![\\\"Open](\\\"https://colab.research.google.com/assets/colab-badge.svg\\\"){kind=icon}
\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Goals\\n\",\n \"\\n\",\n \"\\n\",\n \"### Learn how to use hyper parameter analyser for learning rates\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Table of Contents\\n\",\n \"\\n\",\n \"\\n\",\n \"## [Install](#0)\\n\",\n \"\\n\",\n \"\\n\",\n \"## [Load experiment in default mode](#1)\\n\",\n \"\\n\",\n \"\\n\",\n \"## [Run Analyser](#2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"# Install Monk\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Using pip (Recommended)\\n\",\n \"\\n\",\n \" - colab (gpu) \\n\",\n \" - All bakcends: `pip install -U monk-colab`\\n\",\n \" \\n\",\n \"\\n\",\n \" - kaggle (gpu) \\n\",\n \" - All backends: `pip install -U monk-kaggle`\\n\",\n \" \\n\",\n \"\\n\",\n \" - cuda 10.2\\t\\n\",\n \" - All backends: `pip install -U monk-cuda102`\\n\",\n \" - Gluon bakcned: `pip install -U monk-gluon-cuda102`\\n\",\n \"\\t - Pytorch backend: `pip install -U monk-pytorch-cuda102`\\n\",\n \" - Keras backend: `pip install -U monk-keras-cuda102`\\n\",\n \" \\n\",\n \"\\n\",\n \" - cuda 10.1\\t\\n\",\n \" - All backend: `pip install -U monk-cuda101`\\n\",\n \"\\t - Gluon bakcned: `pip install -U monk-gluon-cuda101`\\n\",\n \"\\t - Pytorch backend: `pip install -U monk-pytorch-cuda101`\\n\",\n \"\\t - Keras backend: `pip install -U monk-keras-cuda101`\\n\",\n \" \\n\",\n \"\\n\",\n \" - cuda 10.0\\t\\n\",\n \" - All backend: `pip install -U monk-cuda100`\\n\",\n \"\\t - Gluon bakcned: `pip install -U monk-gluon-cuda100`\\n\",\n \"\\t - Pytorch backend: `pip install -U monk-pytorch-cuda100`\\n\",\n \"\\t - Keras backend: `pip install -U monk-keras-cuda100`\\n\",\n \" \\n\",\n \"\\n\",\n \" - cuda 9.2\\t\\n\",\n \" - All backend: `pip install -U monk-cuda92`\\n\",\n \"\\t - Gluon bakcned: `pip install -U monk-gluon-cuda92`\\n\",\n \"\\t - Pytorch backend: `pip install -U monk-pytorch-cuda92`\\n\",\n \"\\t - Keras backend: `pip install -U monk-keras-cuda92`\\n\",\n \" \\n\",\n \"\\n\",\n \" - cuda 9.0\\t\\n\",\n \" - All backend: `pip install -U monk-cuda90`\\n\",\n \"\\t - Gluon bakcned: `pip install -U monk-gluon-cuda90`\\n\",\n \"\\t - Pytorch backend: `pip install -U monk-pytorch-cuda90`\\n\",\n \"\\t - Keras backend: `pip install -U monk-keras-cuda90`\\n\",\n \" \\n\",\n \"\\n\",\n \" - cpu \\t\\t\\n\",\n \" - All backend: `pip install -U monk-cpu`\\n\",\n \"\\t - Gluon bakcned: `pip install -U monk-gluon-cpu`\\n\",\n \"\\t - Pytorch backend: `pip install -U monk-pytorch-cpu`\\n\",\n \"\\t - Keras backend: `pip install -U monk-keras-cpu`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Install Monk Manually (Not recommended)\\n\",\n \" \\n\",\n \"### Step 1: Clone the library\\n\",\n \" - git clone https://github.com/Tessellate-Imaging/monk_v1.git\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"### Step 2: Install requirements \\n\",\n \" - Linux\\n\",\n \" - Cuda 9.0\\n\",\n \" - `cd monk_v1/installation/Linux && pip install -r requirements_cu90.txt`\\n\",\n \" - Cuda 9.2\\n\",\n \" - `cd monk_v1/installation/Linux && pip install -r requirements_cu92.txt`\\n\",\n \" - Cuda 10.0\\n\",\n \" - `cd monk_v1/installation/Linux && pip install -r requirements_cu100.txt`\\n\",\n \" - Cuda 10.1\\n\",\n \" - `cd monk_v1/installation/Linux && pip install -r requirements_cu101.txt`\\n\",\n \" - Cuda 10.2\\n\",\n \" - `cd monk_v1/installation/Linux && pip install -r requirements_cu102.txt`\\n\",\n \" - CPU (Non gpu system)\\n\",\n \" - `cd monk_v1/installation/Linux && pip install -r requirements_cpu.txt`\\n\",\n \" \\n\",\n \" \\n\",\n \" - Windows\\n\",\n \" - Cuda 9.0 (Experimental support)\\n\",\n \" - `cd monk_v1/installation/Windows && pip install -r requirements_cu90.txt`\\n\",\n \" - Cuda 9.2 (Experimental support)\\n\",\n \" - `cd monk_v1/installation/Windows && pip install -r requirements_cu92.txt`\\n\",\n \" - Cuda 10.0 (Experimental support)\\n\",\n \" - `cd monk_v1/installation/Windows && pip install -r requirements_cu100.txt`\\n\",\n \" - Cuda 10.1 (Experimental support)\\n\",\n \" - `cd monk_v1/installation/Windows && pip install -r requirements_cu101.txt`\\n\",\n \" - Cuda 10.2 (Experimental support)\\n\",\n \" - `cd monk_v1/installation/Windows && pip install -r requirements_cu102.txt`\\n\",\n \" - CPU (Non gpu system)\\n\",\n \" - `cd monk_v1/installation/Windows && pip install -r requirements_cpu.txt`\\n\",\n \" \\n\",\n \" \\n\",\n \" - Mac\\n\",\n \" - CPU (Non gpu system)\\n\",\n \" - `cd monk_v1/installation/Mac && pip install -r requirements_cpu.txt`\\n\",\n \" \\n\",\n \" \\n\",\n \" - Misc\\n\",\n \" - Colab (GPU)\\n\",\n \" - `cd monk_v1/installation/Misc && pip install -r requirements_colab.txt`\\n\",\n \" - Kaggle (GPU)\\n\",\n \" - `cd monk_v1/installation/Misc && pip install -r requirements_kaggle.txt`\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"### Step 3: Add to system path (Required for every terminal or kernel run)\\n\",\n \" - `import sys`\\n\",\n \" - `sys.path.append(\\\"monk_v1/\\\");`\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Dataset - Caltech-256\\n\",\n \" - https://www.kaggle.com/jessicali9530/caltech256\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"! wget --load-cookies /tmp/cookies.txt \\\"https://docs.google.com/uc?export=download&confirm=$(wget --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1Lltrl2U4L8WJkyBjMBFHSaoK8dLhoItl' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\\\1\\\\n/p')&id=1Lltrl2U4L8WJkyBjMBFHSaoK8dLhoItl\\\" -O caltech256.zip && rm -rf /tmp/cookies.txt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"! unzip -qq caltech256.zip\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Imports\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"#Using gluon backend \\n\",\n \"\\n\",\n \"# When installed using pip\\n\",\n \"from monk.gluon_prototype import prototype\\n\",\n \"\\n\",\n \"\\n\",\n \"# When installed manually (Uncomment the following)\\n\",\n \"#import os\\n\",\n \"#import sys\\n\",\n \"#sys.path.append(\\\"monk_v1/\\\");\\n\",\n \"#sys.path.append(\\\"monk_v1/monk/\\\");\\n\",\n \"#from monk.gluon_prototype import prototype\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"# Load experiment in default mode\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Mxnet Version: 1.5.0\\n\",\n \"\\n\",\n \"Experiment Details\\n\",\n \" Project: Project\\n\",\n \" Experiment: analyser_lr\\n\",\n \" Dir: /home/abhi/Desktop/Work/tess_tool/gui/v0.3/finetune_models/Organization/development/v5.3_roadmaps/1_getting_started_roadmap/6_hyperparameter_tuning/workspace/Project/analyser_lr/\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"gtf = prototype(verbose=1);\\n\",\n \"gtf.Prototype(\\\"Project\\\", \\\"analyser_lr\\\");\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Dataset Details\\n\",\n \" Train path: caltech256/train\\n\",\n \" Val path: None\\n\",\n \" CSV train path: None\\n\",\n \" CSV val path: None\\n\",\n \"\\n\",\n \"Dataset Params\\n\",\n \" Input Size: 224\\n\",\n \" Batch Size: 4\\n\",\n \" Data Shuffle: True\\n\",\n \" Processors: 4\\n\",\n \" Train-val split: 0.7\\n\",\n \"\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/abhi/.virtualenvs/finetune_py36/lib/python3.6/site-packages/mxnet/gluon/data/vision/datasets.py:312: UserWarning: Ignoring caltech256/train/198.spider/RENAME2 of type . Only support .jpg, .jpeg, .png\\n\",\n \" filename, ext, ', '.join(self._exts)))\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Pre-Composed Train Transforms\\n\",\n \"[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\\n\",\n \"\\n\",\n \"Pre-Composed Val Transforms\\n\",\n \"[{'RandomHorizontalFlip': {'p': 0.8}}, {'Normalize': {'mean': [0.485, 0.456, 0.406], 'std': [0.229, 0.224, 0.225]}}]\\n\",\n \"\\n\",\n \"Dataset Numbers\\n\",\n \" Num train images: 21424\\n\",\n \" Num val images: 9183\\n\",\n \" Num classes: 257\\n\",\n \"\\n\",\n \"Model Params\\n\",\n \" Model name: resnet18_v1\\n\",\n \" Use Gpu: True\\n\",\n \" Use pretrained: True\\n\",\n \" Freeze base network: False\\n\",\n \"\\n\",\n \"Model Details\\n\",\n \" Loading pretrained model\\n\",\n \" Model Loaded on device\\n\",\n \" Model name: resnet18_v1\\n\",\n \" Num of potentially trainable layers: 41\\n\",\n \" Num of actual trainable layers: 41\\n\",\n \"\\n\",\n \"Optimizer\\n\",\n \" Name: sgd\\n\",\n \" Learning rate: 0.01\\n\",\n \" Params: {'lr': 0.01, 'momentum': 0, 'weight_decay': 0, 'momentum_dampening_rate': 0, 'clipnorm': 0.0, 'clipvalue': 0.0}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Learning rate scheduler\\n\",\n \" Name: steplr\\n\",\n \" Params: {'step_size': 1, 'gamma': 0.98, 'last_epoch': -1}\\n\",\n \"\\n\",\n \"Loss\\n\",\n \" Name: softmaxcrossentropy\\n\",\n \" Params: {'weight': None, 'batch_axis': 0, 'axis_to_sum_over': -1, 'label_as_categories': True, 'label_smoothing': False}\\n\",\n \"\\n\",\n \"Training params\\n\",\n \" Num Epochs: 5\\n\",\n \"\\n\",\n \"Display params\\n\",\n \" Display progress: True\\n\",\n \" Display progress realtime: True\\n\",\n \" Save Training logs: True\\n\",\n \" Save Intermediate models: True\\n\",\n \" Intermediate model prefix: intermediate_model_\\n\",\n \"\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"monk_v1/monk/system/imports.py:160: UserWarning: ArgumentWarning: clipnorm and clipvalue are active only for keras in current version of Monk\\n\",\n \" warnings.warn(msg)\\n\",\n \"monk_v1/monk/system/imports.py:160: UserWarning: ArgumentWarning: momentum_dampening_rate is active only for pytorch in current version of Monk\\n\",\n \" warnings.warn(msg)\\n\"\n ]\n }\n ],\n \"source\": [\n \"gtf.Default(dataset_path=\\\"caltech256/train\\\", \\n\",\n \" model_name=\\\"resnet18_v1\\\", \\n\",\n \" freeze_base_network=False,\\n\",\n \" num_epochs=5);\\n\",\n \"\\n\",\n \"#Read the summary generated once you run this cell. \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"\\n\",\n \"# Analyse Learning Rates\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Analysis Project Name\\n\",\n \"analysis_name = \\\"analyse_learning_rates\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Learning rates to explore\\n\",\n \"lrs = [0.1, 0.05, 0.01, 0.005, 0.0001];\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Num epochs for each sub-experiment to run\\n\",\n \"epochs=10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Percentage of original dataset to take in for experimentation\\n\",\n \"percent_data=10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Running Learning rate analysis\\n\",\n \"Analysis Name : analyse_learning_rates\\n\",\n \"\\n\",\n \"Running experiment : 1/5\\n\",\n \"Experiment name : Learning_Rate_0.1\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/abhi/.virtualenvs/finetune_py36/lib/python3.6/site-packages/mxnet/gluon/data/vision/datasets.py:312: UserWarning: Ignoring caltech256/train/198.spider/RENAME2 of type . Only support .jpg, .jpeg, .png\\n\",\n \" filename, ext, ', '.join(self._exts)))\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Estimated time : 3 min\\n\",\n \"Experiment Complete\\n\",\n \"\\n\",\n \"\\n\",\n \"Running experiment : 2/5\\n\",\n \"Experiment name : Learning_Rate_0.05\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/abhi/.virtualenvs/finetune_py36/lib/python3.6/site-packages/mxnet/gluon/data/vision/datasets.py:312: UserWarning: Ignoring caltech256/train/198.spider/RENAME2 of type . Only support .jpg, .jpeg, .png\\n\",\n \" filename, ext, ', '.join(self._exts)))\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Estimated time : 3 min\\n\",\n \"Experiment Complete\\n\",\n \"\\n\",\n \"\\n\",\n \"Running experiment : 3/5\\n\",\n \"Experiment name : Learning_Rate_0.01\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/abhi/.virtualenvs/finetune_py36/lib/python3.6/site-packages/mxnet/gluon/data/vision/datasets.py:312: UserWarning: Ignoring caltech256/train/198.spider/RENAME2 of type . Only support .jpg, .jpeg, .png\\n\",\n \" filename, ext, ', '.join(self._exts)))\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Estimated time : 3 min\\n\",\n \"Experiment Complete\\n\",\n \"\\n\",\n \"\\n\",\n \"Running experiment : 4/5\\n\",\n \"Experiment name : Learning_Rate_0.005\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/abhi/.virtualenvs/finetune_py36/lib/python3.6/site-packages/mxnet/gluon/data/vision/datasets.py:312: UserWarning: Ignoring caltech256/train/198.spider/RENAME2 of type . Only support .jpg, .jpeg, .png\\n\",\n \" filename, ext, ', '.join(self._exts)))\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Estimated time : 3 min\\n\",\n \"Experiment Complete\\n\",\n \"\\n\",\n \"\\n\",\n \"Running experiment : 5/5\\n\",\n \"Experiment name : Learning_Rate_0.0001\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/home/abhi/.virtualenvs/finetune_py36/lib/python3.6/site-packages/mxnet/gluon/data/vision/datasets.py:312: UserWarning: Ignoring caltech256/train/198.spider/RENAME2 of type . Only support .jpg, .jpeg, .png\\n\",\n \" filename, ext, ', '.join(self._exts)))\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Estimated time : 3 min\\n\",\n \"Experiment Complete\\n\",\n \"\\n\",\n \"\\n\",\n \"Comparing Experiments\\n\",\n \"Comparison ID: Comparison_analyse_learning_rates\\n\",\n \"Generated statistics post all epochs\\n\",\n \"| Experiment Name | Train Acc | Val Acc | Train Loss | Val Loss |\\n\",\n \"|----------------------+-------------+-----------+--------------+------------|\\n\",\n \"| Learning_Rate_0.1 | 0.0746437 | 0.0639731 | 5.10316 | 5.27679 |\\n\",\n \"| Learning_Rate_0.05 | 0.358965 | 0.23569 | 2.78242 | 3.77081 |\\n\",\n \"| Learning_Rate_0.01 | 0.978995 | 0.47138 | 0.2577 | 2.3881 |\\n\",\n \"| Learning_Rate_0.005 | 0.945611 | 0.491582 | 0.599715 | 2.53947 |\\n\",\n \"| Learning_Rate_0.0001 | 0.0982746 | 0.0808081 | 5.119 | 5.32924 |\\n\",\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"