDi0Da4UNIRAlbMBazVWN5Bm9IekWrA7ahQ4fqkiD/+c9/9Jd89ARcojNnzsjcuXM1Twk9CXbk7J9MFC+ImtEMiZEIy5Yt89zOIIjIfTDSbMaMGdoqhB9I3333nX4/IIjCQAmsJUhkQgvd66+/rt2vaFm0IwZCLoe+2gcffFC++eYbzRcI5lo1RGQ/GNGEvA7kauzdu1cGDhzomYPm3XfflatXr/rtesNIUlySexiGoa1CmL7Brtg15mJo/XnyySd1cT2sZPzpp5/q0FsiIsBwcMwbhokZly5dqsmzuM2ErhAkX6NV4O2339YWJOTvoHWA02qQXTAQimUeIWyYb8OJETx+2WGFeHxxYdXiBQsWxJg0jIgIkBuELjNsJjPRGhPoebPLhI0cJ+QMRhCOI7vG/Ojdu7fOEoqEYafBsPiXX35Zv7CQBInm7Ny5c4e7WERkI5jgb8WKFdKgQYMYf8MPyF9//VWsyPzBh+UgyP6uXbuml4lJ5GeLkAthdtKePXtK2bJl9dJ76nUiovjA98Yjjzyic9pg/bLo3yHr1q2TRo0aidVgmDbmq0FeC06eVpxmwW7D58NRVzwOQRBy17AcSGJ6NBgIucSGDRt0yRBk9ePNhsmtiIiCNWEjusPQEoTAAuttIcnahDWrcubMaYlRRfj+Qws45p45fPiwWBFO8ma+lRsCobuJqCuCICTzJwYDIYfDmwxBT79+/XQF4CVLljAXiIiCConRyAmKPmGj+R2EyRoxAq1Pnz46qzXmLwonjJDFBIBW7R5DYHDu3DntfrRii5VV6ooWvWCczxgIORSGsP7+++86HwgmuzKnTEdiIwMhIkqKCRvNpGrM/ov5ykaNGiXjx4/XLnks6JzYX/KJgZOuVWeWRnCAkzzK54ZAKGWY6+rsV9ilsCwG+uwxIyyCIDQ3Ym0YjPCw6gefiJwpe/bssnPnTp2xGquLYx4ijFrFkgiY1ZoTNFK4MRByYEsQFuHzTlxEINSuXTvH9zUTkTXhlz7WLsOyHeiexySuaCXCYq+Ym4gonBgIOcyff/4ZY/QGrqPvnogonPBjrHHjxvLTTz/pNB6YkBFL+3hP8srvKkpqDIQcBrO+Ru9nRU4QEhiJiKwSENWrV0+mTZvm+b7CUGgs7VGyZEl5+umn5Y8//gh3McklGAg5dCirmRCNy8mTJ1t6hlciovPnz0vlypW1BRv5jGXKlJEnnnhCdu3a5en2xzQgXMuMgo2BkB9YXgPz7uCDaXWYuwMzRK9Zs8YzlPXQoUM6YzQuueYPEVld3rx55dtvv5Vt27ZpdxmG3X/xxRdy//3364bk6scff1wvMSCEKFgiDC64EisM+cyUKZNcvHhRMmbMKFaDw4ehqGj1QfkwSRiGyQcKv8IwQycWT3TDcE231NVt9WVdnQNLdIwcOVLmzZsX429o6caPPKe2dDv92CZVXeN7/nb2K+wCQ4cO1SAIfe5YPT4hQRARkdXcd999Ov0H5kLz1QqOgSFEwcBAyMYwMdmbb76p/584caL2pxMROQlmxPfVUoAlPDBpLFFiMRCyqc8++0yXzQAEQ1jnh4jI6QNAEBRhiQzMSYTcoSFDhsj169fDXUyyMQZCNoQ5ODp16qT/RzD0yiuvhLtIREQhgwEfBw4ckC+//FLzIDHXULNmzXTVcizbgW60FStWhLuYZFMMhGyoUqVKOnqiQ4cO8t5773HGaCJyRctQ9erV9RKr23/99dfy1Vdf6Wiz/fv3S/369XVWfaJAMRCyITQLY54NDCF1+ogCIiJ/MMx+9+7d0rdvX/1BiKH1RIHiWdQm0CyMEWLm8hnoL8eKvUREboZh0VizDDlDL730kuf2HTt2yJ49e8JaNrIHBkI2cPLkSW32feONN2TYsGHhLg4RkeVgZXvzx+GtW7d0mY7y5cvL8OHDdYFXIn8YCFnchQsX5NFHH9UWITT79urVK9xFIiKyNEykV7BgQQ2IRowYoQHRjz/+GO5ikUUxELIwDAnFyAistZMzZ05drTl37tzhLhYRkaVly5ZNlixZorNS58qVSydfrF27tnTp0kXOnTsX7uKRxTAQsqjbt29L27ZtZd26dTpF+PLly6Vo0aLhLhYRkS0gebpNmza6ij2WIcL16dOnS6lSpXQIPpGJgZBFYYLEb775RlKnTq2XaNolIqLAZM6cWT7++GNdub5s2bJSrlw5KVSoULiLRRbCQMiimjRpImnTppX58+dLjRo1wl0cIiJbq1atmmzfvl3mzJnjmXsNi3FiLjbkYK5evVqOHTsW7mJSGDAQ8mPChAlSunRpqVy5cliev3Xr1rq6ctOmTcPy/EREToNRZVjl3IRZ+THkHmkHderU0QRrzM9G7sJAyI/evXvrRF1btmxJsudEYt+RI0c817Nnz55kz01E5Db4sesN87QhLYEtQ+7CQMgisIbOU089JQ899JDOG0REREkbCMGdO3f0RzC5BwMhC1i5cqUGQfg10qhRIx0qT0REoVW8eHGfyxQNHjxYTp06FZYyUdJjIBRmmBa+RYsWOvEX8oImTpzIRVSJiJIAFnCdMmWKLlkECIrSpEmjXWOcjdo9UoS7AG6FDxpagvr37y9XrlyRunXr6kKq5geSiIhCr2vXrtKwYUPZt2+fFCtWTK5evaqjyZA4Te7AQCgMMCqhW7dungVU8YFbuHChpEqVKtxFIyJyZcsQNl8WLVqkEzD269ePrfUOxUAoDC1B3kGQeRt+gWTIkCGsZSMiov/v8OHD0r59e7l27ZrOUP3RRx9JZGRkuItFQcYcoST2119/RQmCzFEKaJYlIiLrKFCggIwcOVJzhz755BPtQvvnn3/CXSwKMgZCFhilgLwg9E0TEZF1oCsMXWKLFy/WFnusYF+1alXZu3dvuItGQcRAKMyjFHA5efJkv/3TREQU/iWPfvrpJ83nROs9gqEVK1aEu1gUJAyEwjRKActnYG0bXOI6ERFZFxZs3bx5s1SvXl1zOr/77rtwF4mChMnSFhylQERE1oN1ylatWqWr2fft2zfcxaEgYYsQERFRPGGaE8z/liLF/7UjYDLcIUOGaCsR2RMDISIiogR68cUXZdSoUVKtWjXZv39/uItDCcBAiIiIKIE6d+4sefPm1XmGkES9du3acBeJAsRAiIiIKIEqVKigSdSVKlWSc+fOSb169WTGjBnhLhYFgIEQERFRIuTJk0fWrFkjTzzxhNy+fVtbiQYOHBhj8lyyJgZCREREiZQ2bVqZO3euDB06VK9PmjRJl+gg63P88PmjR49Khw4d5PTp05rl/9prr2nUTkREFExYNWDEiBFSqlQpyZIlixQuXNizniSWV8LKApw2xXocHwgh+Bk3bpzcf//9cvLkSalYsaI0btxY0qVLF+6iERGRAz355JOe/0+dOtWz0DYCJawswEl0rcXxXWO5c+fWIAhy5col2bJl46J5REQUcmgJMoMgwGX37t31drKOsAdCGGrYtGlTTTbDAneLFi2KcZ8JEyZIoUKFJHXq1Do8ERn6CbFt2zZd6T1//vxBKDkREZF/6A6LnjCNcxDWKyPrCHsgdPXqVSlfvrwGO77MmzdPXnjhBRk2bJhs375d79uwYUPN+TGhxQfrwETfjh8/7rkPWoGeeeYZbZYkIiIKNeQEoTssOv4Yt5aw5wg1atRIN3/Gjh0rzz77rA5HNDPxlyxZItOmTZNBgwbpbTt37oz1OW7evCktWrTQ+2PBvLjui8106dIlvURU7+ShkKibYRiOrqMb6+q2+rKuzmXH+qKnA+esnj17akuQ6c0335RPP/1Ue0GcUteECmVd47vPsAdCscEaLujOGjx4sOc2RNeYsGrjxo3x2gde4E6dOkmdOnV09FhcRo8erVn/0Z05c0Zu3LghToU3DNbKwevl6xeMk7iprm6rL+vqXHatL1I/MEjn0KFDcuLECXnuued0wkWMKEP+kJPqmhChrOvly5ftHwidPXtWo+icOXNGuR3X9+zZE699bNiwQbvXypUr58k/mj17ttx3330+74+gC11x3i1CaMbMnj27ZMyYUZz8ZsSvE9TTDR88t9TVbfVlXZ3LzvXFqvXmoB38wMc5BhMwvvLKKz7rYue6BiqUdUVese0DoWB4+OGHA2pyw8rC2KLDAXL6GxJvRjfU0211dVt9WVfnckJ9+/Xrpyf9Nm3aeFawd2pd4ytUdY3v/iz9CmOoe/LkyeXUqVNRbsd1DIUnIiKy20n/6aeflsjISM9taCWi8LF0IIQ3CvpWV65c6bkNrTu4Xq1atZA+N0axlS5dWipXrhzS5yEiInf6999/tYUIeUT4P7k0ELpy5YqO+jJHfh08eFD/f+TIEb2OvtRPPvlEZs6cKX/88Ydm32PIvTmKLFR69+4tu3fvli1btoT0eYiIyJ3279+v57fvv/9eBgwYEO7iuFbYc4S2bt0qtWvX9lw3E5U7duyomfVt27bVEVtYyA5LZCDhbNmyZTESqImIiOykZMmSMmvWLHn88cd1KSgM4unSpUu4i+U6YW8RqlWrlg6bi74hCDL16dNHV/HF/D4///yzzi5NRERkd61bt5bhw4fr/3v06CHr168Pd5FcJ+yBkFUxR4iIiJLCa6+9pq1Ct2/fllatWukPf0o6DIT8YI4QERElBQzzRi8IUj+QCoJWIiZPJx0GQkRERGGWLl06+frrr6VIkSIyZMiQWOcYouDiK01ERGQBBQoU0FUTMH+e98LiFFpsESIiIrKIlClTev6PaWSWL18e1vK4AQMhP5gsTURE4YJFWjFCumXLlrJjx45wF8fRGAj5wWRpIiIKl3z58mny9PXr16V58+Yxlpqi4GEgREREZDFIlp4zZ45Ounj06FFtGcJcehR8DISIiIgsKHPmzLJ48WK93Lhxo064iAmHKbgYCBEREVlUiRIlZN68eZ65hrAUBwUXAyEiIiILa9Cggbz33nv6/88//1xnoKbg4TxCsYwaw3bnzp1wF4WIiFzu+eefl7Rp00r79u2jDLGnxGOLkB8cNUZERFYREREh3bp10xmoTfyhHhwMhIiIiGwECdNvvvmmNG7cmGuSBQEDISIiIptNtvjWW2/J999/Ly+99FK4i2N7DISIiIhspHDhwjJr1iz9//jx42XMmDGyevVqOXbsWLiLZksMhIiIiGymVatWMmLECP3/yy+/LHXq1JGCBQvK1KlTw10022Eg5AfXGiMiIivr3LlzlOt3796V7t27s2UoQAyE/OCoMSIisrJ9+/bFuA0jyXzdTv4xECIiIrKh4sWL64zT3pInTy7FihULW5nsiIEQERGRTVeonzJligY/gMvJkyfr7RR/nFmaiIjIprp27SoNGzbU7jC0BDEIChwDISIiIhtD8IMNEy2uWLFCsmbNKhUqVAh3sWyDXWNEREQOgEkW69evL6+88kq4i2IrDISIiIgcoG3btpontHz5ctm+fXu4i2MbDIT84DxCRERkJ0WKFJF27drp/0ePHh3u4tgGAyE/OI8QERHZzaBBg/Tyyy+/lD179oS7OLbAQIiIiMghypYtK82aNdPE6XfeeSfcxbEFBkJEREQOMnjwYL2cPXu2HDlyJNzFcXYgdOPGjeCVhIiIiBLtwQcflNq1a0vRokXl+PHj4S6O8wIhLOr2xhtvSN68eSV9+vRy4MABvf21117jqrdEREQWMHfuXPn99981KKIgB0JvvvmmzJgxQ/seIyMjo/RLfvrpp4HujoiIiIIsR44cnqU3KMiB0KxZs3Rtk/bt20d5kcuXL88MdSIiIgu5fv26TJo0SS5evBjuojhniY2///7b58q26DK7fft2sMpFREREidSkSRNZvXq1XLhwwTO0nhLZIoRJBtetWxfj9i+++EIeeOCBQHdHREREIdKpUye9fP/997V1iILQIjR06FDp2LGjtgyhFeirr76SvXv3apfZt99+G+juiIiIKESefPJJPW8fPnxYpk2bppMFUyJbhJo3by7ffPONrnCbLl06fYH/+OMPvQ2LvTkFl9ggIiK7S5kypQwYMED/P2bMGKawBGseoRo1asgPP/wgp0+flmvXrsn69eulQYMG4iRcYoOIiJygS5cuOooMrUJz5swJd3HsHwhhUbdz587FuB2JWPgbERERWUeaNGmkf//++v+33npL01ooEYHQoUOH5M6dOzFuv3nzpuYNERERkbX07NlTMmfOrKO+0XBBCUiWXrx4sef/y5cvl0yZMnmuIzBauXKlFCpUKL67IyIioiSCc/a+ffvknnvuCXdR7BsItWjRQi8jIiJ01Fj0ZCwEQe+9917wS0hERESJxiAokV1j6FPEVqBAAU2SNq9jQ7cYhtA/9thj8d0dERERhcHRo0flww8/DHcx7DuP0MGDB0NTEiIiIgopLLVRqlQpHfFdpUoVqVq1qrhdwIEQXL16VdasWSNHjhyRW7duRfnbc889F6yyERERUZBzhdq0aaOLp48ePVoWLVokbhdwILRjxw5p3LixRpMIiLJmzSpnz56VtGnT6jwFDISIiIisa+DAgTJz5kz5+uuv5bfffpOyZcuKmwU8fB5zETRt2lTOnz+vcxNs2rRJJ2mqWLGivPvuu6EpJREREQUFusZatWql/3/77bfF7QIOhHbu3CkvvviiJEuWTJInT66J0vnz55d33nlHXnnlldCUkoiIiIJm8ODBejlnzhzX5/4GHAhhqDyCIEBXGPKEzH5HZKITERGRtaEXB0tj3blzR9cgC5djx47Jhg0b9NI2gdADDzzgWX+rZs2auujq559/Lv369XN9PyMREZGdWoXSpUunub7hMHXqVClcuLA8/vjjeonrtgiERo0aJblz59b/jxw5UrJkyaJTd585c0YmT54cijISERFRkKEx49ixY/Lmm28m+XPjebt16+ZZ9wyX3bt3D0vLUMCjxipVquT5P7rGli1bJk40YcIE3Xytq0ZERGR3WCkic+bMYXnu77//PsbirzjfYhmQfPnyWbtFyJ/t27c7ambp3r17y+7duz3dgERERE61bt06mTdvXpI8165du3TQVXQYgIVFYZNaQIEQFlt96aWXdHTYgQMH9LY9e/boOmSVK1eOEd0RERGRtS1btkweeeQRbQDA/IChhEaTOnXqyIULF6RgwYIa/AAukV6T1K1BAQVCSGJq1KiRzkaJeQcefPBB+eyzz6RatWqSK1cunZTpu+++C21piYiIKKjq1asnRYoUkXPnzsmnn34asudBD0vdunXln3/+0aU9MB0PGlW+/PJLvezatauEQ7wDofHjx2sAhFmk58+fr5cff/yx/PrrrzJp0iS59957Q1tSIiIiCroUKVLobNOAiZGjL50VDJh8GQEXWoKqV6+uOULIT0ILEK6HoyUo4EBo//798sQTT+j/MSMlXjjMPRDOwhMREVHidezYUUeEY9QWenuCCfMEYc6iS5cuSY0aNbQrLmPGjGIV8Q6Erl+/ruuJmZnmqVKl8gyjJyIiIvtKlSqVJ4H5rbfeCtqI6bVr10rDhg3l8uXLUqtWLVm6dKlkyJBBrCSg4fPoO0yfPr3+/99//9V8oWzZskW5DxddJSIisp9u3brp/IB//fWXfPXVV55eoIRavXq1jibHIu3oFsMir2aDii0DoQIFCsgnn3ziuY4E6dmzZ0e5D1qKGAgRERHZT4YMGfQcjq6xyMjIRO1rxYoV0qxZM+1NQovQwoULdaF2K4p3IHTo0KHQloSIiIjCauDAgfLqq69qHnBCYaodTKtz48YNady4sY4KS506tVhV0CZUJCIiIntLkyZNooIgTKODliAEQbhEF5uVgyBgIERERERR3Lx5U9NhNm/eLPG1ePFibQnC8PuWLVvKggULNAnb6hgIERERURRYQQLJ0yNGjJD4QA5Q69at5fbt25pkjeU6EptnlFQYCBEREVEUPXv2lGTJkmlXF2aAjg1afhD8YDR5u3bt5L///a+kTJlS7IKBEBEREUWBxU/btGmj/+/fv79OtOjLnDlz5Mknn9R5h55++mkdTZ6YHCNbBEKYGdLXhsmSQjEtNxERESW9kiVL6uWPP/6oC6RizVFvGGaP4AdBUKdOnXRuQbsFQQkKhLA2SJYsWWJsuB3Z5nixhg0bxpXoiYiIbOrYsWPyxhtveK7jnN69e3dPyxCCnmeeeUZv/89//qNBkrmSvN0EHLqh8kOGDNHor0qVKnobsspnzpypcw+cOXNGF21DpjiSrYiIiMhe/vrrrxgNGmj52bdvn64VhkRqwzCkR48eMmHCBM0nsquAAyEEPO+9956n7xCaNm0q9913n0yePFlWrlyps1Bjmm4GQkRERPZTvHhxDW68gyG0+GzcuNFzbu/Tp4988MEHuqqEnQUcwv3000/ywAMPxLgdt+EFgocffliOHDkiVnDhwgWpVKmS3H///VK2bNkoy4QQERFRTPny5ZMpU6Z4urtw2bZtW08Q1K9fP0cEQQkKhPLnzx8jYQpwG/4G586d07whq6ydgtVvMfzv559/llGjRmn5iIjIPpCbsmHDBr+jlxK7bywQGop923n/Xbt21QaOsWPHSt++fXVYPAwYMEBvc0IQlKCuMeT/YL6ApUuXSuXKlfW2rVu3yp49e+SLL77Q61u2bNHI0QoQxZqr3WKmTPRpYiMiInvAD23kpKCbBt01yEnp3LlzUPY9ffp06d27d0j2ndD94744X2GLK/cmlOWf7rVvE1qE3nzzTccEQRBhJCAqOHjwoOYD/fnnn54hdsgmL1SoUMAFQGvNmDFjZNu2bXLixAmdnRJTdHvDgcV9Tp48KeXLl5cPP/zQk6gd3+6xmjVravIX9oMDG1+YGiBTpkxy8eJFyZgxowQLIneUB/2waIIMtkD3jzf66dOnJUeOHPFKerNa+QOBblsE6wjkkc8WbHY+tlYre6ACfR/bmdPqevXqVTl8+LAu8G1eYsN5Jq4J/SjpHDlyxNP7Y/X3cXzP3wka8F+4cGF56623JFhvfgQ3Xbp0kVatWsX4O6bpfuGFF2TSpElStWpVGTdunDRs2FD27t2rLxwg/wczWkb3/fffS548eXRo/65du+TUqVP6HI8//rjkzJlTrPDrBlE1RuA98sgjQds/gkuM7kOMG9/9oyyYCwpdiXG9GROy/8SUH4ErEvIxEtF7w/Ttvm6LrfzRf1miDxzNv8Fi5/3buezegZZdg1y7B6Fx7R8nJV+Bjvn/s2fPBr1MFHz79+8PaiBk2xYhtLBgyDyiuOjD6zCvQIILExERo0UIwQ++1D766CO9jufDQUB/5aBBgwJ+jl69ekmdOnU0GPLFbI70/vDi+c6fPx+UFiF8WSCQ5DxLoYMJvfwFSOjCja5atWpBWR0Zqy2bAwastH98xLH+D6a899ecHY6yoyxNmjTRfMLoxyuugNfXhgUfX3vtNU+g9c477+iMt8GCGXRffvnlkOw/IfvGfZHveM8998T54yWUZY++fxxXjCrOlSuXBjlmoIPv0LjgOxY9C5iPzrxMnz69fm9HH72EVqK8efMmqtx///23/hAPxb4Ts3/cH4FhtmzZYj22oSz/3z72jWOLYxnMQBr7x7Q72bNnD0mLEL5f4moRCjgQ+uabb6R9+/Zy5coV3bH3Fyv+/88//wQtEMJM1cjvQe6Rd3DUsWNHDca+/vrrOPeJViDsAy0deDEeeugh/dBiuL8vw4cP97nIHJpnsY/EQrKfryCsQoUKQUkwx+u/Y8eOgPePtwFa1RBExNb3m9D9x5e//eNLEWXDewIbglWc3M3rRGR9aJ3HSRQ/Ls1L7/+jG8MXJOki0MI8NjjRI5B76qmnglKmUO47oftHcIDzFV6PuIKDpHptoGjRorJ+/XoJpkDqGij0cpQoUSL4gRB22rhxYx19ZSYhB0v0QOj48eMa1WLIPn6ZmnBg1qxZo6PA4oKWK3PiJ2zoZkE+kz/haBHCm/fAgQNBibITuv/4RuVWLD+OqxkcmZfRN9yOfXfo0CFKsjzqOn78eP1VnVj4df7cc89Zbv+4P97H0X+4WKHsQ4cO1RYnX8fM1zH09zf8MMOXni/BSOqM7WsysfsP5b7DuX8MqqlRo4a26phbYr5DkZuCXNKKFSsGvdsT3w2YKBDra4Wq2zCQ/QfaShLK8h87dkwbHfD5xbkQLULBZIUWIbyJA5I2bVpj//79RiigOAsXLvRc//vvv/W2n376Kcr9BgwYYFSpUsVIChcvXtQy4DJYPv30UyN58uS6X1ziejAlZP937twxTpw4oZeh2H8gQrl/O5c91MfWimWPr6NHjxrJkiXTfZsbngO3W33/Cd13fI+rnV+bhH5H2Z3V6nry5Ek9rhEREcaNGzdsU9f4nr8DDoRatmxpzJs3LzFli3cgdPPmTf1Aed8GzzzzjNGsWTPDroEQ4Eti9erVQf+ySOj+A30zWq38gTh8+LDx5Zdf6mUo2PnYWq3sgbBzIMcfL/YLDkLJanW9e/eukS5dOj2+e/fudVwgFHDXGEZ+vP766zpPAfJskIDprVmzZkFPlsZQeQyZN5vR0CyKqb0TkiwdXxiyjw19o8gPCvbweatx2lDc2Liprm6rL7pPMK8ZZpMP1aixUHZBBNp9EuiUF1bq/gmUm97HVqxr1apVtft59uzZ2j3ppOHzAQdCsRUUgYyZVBVf6NvHh8dcpgOzVdauXVuyZs2qX2QYPo/kaMxbhIAIw+fnz5+vo3+SYgh8qOYRshorfvBCxU11dVt9WVfnclN9rVhX43/TmQSbLecRCvawb/x6Q+BjwpxBgOAHc8lghmokUiGpEhMqYs4grHwbznmAiIiI3CTCQTNJB2VCxWCqVatWnEteoBsMGxEREVGSB0JYYRZD0DHMFf+PDYbYOYF3jhAREZGb7dy5U2eCx7Q569atE9cFQu+//75OoohACP+PrenMKYEQ5hvCZvYxEhERuVXatGll+/btehmqfCFLB0JYZNXX/4mIiMj5ChUqpMHPtWvXdMUGLKHiFNZIRyciIiLLioyM9Cy2ipn+XZ0sjZwZjOZauXKlz0VXV61aFczyERERkQUULVpU5+rCCvTVq1cX1wZCzz//vAZCWDW6bNmyjuon9MZkaSIiov+vSJEisnr1arYIzZ07Vyc0xMKrTsZkaSIioqiBEDgtEEqWkH5CTKFORERE7lGqVCkpWbKkzgLt6kDoxRdflPHjx8c5CSIRERE5R6tWrXR5qzFjxoiru8bWr1+vfYRLly6VMmXKxFh09auvvgpm+YiIiIisEwhlzpxZWrZsGZrSEBERkeUZDppUMaBA6N9//9UFUhs0aOCoyZR84agxIiKiqDp06CDffvutzJw5U5o1ayauyxFKkSKF9OjRQ27evClOhxFju3fvli1btoS7KERERJZw48YNuXDhgqNGjgWcLF2lShXZsWNHaEpDRERElp5UETCpomtzhHr16qUjx44dOyYVK1aUdOnSRfl7uXLlglk+IiIisogiDpxLKOBAqF27dnrpvco8EqbMxCnm1BARETlTUbYIcfV5IiIit7cIHTx4UNcaTZYsmfsCoYIFC4obcNQYERFRVFiBHgOnbt26JcePH5d8+fKJ6wIhE0ZUYRVavBjenDKcjmuNERERRYUgqFatWnqJEWROEHAghAQpTKj466+/enKDwJxYiS0oREREzvXDDz+IkwTcuff8889L4cKF5fTp05I2bVr5/fffZe3atVKpUiX58ccfQ1NKIiIiIiu0CG3cuFFWrVol2bJl0yQpbA8//LCMHj1aR5JxjiEiIiLnu3nzpqRKlUpc1yKErq8MGTLo/xEMIVnKTKLeu3dv8EtIRERElrHqf40hjzzyiLiyRahs2bKya9cu7R6rWrWqvPPOOxIZGSlTpkzxDKsjIiIiZ8qaNaucO3fOnYuuwquvvipXr17V/7/++uvy2GOPSY0aNeSee+6RefPmhaKMREREZBFF/tfocfbsWR1ZnTFjRnFVINSwYUPP/4sVKyZ79uyRf/75R7JkyeKY6BA4jxAREVFMCHzQNYZACCPJ77//frGzBE8JuW/fPlm+fLlcv35dm8mchqvPExEROX+pjYADIfQL1q1bV0qUKCGNGzeWEydO6O1du3bVxViJiIjI2Yo4aPHVgAOh/v37S8qUKXVWacwjZGrbtq0sW7Ys2OUjIiIiiynioEAo4Byh77//XrvEoq8vUrx4cTl8+HAwy0ZEREQWVLFiRc0ZLlOmjLguEMKIMe+WIBMSpp0wsRIRERHFDkttYXOCgLvGMFR+1qxZnusYKXb37l2dT6h27drBLh8RERGRdVqEEPAgWXrr1q268vzLL7+s642hRWjDhg2hKSURERFZzqVLl7Q3yM49QskSMrP0n3/+qeuLNW/eXLvKWrVqpWuMmcPpiIiIyNmqV68umTJlknXr1omrWoQAFR8yZEiU244dOybdunXTpTaIiIjI2bJkyeKIkWMJnlDR1/xCU6dODdbuiIiIyMKKOmRSxaAFQk6D5TVKly4tlStXDndRiIiILKeIQ+YSYiDkB5fYICIi8o+BEBEREblWUa+uMcMwxPHJ0hgZFpsLFy4EozxERERkA4ULF9bLixcvyvnz5227AHuKQEaKxfX3Z555JhhlIiIiIotLmzattGnTRkeP3b59W+wq3oHQ9OnTQ1sSIiIispV58+aJ3TFHiIiIiFyLgRAREREl2JUrV+Tvv/8Wu2IgRERERAnuGsuQIYM8/fTTYlcMhIiIiChB8ufPb/u5hBgIERERUaLmEjp69KjcvHlT7IiBEBERESVIjhw5dBg9JlQ8fPiw2BEDISIiIkqQiIgI2y+1wUCIiIiIXLsKPQMhIiIiSjC7twjFe2Zpt5kwYYJud+7cCXdRiIiILKtWrVpy9epVeeihh8SOGAj50bt3b90uXboU5zprREREbtWsWTPd7IpdY0RERORaDISIiIgoUdA19ttvv8n169fFbhgIERERUaKULl1a7rvvPtmxY4fYDQMhIiIicu3IMQZCRERE5Nq5hBgIERERUaKwRYiIiIhcqwgDISIiInKrouwaIyIiIre3CJ04cUKuXbsmdsKZpYmIiChRsmbNKr169ZJ8+fLZbmkqBkJERESUKBEREbo+px2xa4yIiIhciy1CRERElGhYXgOjxgzDkLJly4pdsEWIiIiIEm327NkaAA0cOFDshIEQERERuXYuIQZCREREFLS5hA4ePCh3794Vu2AgRERERImWP39+SZ48udy8eVOOHz8uduGaQAgTPBUsWFBeeumlcBeFiIjIcVKkSKHnWbt1j7kmEBo5cqQ8+OCD4S4GERGRYxW14VIbrgiE/vrrL9mzZ480atQo3EUhIiJyrCI2TJgOeyC0du1aadq0qeTJk0dnply0aFGM+2C2ykKFCknq1KmlatWqsnnz5oCeA91ho0ePDmKpiYiIKLoWLVpoD4ydGh7CPqHi1atXpXz58tKlSxdp1apVjL/PmzdPXnjhBZk0aZIGQePGjZOGDRvK3r17JUeOHHqf+++/X/79998Yj/3+++9ly5YtUqJECd1++umnJKkTERGRGz366KO62UnYAyFEjbFFjmPHjpVnn31WOnfurNcREC1ZskSmTZsmgwYN0tt27tzp9/GbNm2SuXPnyoIFC+TKlSty+/ZtyZgxowwdOtTn/ZHtjs106dIlvcRQQDsNBwwU6obZQJ1cRzfW1W31ZV2dy031ZV2DI777DHsgFJtbt27Jtm3bZPDgwZ7bkiVLJvXq1ZONGzfGax/oEjO7xWbMmCG//fab3yDIvP+IESNi3H7mzBm5ceOGOBXeMBcvXtQ3JF5jJ3NTXd1WX9bVudxUX7vX9c8//5QjR47IQw89JGnSpAlbXS9fvmz/QOjs2bNy584dyZkzZ5TbcR3Jz6GAoAtdcd4tQpgbIXv27NqS5FR4MyJHC/W04wcvEG6qq9vqy7o6l5vqa/e63nfffXr+RkOGOZw+HHVFXrHtA6Fg69SpU5z3SZUqlW7R4QDZ8Q0ZCLwZ3VBPt9XVbfVlXZ3LTfW1c12LFCmigdChQ4ekQoUKYatrfPdn6Vc4W7ZsOkvlqVOnotyO67ly5QpbuYiIiMgZQ+gtHQhFRkZKxYoVZeXKlVGa0XC9WrVqIX1uDNkvXbq0VK5cOaTPQ0RE5CRFbTapYti7xjCSa9++fZ7rWKwNo8CyZs0qBQoU0Hydjh07SqVKlaRKlSo6fB5D7s1RZKHSu3dv3ZAjlClTppA+FxERkVMUsVmLUNgDoa1bt0rt2rU9181EZQQ/GOXVtm1bHbGFkV4nT57UOYOWLVsWI4GaiIiIwq8oW4QCU6tWLR02F5s+ffroRkRERPZoETp8+LBOdozFWK3M2qULI+QIYcPwfSIiIoqfvHnzyhtvvKEBkR0mhWQg5AdzhIiIiBI2bP3VV18Vu7D0qDEiIiKiUGKLEBEREQXViRMnZMeOHZI5c2apXr26WBlbhIiIiCio5s2bJ02aNJH3339frI6BkB+cUJGIiMj5cwkxEPIDidK7d++WLVu2hLsoREREtlKEgRARERG5PRC6cOGC/PPPP2JlDISIiIgoqNKmTetZHN3qrUIMhIiIiMi1S20wECIiIiLX5glxHiE/uMQGERFRwnXt2lUaNmwoVatWFStjIOQHl9ggIiJKuJo1a4odsGuMiIiIXIuBEBEREQUdUkuWLVsmH3/8sdy6dUusil1jREREFJJV6Fu3bi3Xrl2T+vXrS/HixcWK2CJEREREQRcREeEZOWblIfQMhPzgWmNERETOH0LPQMgPrjVGRETk/EkVGQgRERFRSLBFiIiIiFyrKFuEiIiIyK2KeLUIGYYhVsTh80RERBQShQsXllmzZmnLEAIhjCSzGgZCREREFBKRkZHSoUMHsTJ2jREREZFrMRDyg/MIERERJd5vv/0mEydOlB9++EGsiIGQH5xHiIiIKPEWL14svXr1ks8//1ysiIEQERERhYzVl9lgIEREREQhn0vIqpMqMhAiIiKikLcIHT9+XK5fvy5Ww0CIiIiIQiZr1qySKVMm/f/BgwfFahgIERERUchgEkUr5wkxECIiIiLX5glxZmkiIiIKqUGDBslzzz0nZcqUEathIEREREQhVbFiRbEqdo0RERGRazEQ8oNLbBAREQXHjRs3ZPLkydpFdvfuXbESBkJ+cIkNIiKi4EiRIoWeV99++205ceKEWAkDISIiIgp5IFSwYEFLDqFnIERERESuHULPQIiIiIhCzqqTKjIQIiIiopBjixARERG5VhG2CBEREZFbFbVoixBnliYiIqKQK1WqlKxZs8bTMmQVDISIiIgo5FKnTi2PPPKIWA27xoiIiMi1GAgRERFRkli1apUus/HNN9+IVTAQIiIioiSxcuVKXWZj6dKlYhUMhIiIiChJR45ZaQg9AyEiIiJy7RB6BkJ+TJgwQUqXLi2VK1cOd1GIiIgcocj/hs4fOnRI/v33X7ECBkJ+9O7dW3bv3i1btmwJd1GIiIgcIW/evBIZGalB0LFjx8QKGAgRERFRkkiWLJkULlzYUnlCDISIiIjItQnTDISIiIgoyYwfP16OHDkiXbt2FSvgEhtERESUZIoVK+b5/927dyXc2CJERERErsVAiIiIiJLMhQsX5JVXXpFOnTqJFTAQIiIioiSTMmVKGT16tMycOVPOnz8v4cZAiIiIiJJMunTpJFeuXJYZOcZAiIiIiFy71AYDISIiIgrLUhsMhIiIiMh1irJFiIiIiNzeInTw4MFwF4WBEBEREYWnRQgzTIcbAyEiIiJKUhUqVJBDhw7J7t27Jdy4xAYRERElqdSpU0vBggW5xAYRERFRODEQIiIioiT3+eefS/v27WXJkiUSTq4IhAoVKiTlypWT+++/X2rXrh3u4hAREbne1q1bZe7cuXoZTq7JEfrpp58kffr04S4GERERyf8fOYak6XByRYsQERERWXMuoSNhHkIf9kBo7dq10rRpU8mTJ49ERETIokWLYtxnwoQJ2r2FLPOqVavK5s2bA3oO7LdmzZpSuXJl7ZMkIiKi8DJbhLDw6tGjR90bCF29elXKly+vwY4v8+bNkxdeeEGGDRsm27dv1/s2bNhQTp8+7bkPcn/Kli0bYzt+/Lj+ff369bJt2zZZvHixjBo1Sn755Zckqx8RERHFtHr1ar28efOmtg5NnTpVwiHCMAxDLAItNwsXLpQWLVp4bkMLEFpyPvroI72OOQfy588vffv2lUGDBgX8HAMGDJAyZcpIp06dfP4dBwSb6dKlS/p858+fl4wZM4pT4XU9c+aMZM+eXZIlC3t8HFJuqqvb6su6Opeb6uuGuh47dkwKFy4cZR6h5MmT69pj+fLlC8pz4PydJUsWuXjxYqznb0snS9+6dUtbcgYPHuy5DW+KevXqycaNG+Pd4oQXOkOGDHLlyhVZtWqVtGnTxu/9R48eLSNGjIhxO96UN27cEKfCa4Q3C+Jip37w3FhXt9WXdXUuN9XXDXXdsmVLjMkU79y5oyPIIiMjg/Icly9fjtf9LB0InT17Vl+YnDlzRrkd1/fs2ROvfZw6dUpatmyp/8e+nn32WW1h8gdBF7riorcIITJ3eosQWuSc/AvEjXV1W31ZV+dyU33dUNfKlStr3aK3CFWqVEly5MgRlOdAXrHtA6FgQL/jrl274n3/VKlS6RYdDphT35AmfPDcUE+31dVt9WVdnctN9XV6XQsUKCBTpkyR7t27ayMFgqDJkyfr7cES39fO0oFQtmzZ9MVBq443XM+VK1fYykVERESJ07VrV6lfv752h6ElKJhBUCAsHWqin7BixYqycuVKz21oRsP1atWqhfS5MYqtdOnSsXajERERUcIhMbp69epBS5BOiLC3CCGBed++fZ7rBw8elJ07d0rWrFk1OkS+TseOHTVarFKliowbN04ToDt37hzScvXu3Vs35AhlypQppM9FRERELg2E0CTmvf6XmaiM4GfGjBnStm1bHbE1dOhQOXnypM4ZtGzZshgJ1ERERES2C4Rq1aqlQwRj06dPH92IiIiIXJMjFE7MESIiInI+BkJ+ID9o9+7dOukTERERORMDISIiInItBkJERETkWgyEiIiIyLUYCPnBZGkiIiLnYyDkB5OliYiInI+BEBEREblW2CdUtDpzskcsteFkWMPt8uXLkjp1aseuduzGurqtvqyrc7mpvqxrcJjn7bgmbWYgFAccIMifP3+4i0JEREQJOI/HtmZohBFXqORyiFaPHz8uGTJkkIiICHEqRM4I9o4ePSoZM2YUJ3NTXd1WX9bVudxUX9Y1OBDeIAjKkydPrK1NbBGKA168fPnyiVvgjej0D54b6+q2+rKuzuWm+rKuiRdbS5DJ2Z2PRERERLFgIERERESuxUCIVKpUqWTYsGF66XRuqqvb6su6Opeb6su6Ji0mSxMREZFrsUWIiIiIXIuBEBEREbkWAyEiIiJyLQZCRERE5FoMhFxg9OjRUrlyZZ0dO0eOHNKiRQvZu3dvrI+ZMWOGzqTtvWEtGKsbPnx4jHKXKlUq1scsWLBA74P63XffffLdd9+JXRQqVChGfbH17t3b9sd17dq10rRpU50VFuVctGhRlL9jnMfQoUMld+7ckiZNGqlXr5789ddfce53woQJ+rqh3lWrVpXNmzeLlet6+/ZtGThwoL4306VLp/d55plndMb7YH8WrHJsO3XqFKPsjz76qOOOLfj6/GIbM2aM7Y7t6Hica27cuKHfT/fcc4+kT59eWrduLadOnYp1vwn9rMcXAyEXWLNmjb7xNm3aJD/88IN+sTZo0ECuXr0a6+Mwy+eJEyc82+HDh8UOypQpE6Xc69ev93vfn376SZ588knp2rWr7NixQz+42H777Texgy1btkSpK44vPPHEE7Y/rnh/li9fXk9uvrzzzjvywQcfyKRJk+Tnn3/WIKFhw4b6RevPvHnz5IUXXtDhutu3b9f94zGnT58Wq9b12rVrWtbXXntNL7/66is9uTRr1iyonwUrHVtA4ONd9jlz5sS6TzseW/CuI7Zp06ZpYIMAwW7Hdk08zjX9+/eXb775Rn+A4v4I6Fu1ahXrfhPyWQ8Ihs+Tu5w+fRpTJhhr1qzxe5/p06cbmTJlMuxm2LBhRvny5eN9/zZt2hhNmjSJclvVqlWN7t27G3b0/PPPG0WLFjXu3r3rqOOK9+vChQs911G/XLlyGWPGjPHcduHCBSNVqlTGnDlz/O6nSpUqRu/evT3X79y5Y+TJk8cYPXq0YdW6+rJ582a93+HDh4P2WbBSfTt27Gg0b948oP045dii3nXq1In1PnY5tqejnWvwGU2ZMqWxYMECz33++OMPvc/GjRt97iOhn/VAsEXIhS5evKiXWbNmjfV+V65ckYIFC+qCeM2bN5fff/9d7ABNpmiGLlKkiLRv316OHDni974bN27UZlZv+KWB2+3m1q1b8tlnn0mXLl1iXSDYrsfV28GDB+XkyZNRjh3WFEJ3iL9jh9dn27ZtUR6DtQRx3W7HG59hHOPMmTMH7bNgNT/++KN2r5QsWVJ69uwp586d83tfpxxbdBEtWbJEW6jjYodjezHauQbHCK1E3scJXXoFChTwe5wS8lkPFAMhl7l7967069dPHnroISlbtqzf++HLB020X3/9tZ5c8bjq1avLsWPHxMrw4UAezLJly2TixIn6IapRo4auQOwLPmA5c+aMchuu43a7Qe7BhQsXNL/Cacc1OvP4BHLszp49K3fu3LH98UZ3AHKG0KUb2yKVgX4WrATdYrNmzZKVK1fK22+/rV0ojRo10uPn5GM7c+ZMza+Jq6vIDsf2ro9zDY5FZGRkjAA+tuOUkM96oLj6vMug/xb5L3H1J1erVk03E06W9957r0yePFneeOMNsSp8WZrKlSunXxho/Zg/f368fmXZ2dSpU7X++JXotONK/we/ptu0aaPJozgBOvWz0K5dO8//kSSO8hctWlRbierWrStOhR8paN2JawCDHY5t73iea6yALUIu0qdPH/n2229l9erVki9fvoAemzJlSnnggQdk3759Yif45VGiRAm/5c6VK1eMEQu4jtvtBAnPK1askP/85z+uOK7m8Qnk2GXLlk2SJ09u2+NtBkE41khEja01KCGfBStD9w+On7+y2/3Ywrp16zQJPtDPsBWPbR8/5xocC3RjouU6vscpIZ/1QDEQcgH8esQbc+HChbJq1SopXLhwwPtAs/Ovv/6qwxftBPkw+/fv91tutI6g+d0bTjLerSZ2MH36dM2naNKkiSuOK97D+BL0PnaXLl3SESX+jh2a5CtWrBjlMWi+x3WrH28zCEJeCAJeDD0O9mfBytB1ixwhf2W387H1btFFHTDCzK7H1ojjXIP64ceX93FC8If8Jn/HKSGf9YQUnByuZ8+eOlLoxx9/NE6cOOHZrl275rlPhw4djEGDBnmujxgxwli+fLmxf/9+Y9u2bUa7du2M1KlTG7///rthZS+++KLW8+DBg8aGDRuMevXqGdmyZdPRC77qifukSJHCePfdd3X0AkZjYFTDr7/+atgFRscUKFDAGDhwYIy/2fm4Xr582dixY4du+KoaO3as/t8cKfXWW28ZmTNnNr7++mvjl19+0dE2hQsXNq5fv+7ZB0bffPjhh57rc+fO1dEmM2bMMHbv3m1069ZN93Hy5EnDqnW9deuW0axZMyNfvnzGzp07o3yGb9686beucX0WrFpf/O2ll17SUUQo+4oVK4wKFSoYxYsXN27cuOGoY2u6ePGikTZtWmPixIk+92GXY9szHueaHj166PfVqlWrjK1btxrVqlXTzVvJkiWNr776ynM9Pp/1xGAg5AL48PnaMJTaVLNmTR2yaurXr5++WSMjI42cOXMajRs3NrZv325YXdu2bY3cuXNrufPmzavX9+3b57eeMH/+fKNEiRL6mDJlyhhLliwx7ASBDY7n3r17Y/zNzsd19erVPt+3Zn0wrPa1117TeuAEWLdu3RivQcGCBTW49YYTivkaYMj1pk2bDCvXFSc7f59hPM5fXeP6LFi1vjhpNmjQwMiePbv+KEG9nn322RgBjROOrWny5MlGmjRpdFi4L3Y5thKPcw2Cl169ehlZsmTR4K9ly5YaLEXfj/dj4vNZT4yI/z0pERERkeswR4iIiIhci4EQERERuRYDISIiInItBkJERETkWgyEiIiIyLUYCBEREZFrMRAiIiIi12IgREQUh4iICFm0aFG4i0FEIcBAiIgsrVOnThqIRN8effTRcBeNiBwgRbgLQEQUFwQ9WFjWW6pUqcJWHiJyDrYIEZHlIejBCtTeW5YsWfRvaB2aOHGiNGrUSNKkSSNFihSRL774Isrjf/31V6lTp47+HSu3d+vWTVfs9jZt2jQpU6aMPhdW8cYq2t7Onj0rLVu2lLRp00rx4sVl8eLFnr+dP39e2rdvL9mzZ9fnwN+jB25EZE0MhIjI9l577TVp3bq17Nq1SwOSdu3ayR9//KF/u3r1qjRs2FADpy1btsiCBQtkxYoVUQIdBFK9e/fWAAlBE4KcYsWKRXmOESNGSJs2beSXX36Rxo0b6/P8888/nuffvXu3LF26VJ8X+8uWLVsSvwpElCBBW76ViCgEsEp38uTJjXTp0kXZRo4cqX/H11iPHj2iPKZq1apGz5499f9TpkzRla6vXLni+fuSJUuMZMmSeVY0z5MnjzFkyBC/ZcBzvPrqq57r2BduW7p0qV5v2rSp0blz5yDXnIiSAnOEiMjyateura0s3rJmzer5f7Vq1aL8Ddd37typ/0cLTfny5SVdunSevz/00ENy9+5d2bt3r3atHT9+XOrWrRtrGcqVK+f5P/aVMWNGOX36tF7v2bOntkht375dGjRoIC1atJDq1asnstZElBQYCBGR5SHwiN5VFSzI6YmPlClTRrmOAArBFCA/6fDhw/Ldd9/JDz/8oEEVutrefffdkJSZiIKHOUJEZHubNm2Kcf3ee+/V/+MSuUPIFTJt2LBBkiVLJiVLlpQMGTJIoUKFZOXKlYkqAxKlO3bsKJ999pmMGzdOpkyZkqj9EVHSYIsQEVnezZs35eTJk1FuS5EihSchGQnQlSpVkocfflg+//xz2bx5s0ydOlX/hqTmYcOGaZAyfPhwOXPmjPTt21c6dOggOXPm1Pvg9h49ekiOHDm0defy5csaLOF+8TF06FCpWLGijjpDWb/99ltPIEZE1sZAiIgsb9myZTqk3Rtac/bs2eMZ0TV37lzp1auX3m/OnDlSunRp/RuGuy9fvlyef/55qVy5sl5HPs/YsWM9+0KQdOPGDXn//fflpZde0gDr8ccfj3f5IiMjZfDgwXLo0CHtaqtRo4aWh4isLwIZ0+EuBBFRQiFXZ+HChZqgTEQUKOYIERERkWsxECIiIiLXYo4QEdkae/eJKDHYIkRERESuxUCIiIiIXIuBEBEREbkWAyEiIiJyLQZCRERE5FoMhIiIiMi1GAgRERGRazEQIiIiItdiIERERETiVv8PwYBxeNy/n0cAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "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": 14, "id": "c51b1bde", "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": 15, "id": "495443f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.8079203842850902\n", "1.2520739277706872\n", "0.13887232801188593\n", "1.2606145596202416\n" ] }, { "ename": "NameError", "evalue": "name 'training_history2' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[15], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mmax\u001b[39m(training_history1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain_losses\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mmax\u001b[39m(training_history1[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_losses\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mmax\u001b[39m(\u001b[43mtraining_history2\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain_losses\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;28mmax\u001b[39m(training_history2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_losses\u001b[39m\u001b[38;5;124m'\u001b[39m]))\n", "\u001b[0;31mNameError\u001b[0m: name 'training_history2' is not defined" ] } ], "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": 16, "id": "d5e4ed8a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/1755058589.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_5224/1755058589.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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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/1755058589.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": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/1755058589.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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", 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" ] }, "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": 18, "id": "c6977b39", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using device: mps\n", "Starting training with fixed learning rate Adam for 20 epochs...\n", "Learning rate: 0.001\n", "Epoch 1, Batch 50: Loss = 1.7992, LR = 0.001000\n", "Epoch 1, Batch 100: Loss = 1.5267, LR = 0.001000\n", "Epoch 1, Batch 150: Loss = 1.4172, LR = 0.001000\n", "Epoch 1, Batch 200: Loss = 1.3653, LR = 0.001000\n", "Epoch 1, Batch 250: Loss = 1.3108, LR = 0.001000\n", "Epoch 1, Batch 300: Loss = 1.2845, LR = 0.001000\n", "Epoch 1, Batch 350: Loss = 1.2383, LR = 0.001000\n", "Epoch 1/20 completed.\n", " Train Loss: 1.1987\n", " Val Loss: 1.0732\n", " Val Accuracy: 0.6180\n", " Learning Rate: 0.001000\n", "Epoch 2, Batch 50: Loss = 1.1568, LR = 0.001000\n", "Epoch 2, Batch 100: Loss = 1.1353, LR = 0.001000\n", "Epoch 2, Batch 150: Loss = 1.1301, LR = 0.001000\n", "Epoch 2, Batch 200: Loss = 1.1256, LR = 0.001000\n", "Epoch 2, Batch 250: Loss = 1.1010, LR = 0.001000\n", "Epoch 2, Batch 300: Loss = 1.0808, LR = 0.001000\n", "Epoch 2, Batch 350: Loss = 1.0754, LR = 0.001000\n", "Epoch 2/20 completed.\n", " Train Loss: 1.0887\n", " Val Loss: 0.9387\n", " Val Accuracy: 0.6730\n", " Learning Rate: 0.001000\n", "Epoch 3, Batch 50: Loss = 1.0271, LR = 0.001000\n", "Epoch 3, Batch 100: Loss = 1.0160, LR = 0.001000\n", "Epoch 3, Batch 150: Loss = 1.0038, LR = 0.001000\n", "Epoch 3, Batch 200: Loss = 1.0065, LR = 0.001000\n", "Epoch 3, Batch 250: Loss = 1.0158, LR = 0.001000\n", "Epoch 3, Batch 300: Loss = 0.9618, LR = 0.001000\n", "Epoch 3, Batch 350: Loss = 0.9993, LR = 0.001000\n", "Epoch 3/20 completed.\n", " Train Loss: 0.9748\n", " Val Loss: 0.8303\n", " Val Accuracy: 0.7100\n", " Learning Rate: 0.001000\n", "Epoch 4, Batch 50: Loss = 0.9219, LR = 0.001000\n", "Epoch 4, Batch 100: Loss = 0.9607, LR = 0.001000\n", "Epoch 4, Batch 150: Loss = 0.9546, LR = 0.001000\n", "Epoch 4, Batch 200: Loss = 0.9704, LR = 0.001000\n", "Epoch 4, Batch 250: Loss = 0.9387, LR = 0.001000\n", "Epoch 4, Batch 300: Loss = 0.9385, LR = 0.001000\n", "Epoch 4, Batch 350: Loss = 0.9353, LR = 0.001000\n", "Epoch 4/20 completed.\n", " Train Loss: 0.9321\n", " Val Loss: 0.7967\n", " Val Accuracy: 0.7216\n", " Learning Rate: 0.001000\n", "Epoch 5, Batch 50: Loss = 0.9016, LR = 0.001000\n", "Epoch 5, Batch 100: Loss = 0.9215, LR = 0.001000\n", "Epoch 5, Batch 150: Loss = 0.9161, LR = 0.001000\n", "Epoch 5, Batch 200: Loss = 0.9186, LR = 0.001000\n", "Epoch 5, Batch 250: Loss = 0.9072, LR = 0.001000\n", "Epoch 5, Batch 300: Loss = 0.8761, LR = 0.001000\n", "Epoch 5, Batch 350: Loss = 0.9067, LR = 0.001000\n", "Epoch 5/20 completed.\n", " Train Loss: 0.8986\n", " Val Loss: 0.7529\n", " Val Accuracy: 0.7351\n", " Learning Rate: 0.001000\n", "Epoch 6, Batch 50: Loss = 0.8815, LR = 0.001000\n", "Epoch 6, Batch 100: Loss = 0.8642, LR = 0.001000\n", "Epoch 6, Batch 150: Loss = 0.8597, LR = 0.001000\n", "Epoch 6, Batch 200: Loss = 0.8696, LR = 0.001000\n", "Epoch 6, Batch 250: Loss = 0.8572, LR = 0.001000\n", "Epoch 6, Batch 300: Loss = 0.8389, LR = 0.001000\n", "Epoch 6, Batch 350: Loss = 0.8681, LR = 0.001000\n", "Epoch 6/20 completed.\n", " Train Loss: 0.8725\n", " Val Loss: 0.7591\n", " Val Accuracy: 0.7348\n", " Learning Rate: 0.001000\n", "Epoch 7, Batch 50: Loss = 0.8473, LR = 0.001000\n", "Epoch 7, Batch 100: Loss = 0.8599, LR = 0.001000\n", "Epoch 7, Batch 150: Loss = 0.8196, LR = 0.001000\n", "Epoch 7, Batch 200: Loss = 0.8245, LR = 0.001000\n", "Epoch 7, Batch 250: Loss = 0.8420, LR = 0.001000\n", "Epoch 7, Batch 300: Loss = 0.8245, LR = 0.001000\n", "Epoch 7, Batch 350: Loss = 0.8207, LR = 0.001000\n", "Epoch 7/20 completed.\n", " Train Loss: 0.8311\n", " Val Loss: 0.7496\n", " Val Accuracy: 0.7399\n", " Learning Rate: 0.001000\n", "Epoch 8, Batch 50: Loss = 0.8156, LR = 0.001000\n", "Epoch 8, Batch 100: Loss = 0.8361, LR = 0.001000\n", "Epoch 8, Batch 150: Loss = 0.8254, LR = 0.001000\n", "Epoch 8, Batch 200: Loss = 0.8177, LR = 0.001000\n", "Epoch 8, Batch 250: Loss = 0.8009, LR = 0.001000\n", "Epoch 8, Batch 300: Loss = 0.8015, LR = 0.001000\n", "Epoch 8, Batch 350: Loss = 0.7973, LR = 0.001000\n", "Epoch 8/20 completed.\n", " Train Loss: 0.7914\n", " Val Loss: 0.6866\n", " Val Accuracy: 0.7589\n", " Learning Rate: 0.001000\n", "Epoch 9, Batch 50: Loss = 0.7735, LR = 0.001000\n", "Epoch 9, Batch 100: Loss = 0.8176, LR = 0.001000\n", "Epoch 9, Batch 150: Loss = 0.7918, LR = 0.001000\n", "Epoch 9, Batch 200: Loss = 0.7896, LR = 0.001000\n", "Epoch 9, Batch 250: Loss = 0.7957, LR = 0.001000\n", "Epoch 9, Batch 300: Loss = 0.8163, LR = 0.001000\n", "Epoch 9, Batch 350: Loss = 0.7979, LR = 0.001000\n", "Epoch 9/20 completed.\n", " Train Loss: 0.7793\n", " Val Loss: 0.6844\n", " Val Accuracy: 0.7641\n", " Learning Rate: 0.001000\n", "Epoch 10, Batch 50: Loss = 0.7573, LR = 0.001000\n", "Epoch 10, Batch 100: Loss = 0.7734, LR = 0.001000\n", "Epoch 10, Batch 150: Loss = 0.7742, LR = 0.001000\n", "Epoch 10, Batch 200: Loss = 0.7880, LR = 0.001000\n", "Epoch 10, Batch 250: Loss = 0.7798, LR = 0.001000\n", "Epoch 10, Batch 300: Loss = 0.7812, LR = 0.001000\n", "Epoch 10, Batch 350: Loss = 0.7847, LR = 0.001000\n", "Epoch 10/20 completed.\n", " Train Loss: 0.7706\n", " Val Loss: 0.6613\n", " Val Accuracy: 0.7697\n", " Learning Rate: 0.001000\n", "Epoch 11, Batch 50: Loss = 0.7537, LR = 0.001000\n", "Epoch 11, Batch 100: Loss = 0.7594, LR = 0.001000\n", "Epoch 11, Batch 150: Loss = 0.7789, LR = 0.001000\n", "Epoch 11, Batch 200: Loss = 0.7343, LR = 0.001000\n", "Epoch 11, Batch 250: Loss = 0.7709, LR = 0.001000\n", "Epoch 11, Batch 300: Loss = 0.7483, LR = 0.001000\n", "Epoch 11, Batch 350: Loss = 0.7814, LR = 0.001000\n", "Epoch 11/20 completed.\n", " Train Loss: 0.7639\n", " Val Loss: 0.6578\n", " Val Accuracy: 0.7679\n", " Learning Rate: 0.001000\n", "Epoch 12, Batch 50: Loss = 0.7647, LR = 0.001000\n", "Epoch 12, Batch 100: Loss = 0.7266, LR = 0.001000\n", "Epoch 12, Batch 150: Loss = 0.7576, LR = 0.001000\n", "Epoch 12, Batch 200: Loss = 0.7247, LR = 0.001000\n", "Epoch 12, Batch 250: Loss = 0.7777, LR = 0.001000\n", "Epoch 12, Batch 300: Loss = 0.7551, LR = 0.001000\n", "Epoch 12, Batch 350: Loss = 0.7298, LR = 0.001000\n", "Epoch 12/20 completed.\n", " Train Loss: 0.7557\n", " Val Loss: 0.6488\n", " Val Accuracy: 0.7745\n", " Learning Rate: 0.001000\n", "Epoch 13, Batch 50: Loss = 0.7210, LR = 0.001000\n", "Epoch 13, Batch 100: Loss = 0.7490, LR = 0.001000\n", "Epoch 13, Batch 150: Loss = 0.7425, LR = 0.001000\n", "Epoch 13, Batch 200: Loss = 0.7306, LR = 0.001000\n", "Epoch 13, Batch 250: Loss = 0.7205, LR = 0.001000\n", "Epoch 13, Batch 300: Loss = 0.7375, LR = 0.001000\n", "Epoch 13, Batch 350: Loss = 0.7173, LR = 0.001000\n", "Epoch 13/20 completed.\n", " Train Loss: 0.7245\n", " Val Loss: 0.6295\n", " Val Accuracy: 0.7803\n", " Learning Rate: 0.001000\n", "Epoch 14, Batch 50: Loss = 0.7304, LR = 0.001000\n", "Epoch 14, Batch 100: Loss = 0.7277, LR = 0.001000\n", "Epoch 14, Batch 150: Loss = 0.7024, LR = 0.001000\n", "Epoch 14, Batch 200: Loss = 0.7136, LR = 0.001000\n", "Epoch 14, Batch 250: Loss = 0.7229, LR = 0.001000\n", "Epoch 14, Batch 300: Loss = 0.7164, LR = 0.001000\n", "Epoch 14, Batch 350: Loss = 0.7298, LR = 0.001000\n", "Epoch 14/20 completed.\n", " Train Loss: 0.7451\n", " Val Loss: 0.6094\n", " Val Accuracy: 0.7902\n", " Learning Rate: 0.001000\n", "Epoch 15, Batch 50: Loss = 0.6914, LR = 0.001000\n", "Epoch 15, Batch 100: Loss = 0.7122, LR = 0.001000\n", "Epoch 15, Batch 150: Loss = 0.7113, LR = 0.001000\n", "Epoch 15, Batch 200: Loss = 0.7220, LR = 0.001000\n", "Epoch 15, Batch 250: Loss = 0.7255, LR = 0.001000\n", "Epoch 15, Batch 300: Loss = 0.7171, LR = 0.001000\n", "Epoch 15, Batch 350: Loss = 0.7097, LR = 0.001000\n", "Epoch 15/20 completed.\n", " Train Loss: 0.6898\n", " Val Loss: 0.6352\n", " Val Accuracy: 0.7763\n", " Learning Rate: 0.001000\n", "Epoch 16, Batch 50: Loss = 0.6928, LR = 0.001000\n", "Epoch 16, Batch 100: Loss = 0.7069, LR = 0.001000\n", "Epoch 16, Batch 150: Loss = 0.7095, LR = 0.001000\n", "Epoch 16, Batch 200: Loss = 0.7185, LR = 0.001000\n", "Epoch 16, Batch 250: Loss = 0.7222, LR = 0.001000\n", "Epoch 16, Batch 300: Loss = 0.6876, LR = 0.001000\n", "Epoch 16, Batch 350: Loss = 0.6825, LR = 0.001000\n", "Epoch 16/20 completed.\n", " Train Loss: 0.7060\n", " Val Loss: 0.6230\n", " Val Accuracy: 0.7842\n", " Learning Rate: 0.001000\n", "Epoch 17, Batch 50: Loss = 0.6668, LR = 0.001000\n", "Epoch 17, Batch 100: Loss = 0.6984, LR = 0.001000\n", "Epoch 17, Batch 150: Loss = 0.7056, LR = 0.001000\n", "Epoch 17, Batch 200: Loss = 0.6943, LR = 0.001000\n", "Epoch 17, Batch 250: Loss = 0.6867, LR = 0.001000\n", "Epoch 17, Batch 300: Loss = 0.6793, LR = 0.001000\n", "Epoch 17, Batch 350: Loss = 0.7189, LR = 0.001000\n", "Epoch 17/20 completed.\n", " Train Loss: 0.7036\n", " Val Loss: 0.6045\n", " Val Accuracy: 0.7906\n", " Learning Rate: 0.001000\n", "Epoch 18, Batch 50: Loss = 0.6808, LR = 0.001000\n", "Epoch 18, Batch 100: Loss = 0.6783, LR = 0.001000\n", "Epoch 18, Batch 150: Loss = 0.6903, LR = 0.001000\n", "Epoch 18, Batch 200: Loss = 0.6747, LR = 0.001000\n", "Epoch 18, Batch 250: Loss = 0.6934, LR = 0.001000\n", "Epoch 18, Batch 300: Loss = 0.6815, LR = 0.001000\n", "Epoch 18, Batch 350: Loss = 0.6969, LR = 0.001000\n", "Epoch 18/20 completed.\n", " Train Loss: 0.6890\n", " Val Loss: 0.6134\n", " Val Accuracy: 0.7919\n", " Learning Rate: 0.001000\n", "Epoch 19, Batch 50: Loss = 0.6875, LR = 0.001000\n", "Epoch 19, Batch 100: Loss = 0.6707, LR = 0.001000\n", "Epoch 19, Batch 150: Loss = 0.6638, LR = 0.001000\n", "Epoch 19, Batch 200: Loss = 0.6852, LR = 0.001000\n", "Epoch 19, Batch 250: Loss = 0.6958, LR = 0.001000\n", "Epoch 19, Batch 300: Loss = 0.6714, LR = 0.001000\n", "Epoch 19, Batch 350: Loss = 0.6790, LR = 0.001000\n", "Epoch 19/20 completed.\n", " Train Loss: 0.6865\n", " Val Loss: 0.6037\n", " Val Accuracy: 0.7963\n", " Learning Rate: 0.001000\n", "Epoch 20, Batch 50: Loss = 0.6705, LR = 0.001000\n", "Epoch 20, Batch 100: Loss = 0.6576, LR = 0.001000\n", "Epoch 20, Batch 150: Loss = 0.6637, LR = 0.001000\n", "Epoch 20, Batch 200: Loss = 0.6497, LR = 0.001000\n", "Epoch 20, Batch 250: Loss = 0.6722, LR = 0.001000\n", "Epoch 20, Batch 300: Loss = 0.6848, LR = 0.001000\n", "Epoch 20, Batch 350: Loss = 0.6620, LR = 0.001000\n", "Epoch 20/20 completed.\n", " Train Loss: 0.6915\n", " Val Loss: 0.6125\n", " Val Accuracy: 0.7843\n", " Learning Rate: 0.001000\n" ] }, { "ename": "OSError", "evalue": "[Errno 30] Read-only file system: 'adam_fixed_lr_results.png'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[18], line 211\u001b[0m\n\u001b[1;32m 208\u001b[0m plt\u001b[38;5;241m.\u001b[39mgrid(\u001b[38;5;28;01mTrue\u001b[39;00m, alpha\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.3\u001b[39m)\n\u001b[1;32m 210\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[0;32m--> 211\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43madam_fixed_lr_results.png\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 212\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTraining complete! Results saved to \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madam_fixed_lr_results.png\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/matplotlib/pyplot.py:1228\u001b[0m, in \u001b[0;36msavefig\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 1225\u001b[0m fig \u001b[38;5;241m=\u001b[39m gcf()\n\u001b[1;32m 1226\u001b[0m \u001b[38;5;66;03m# savefig default implementation has no return, so mypy is unhappy\u001b[39;00m\n\u001b[1;32m 1227\u001b[0m \u001b[38;5;66;03m# presumably this is here because subclasses can return?\u001b[39;00m\n\u001b[0;32m-> 1228\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore[func-returns-value]\u001b[39;00m\n\u001b[1;32m 1229\u001b[0m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mdraw_idle() \u001b[38;5;66;03m# Need this if 'transparent=True', to reset colors.\u001b[39;00m\n\u001b[1;32m 1230\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/matplotlib/figure.py:3395\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m 3393\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[1;32m 3394\u001b[0m _recursively_make_axes_transparent(stack, ax)\n\u001b[0;32m-> 3395\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/matplotlib/backend_bases.py:2204\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m 2200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2201\u001b[0m \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m 2202\u001b[0m \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m 2203\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2204\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2205\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2206\u001b[0m \u001b[43m \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2207\u001b[0m \u001b[43m \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2208\u001b[0m \u001b[43m \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2209\u001b[0m \u001b[43m \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2210\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2211\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 2212\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n", "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/matplotlib/backend_bases.py:2054\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method..\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 2050\u001b[0m optional_kws \u001b[38;5;241m=\u001b[39m { \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m 2051\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, 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pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m)\n\u001b[1;32m 1675\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, (dpi, dpi))\n\u001b[0;32m-> 1676\u001b[0m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/PIL/Image.py:2576\u001b[0m, in \u001b[0;36mImage.save\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m 2574\u001b[0m fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 2575\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2576\u001b[0m fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw+b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2577\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2578\u001b[0m fp \u001b[38;5;241m=\u001b[39m cast(IO[\u001b[38;5;28mbytes\u001b[39m], fp)\n", "\u001b[0;31mOSError\u001b[0m: [Errno 30] Read-only file system: 'adam_fixed_lr_results.png'" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "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", "class SimpleNN(nn.Module):\n", " def __init__(self, hidden_size=128, dropout=0.2):\n", " super().__init__()\n", " # Simple CNN layers to handle the 2D structure and color channels\n", " self.conv_layers = nn.Sequential(\n", " nn.Conv2d(3, 32, kernel_size=3, padding=1), # 3 input channels for RGB\n", " nn.ReLU(),\n", " nn.MaxPool2d(2), # 32x32 -> 16x16\n", " nn.Conv2d(32, 64, kernel_size=3, padding=1),\n", " nn.ReLU(),\n", " nn.MaxPool2d(2), # 16x16 -> 8x8\n", " )\n", " \n", " # Fully connected layers\n", " self.fc_layers = nn.Sequential(\n", " nn.Flatten(),\n", " nn.Linear(64 * 8 * 8, hidden_size), # 64 channels * 8*8 size\n", " nn.BatchNorm1d(hidden_size),\n", " nn.ReLU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(hidden_size, 10) # 10 classes in CIFAR-10\n", " )\n", " \n", " def forward(self, x):\n", " x = self.conv_layers(x)\n", " x = self.fc_layers(x)\n", " return x\n", "\n", "# Set up data paths and hyperparameters\n", "DATA_ROOT = os.path.join(os.path.expanduser(\"~\"), \"data2\")\n", "os.makedirs(DATA_ROOT, exist_ok=True)\n", "\n", "BATCH_SIZE = 128\n", "EPOCHS = 20\n", "INIT_LR = 0.001\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.RandomHorizontalFlip(),\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # RGB channels\n", "])\n", "\n", "test_transform = transforms.Compose([\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # RGB channels\n", "])\n", "\n", "# Load Fashion MNIST dataset\n", "train_dataset = torchvision.datasets.CIFAR10(\n", " DATA_ROOT, train=True, download=True, transform=train_transform\n", ")\n", "test_dataset = torchvision.datasets.CIFAR10(\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": 25, "id": "b5c2f8c7", "metadata": {}, "outputs": [], "source": [ "for i in range(len(training_history1['val_accuracies'])):\n", " training_history2['val_accuracies'][i]=100*training_history2['val_accuracies'][i]\n", " " ] }, { "cell_type": "code", "execution_count": 34, "id": "edd55f41", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/2887544624.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": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/2887544624.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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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/2887544624.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_5224/2887544624.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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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/hh/0n3hsq2540902fc99svc3wcm0000gn/T/ipykernel_5224/2887544624.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_5224/2887544624.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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", 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" ] }, "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": 33, "id": "6915fd8d", "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", " " ] }, { "cell_type": "code", "execution_count": 42, "id": "780be41c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.6954204096361194\n", "0.08370248634187157\n", "0.686483713184915\n", "0.6029066779190981\n", "0.6771663378311109\n", "0.6036666526070124\n", "79.55\n", "76.68\n", "78.43\n" ] } ], "source": [ "print(min(training_history['train_losses']))\n", "\n", "print(min(training_history1['train_losses']))\n", "print(min(training_history2['train_losses']))\n", "\n", "print(min(training_history['val_losses']))\n", "\n", "print(min(training_history1['val_losses']))\n", "\n", "print(min(training_history2['val_losses']))\n", "\n", "print((training_history['val_accuracies'])[-1])\n", "\n", "print((training_history1['val_accuracies'])[-1])\n", "\n", "print((training_history2['val_accuracies'])[-1])\n" ] }, { "cell_type": "code", "execution_count": null, "id": "2de539b1", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }