mobile-gallery-image-classification-using-pytorch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mobile-gallery-image-classification-data/mobile_gallery_image_classification/train\\Cars\n",
      "mobile-gallery-image-classification-data/mobile_gallery_image_classification/train\\Memes\n",
      "mobile-gallery-image-classification-data/mobile_gallery_image_classification/train\\Mountains\n",
      "mobile-gallery-image-classification-data/mobile_gallery_image_classification/train\\Selfies\n",
      "mobile-gallery-image-classification-data/mobile_gallery_image_classification/train\\Trees\n",
      "mobile-gallery-image-classification-data/mobile_gallery_image_classification/train\\Whatsapp_Screenshots\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import os, glob, sys\n",
    "\n",
    "def q(text = ''): # Just an exit function \n",
    "    print(text)\n",
    "    sys.exit()\n",
    "\n",
    "# Input data files are available in the \"/kaggle/input/\" directory.\n",
    "\n",
    "data_dir = 'mobile-gallery-image-classification-data/mobile_gallery_image_classification/train'\n",
    "\n",
    "for class_dir in glob.glob(data_dir + os.sep + '*'):\n",
    "    print(class_dir)\n",
    "\n",
    "# Any results you write to the current directory ('/kaggle/working') are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "device:  cuda\n",
      "data_transforms:  Compose(\n",
      "    Resize(size=(224, 224), interpolation=PIL.Image.BILINEAR)\n",
      "    RandomHorizontalFlip(p=0.5)\n",
      "    ToTensor()\n",
      "    Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
      ")\n",
      "dataset:  Dataset ImageFolder\n",
      "    Number of datapoints: 1266\n",
      "    Root Location: mobile-gallery-image-classification-data/mobile_gallery_image_classification/train\n",
      "    Transforms (if any): Compose(\n",
      "                             Resize(size=(224, 224), interpolation=PIL.Image.BILINEAR)\n",
      "                             RandomHorizontalFlip(p=0.5)\n",
      "                             ToTensor()\n",
      "                             Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
      "                         )\n",
      "    Target Transforms (if any): None\n",
      "\n",
      "number of examples in train_dataset:  1012\n",
      "number of examples in val_dataset  :  254\n"
     ]
    }
   ],
   "source": [
    "# Let's start by loading in the image data\n",
    "import torch\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# Defining the transforms that we want to apply to the data.\n",
    "# Resizing the image to (224,224),\n",
    "# Randomly flipping the image horizontally(with the default probability of 0.5),\n",
    "# Converting the image to Tensore (converting the pixel values btween 0 and 1), \n",
    "# Normalizing the 3-channel data using the 'Imagenet' stats\n",
    "data_transforms = transforms.Compose([\n",
    "        transforms.Resize((224, 224)), \n",
    "        transforms.RandomHorizontalFlip(), \n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "        ])\n",
    "\n",
    "print('data_transforms: ', data_transforms)\n",
    "\n",
    "dataset = datasets.ImageFolder(data_dir, transform = data_transforms)\n",
    "print('dataset: ', dataset)\n",
    "\n",
    "# We need to split the dataset between train and val datasets\n",
    "train_percentage = 0.8\n",
    "train_size = int(len(dataset)*train_percentage)\n",
    "val_size = len(dataset) - train_size\n",
    "train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n",
    "\n",
    "print('\\nnumber of examples in train_dataset: ', len(train_dataset))\n",
    "print('number of examples in val_dataset  : ', len(val_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of batches in train_loader with a batch_size of 64: 16\n",
      "number of batches in val_loader with a batch_size of 64: 4\n"
     ]
    }
   ],
   "source": [
    "# Defining dataloaders which would return data in batches\n",
    "batch_size = 64\n",
    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size = batch_size, shuffle = True)\n",
    "val_loader   = torch.utils.data.DataLoader(val_dataset,   batch_size = batch_size, shuffle = False)\n",
    "\n",
    "print('number of batches in train_loader with a batch_size of {}: {}'.format(batch_size,  len(train_loader)))\n",
    "print('number of batches in val_loader with a batch_size of {}: {}'.format(batch_size,  len(val_loader)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset.classes:  ['Cars', 'Memes', 'Mountains', 'Selfies', 'Trees', 'Whatsapp_Screenshots']\n"
     ]
    }
   ],
   "source": [
    "# This is how we see all the classes present in the dataset. These are nothing but the names of the directories present in the 'data_dir'\n",
    "print('dataset.classes: ', dataset.classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://download.pytorch.org/models/resnet34-333f7ec4.pth\" to C:\\Users\\anime/.cache\\torch\\checkpoints\\resnet34-333f7ec4.pth\n",
      "100%|█████████████████████████████████████████████████████████████████████████████| 83.3M/83.3M [00:16<00:00, 5.26MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model.fc before:  Linear(in_features=512, out_features=1000, bias=True)\n",
      "model.fc after :  Linear(in_features=512, out_features=6, bias=True)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torchvision import models\n",
    "\n",
    "# Defining the model\n",
    "model = models.resnet34(pretrained = True)\n",
    "\n",
    "# The original architecture of resnet34 has 1000 neurons(corresponding to 1000 classes on which it was originally trained on) in the final layer.\n",
    "# So we need to change the final layer according to the number of classes that we have in our dataset\n",
    "print('model.fc before: ', model.fc)\n",
    "model_fc_in_features = model.fc.in_features\n",
    "model.fc = nn.Linear(model_fc_in_features, len(dataset.classes))\n",
    "print('model.fc after : ', model.fc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: conv1.weight has requires_grad: True\n",
      "name: bn1.weight has requires_grad: True\n",
      "name: bn1.bias has requires_grad: True\n",
      "name: layer1.0.conv1.weight has requires_grad: True\n",
      "name: layer1.0.bn1.weight has requires_grad: True\n",
      "name: layer1.0.bn1.bias has requires_grad: True\n",
      "name: layer1.0.conv2.weight has requires_grad: True\n",
      "name: layer1.0.bn2.weight has requires_grad: True\n",
      "name: layer1.0.bn2.bias has requires_grad: True\n",
      "name: layer1.1.conv1.weight has requires_grad: True\n",
      "name: layer1.1.bn1.weight has requires_grad: True\n",
      "name: layer1.1.bn1.bias has requires_grad: True\n",
      "name: layer1.1.conv2.weight has requires_grad: True\n",
      "name: layer1.1.bn2.weight has requires_grad: True\n",
      "name: layer1.1.bn2.bias has requires_grad: True\n",
      "name: layer1.2.conv1.weight has requires_grad: True\n",
      "name: layer1.2.bn1.weight has requires_grad: True\n",
      "name: layer1.2.bn1.bias has requires_grad: True\n",
      "name: layer1.2.conv2.weight has requires_grad: True\n",
      "name: layer1.2.bn2.weight has requires_grad: True\n",
      "name: layer1.2.bn2.bias has requires_grad: True\n",
      "name: layer2.0.conv1.weight has requires_grad: True\n",
      "name: layer2.0.bn1.weight has requires_grad: True\n",
      "name: layer2.0.bn1.bias has requires_grad: True\n",
      "name: layer2.0.conv2.weight has requires_grad: True\n",
      "name: layer2.0.bn2.weight has requires_grad: True\n",
      "name: layer2.0.bn2.bias has requires_grad: True\n",
      "name: layer2.0.downsample.0.weight has requires_grad: True\n",
      "name: layer2.0.downsample.1.weight has requires_grad: True\n",
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      "name: layer3.3.conv1.weight has requires_grad: True\n",
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      "name: layer3.3.bn1.bias has requires_grad: True\n",
      "name: layer3.3.conv2.weight has requires_grad: True\n",
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      "name: layer3.5.bn1.bias has requires_grad: True\n",
      "name: layer3.5.conv2.weight has requires_grad: True\n",
      "name: layer3.5.bn2.weight has requires_grad: True\n",
      "name: layer3.5.bn2.bias has requires_grad: True\n",
      "name: layer4.0.conv1.weight has requires_grad: True\n",
      "name: layer4.0.bn1.weight has requires_grad: True\n",
      "name: layer4.0.bn1.bias has requires_grad: True\n",
      "name: layer4.0.conv2.weight has requires_grad: True\n",
      "name: layer4.0.bn2.weight has requires_grad: True\n",
      "name: layer4.0.bn2.bias has requires_grad: True\n",
      "name: layer4.0.downsample.0.weight has requires_grad: True\n",
      "name: layer4.0.downsample.1.weight has requires_grad: True\n",
      "name: layer4.0.downsample.1.bias has requires_grad: True\n",
      "name: layer4.1.conv1.weight has requires_grad: True\n",
      "name: layer4.1.bn1.weight has requires_grad: True\n",
      "name: layer4.1.bn1.bias has requires_grad: True\n",
      "name: layer4.1.conv2.weight has requires_grad: True\n",
      "name: layer4.1.bn2.weight has requires_grad: True\n",
      "name: layer4.1.bn2.bias has requires_grad: True\n",
      "name: layer4.2.conv1.weight has requires_grad: True\n",
      "name: layer4.2.bn1.weight has requires_grad: True\n",
      "name: layer4.2.bn1.bias has requires_grad: True\n",
      "name: layer4.2.conv2.weight has requires_grad: True\n",
      "name: layer4.2.bn2.weight has requires_grad: True\n",
      "name: layer4.2.bn2.bias has requires_grad: True\n",
      "name: fc.weight has requires_grad: True\n",
      "name: fc.bias has requires_grad: True\n"
     ]
    }
   ],
   "source": [
    "# Now let's have a look at the requires_grad attributes for all the parameters\n",
    "\n",
    "for name, param in model.named_parameters():\n",
    "    print('name: {} has requires_grad: {}'.format(name, param.requires_grad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name:  conv1\n",
      "name:  bn1\n",
      "name:  relu\n",
      "name:  maxpool\n",
      "name:  layer1\n",
      "name:  layer2\n",
      "name:  layer3\n",
      "name:  layer4\n",
      "name:  avgpool\n",
      "name:  fc\n"
     ]
    }
   ],
   "source": [
    "# We see that all the parameters are trainable at the beginning (requires_grad = True means that the parameter is learnable)\n",
    "\n",
    "# Let's see what are the names of the layers so that we can freeze the last two of them\n",
    "for name, module in model.named_children():\n",
    "    print('name: ', name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  We would freeze all but the last few layers (layer4 and fc)\n",
    "\n",
    "for name, param in model.named_parameters():\n",
    "    if ('layer4' in name) or ('fc' in name):\n",
    "        param.requires_grad = True\n",
    "    else:\n",
    "        param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: conv1.weight has requires_grad: False\n",
      "name: bn1.weight has requires_grad: False\n",
      "name: bn1.bias has requires_grad: False\n",
      "name: layer1.0.conv1.weight has requires_grad: False\n",
      "name: layer1.0.bn1.weight has requires_grad: False\n",
      "name: layer1.0.bn1.bias has requires_grad: False\n",
      "name: layer1.0.conv2.weight has requires_grad: False\n",
      "name: layer1.0.bn2.weight has requires_grad: False\n",
      "name: layer1.0.bn2.bias has requires_grad: False\n",
      "name: layer1.1.conv1.weight has requires_grad: False\n",
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      "name: layer1.1.bn1.bias has requires_grad: False\n",
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      "name: layer1.1.bn2.weight has requires_grad: False\n",
      "name: layer1.1.bn2.bias has requires_grad: False\n",
      "name: layer1.2.conv1.weight has requires_grad: False\n",
      "name: layer1.2.bn1.weight has requires_grad: False\n",
      "name: layer1.2.bn1.bias has requires_grad: False\n",
      "name: layer1.2.conv2.weight has requires_grad: False\n",
      "name: layer1.2.bn2.weight has requires_grad: False\n",
      "name: layer1.2.bn2.bias has requires_grad: False\n",
      "name: layer2.0.conv1.weight has requires_grad: False\n",
      "name: layer2.0.bn1.weight has requires_grad: False\n",
      "name: layer2.0.bn1.bias has requires_grad: False\n",
      "name: layer2.0.conv2.weight has requires_grad: False\n",
      "name: layer2.0.bn2.weight has requires_grad: False\n",
      "name: layer2.0.bn2.bias has requires_grad: False\n",
      "name: layer2.0.downsample.0.weight has requires_grad: False\n",
      "name: layer2.0.downsample.1.weight has requires_grad: False\n",
      "name: layer2.0.downsample.1.bias has requires_grad: False\n",
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      "name: layer3.3.bn2.weight has requires_grad: False\n",
      "name: layer3.3.bn2.bias has requires_grad: False\n",
      "name: layer3.4.conv1.weight has requires_grad: False\n",
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      "name: layer3.5.bn2.weight has requires_grad: False\n",
      "name: layer3.5.bn2.bias has requires_grad: False\n",
      "name: layer4.0.conv1.weight has requires_grad: True\n",
      "name: layer4.0.bn1.weight has requires_grad: True\n",
      "name: layer4.0.bn1.bias has requires_grad: True\n",
      "name: layer4.0.conv2.weight has requires_grad: True\n",
      "name: layer4.0.bn2.weight has requires_grad: True\n",
      "name: layer4.0.bn2.bias has requires_grad: True\n",
      "name: layer4.0.downsample.0.weight has requires_grad: True\n",
      "name: layer4.0.downsample.1.weight has requires_grad: True\n",
      "name: layer4.0.downsample.1.bias has requires_grad: True\n",
      "name: layer4.1.conv1.weight has requires_grad: True\n",
      "name: layer4.1.bn1.weight has requires_grad: True\n",
      "name: layer4.1.bn1.bias has requires_grad: True\n",
      "name: layer4.1.conv2.weight has requires_grad: True\n",
      "name: layer4.1.bn2.weight has requires_grad: True\n",
      "name: layer4.1.bn2.bias has requires_grad: True\n",
      "name: layer4.2.conv1.weight has requires_grad: True\n",
      "name: layer4.2.bn1.weight has requires_grad: True\n",
      "name: layer4.2.bn1.bias has requires_grad: True\n",
      "name: layer4.2.conv2.weight has requires_grad: True\n",
      "name: layer4.2.bn2.weight has requires_grad: True\n",
      "name: layer4.2.bn2.bias has requires_grad: True\n",
      "name: fc.weight has requires_grad: True\n",
      "name: fc.bias has requires_grad: True\n"
     ]
    }
   ],
   "source": [
    "# Let's print out the requires_grad for all the parameters and make sure that the desired changes have been made\n",
    "\n",
    "for name, param in model.named_parameters():\n",
    "    print('name: {} has requires_grad: {}'.format(name, param.requires_grad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Yes, the changes have been made ! (See, the parameters present in 'layer4' and 'fc' have requires_grad = True \n",
    "# and rest all the other parameters have requires_grad = False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a trainer function which would train and validate the model\n",
    "\n",
    "def trainer(dataloader_dict, model, loss_fn, optimizer, epochs = 1, log_interval = 1):\n",
    "    print('Training started...')\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    batch_train_losses = []\n",
    "    batch_val_losses = []\n",
    "    for epoch in range(epochs):\n",
    "        print('epoch >>> {}/{}'.format(epoch + 1, epochs))\n",
    "\n",
    "        for phase in ['train', 'val']:\n",
    "            if phase == 'train':\n",
    "                print('___TRAINING___')\n",
    "                model.train()\n",
    "            else:\n",
    "                print('___VALIDATION___')\n",
    "                model.eval()\n",
    "\n",
    "            epoch_loss = 0    \n",
    "            for batch_idx, (inputs, labels) in enumerate(loader[phase]):\n",
    "                # Zero the parameter gradients\n",
    "                optimizer.zero_grad()\n",
    "\n",
    "                # forward\n",
    "                with torch.set_grad_enabled(phase == 'train'):\n",
    "                    outputs = model(inputs)\n",
    "                    batch_loss = loss_fn(outputs, labels)\n",
    "                    epoch_loss += batch_loss.item()*inputs.shape[0]\n",
    "                    \n",
    "                    # Saving the batch losses\n",
    "                    if phase == 'train':\n",
    "                        batch_train_losses.append(batch_loss.item())\n",
    "                    else:\n",
    "                        batch_val_losses.append(batch_loss.item())\n",
    "                    \n",
    "                    if phase == 'train':\n",
    "                        # Backpropagation\n",
    "                        batch_loss.backward()\n",
    "                        optimizer.step()\n",
    "\n",
    "                if (batch_idx + 1)%log_interval == 0:\n",
    "                    print('batch_loss at batch_idx {}/{}: {}'.format(str(batch_idx).zfill(len(str(len(train_loader)))), len(train_loader), batch_loss))\n",
    "\n",
    "            mean_epoch_loss = epoch_loss/len(loader[phase].dataset)\n",
    "            print('>>> {} loss at epoch {}/{}: {}'.format(phase, epoch + 1, epochs, mean_epoch_loss))\n",
    "            \n",
    "            # Storing the losses\n",
    "            if phase == 'train':\n",
    "                train_losses.append(mean_epoch_loss)\n",
    "            else:\n",
    "                val_losses.append(mean_epoch_loss)\n",
    "        \n",
    "        print('====='*5)\n",
    "        \n",
    "    return train_losses, val_losses, batch_train_losses, batch_val_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "from torch.optim import lr_scheduler\n",
    "\n",
    "# Now we define the Loss Function\n",
    "loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "# Define the optimizer\n",
    "lr = 0.00001\n",
    "optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr = lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training started...\n",
      "epoch >>> 1/5\n",
      "___TRAINING___\n",
      "batch_loss at batch_idx 01/16: 1.8830947875976562\n",
      "batch_loss at batch_idx 03/16: 1.7403203248977661\n",
      "batch_loss at batch_idx 05/16: 1.7298262119293213\n",
      "batch_loss at batch_idx 07/16: 1.5611944198608398\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/PIL/TiffImagePlugin.py:780: UserWarning: Corrupt EXIF data.  Expecting to read 4 bytes but only got 0. \n",
      "  warnings.warn(str(msg))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch_loss at batch_idx 09/16: 1.5026812553405762\n",
      "batch_loss at batch_idx 11/16: 1.3258850574493408\n",
      "batch_loss at batch_idx 13/16: 1.2495317459106445\n",
      "batch_loss at batch_idx 15/16: 1.305968165397644\n",
      ">>> train loss at epoch 1/5: 1.5462412377120007\n",
      "___VALIDATION___\n",
      "batch_loss at batch_idx 01/16: 1.1894911527633667\n",
      "batch_loss at batch_idx 03/16: 1.152886152267456\n",
      ">>> val loss at epoch 1/5: 1.1689875689078504\n",
      "=========================\n",
      "epoch >>> 2/5\n",
      "___TRAINING___\n",
      "batch_loss at batch_idx 01/16: 1.0216645002365112\n",
      "batch_loss at batch_idx 03/16: 1.0510553121566772\n",
      "batch_loss at batch_idx 05/16: 0.9224186539649963\n",
      "batch_loss at batch_idx 07/16: 0.8934431672096252\n",
      "batch_loss at batch_idx 09/16: 0.8022943735122681\n",
      "batch_loss at batch_idx 11/16: 0.8075667023658752\n",
      "batch_loss at batch_idx 13/16: 0.7715064287185669\n",
      "batch_loss at batch_idx 15/16: 0.8646692633628845\n",
      ">>> train loss at epoch 2/5: 0.8703589484154471\n",
      "___VALIDATION___\n",
      "batch_loss at batch_idx 01/16: 0.6990357637405396\n",
      "batch_loss at batch_idx 03/16: 0.6862280368804932\n",
      ">>> val loss at epoch 2/5: 0.7019397386415737\n",
      "=========================\n",
      "epoch >>> 3/5\n",
      "___TRAINING___\n",
      "batch_loss at batch_idx 01/16: 0.6628543138504028\n",
      "batch_loss at batch_idx 03/16: 0.5495110750198364\n",
      "batch_loss at batch_idx 05/16: 0.4737720787525177\n",
      "batch_loss at batch_idx 07/16: 0.5540937781333923\n",
      "batch_loss at batch_idx 09/16: 0.5418666005134583\n",
      "batch_loss at batch_idx 11/16: 0.483386754989624\n",
      "batch_loss at batch_idx 13/16: 0.4547680914402008\n",
      "batch_loss at batch_idx 15/16: 0.4926633834838867\n",
      ">>> train loss at epoch 3/5: 0.5295033304116471\n",
      "___VALIDATION___\n",
      "batch_loss at batch_idx 01/16: 0.4764443635940552\n",
      "batch_loss at batch_idx 03/16: 0.45040857791900635\n",
      ">>> val loss at epoch 3/5: 0.4676538405455942\n",
      "=========================\n",
      "epoch >>> 4/5\n",
      "___TRAINING___\n",
      "batch_loss at batch_idx 01/16: 0.3784201443195343\n",
      "batch_loss at batch_idx 03/16: 0.35650306940078735\n",
      "batch_loss at batch_idx 05/16: 0.40147092938423157\n",
      "batch_loss at batch_idx 07/16: 0.3184959292411804\n",
      "batch_loss at batch_idx 09/16: 0.32096436619758606\n",
      "batch_loss at batch_idx 11/16: 0.3386695683002472\n",
      "batch_loss at batch_idx 13/16: 0.32678791880607605\n",
      "batch_loss at batch_idx 15/16: 0.4038775861263275\n",
      ">>> train loss at epoch 4/5: 0.3578496524703361\n",
      "___VALIDATION___\n",
      "batch_loss at batch_idx 01/16: 0.3420213460922241\n",
      "batch_loss at batch_idx 03/16: 0.33064761757850647\n",
      ">>> val loss at epoch 4/5: 0.3362177138722788\n",
      "=========================\n",
      "epoch >>> 5/5\n",
      "___TRAINING___\n",
      "batch_loss at batch_idx 01/16: 0.3416098356246948\n",
      "batch_loss at batch_idx 03/16: 0.25589874386787415\n",
      "batch_loss at batch_idx 05/16: 0.2259582132101059\n",
      "batch_loss at batch_idx 07/16: 0.27338215708732605\n",
      "batch_loss at batch_idx 09/16: 0.2513640224933624\n",
      "batch_loss at batch_idx 11/16: 0.25437164306640625\n",
      "batch_loss at batch_idx 13/16: 0.22256909310817719\n",
      "batch_loss at batch_idx 15/16: 0.2105967402458191\n",
      ">>> train loss at epoch 5/5: 0.2632993612836001\n",
      "___VALIDATION___\n",
      "batch_loss at batch_idx 01/16: 0.24774116277694702\n",
      "batch_loss at batch_idx 03/16: 0.24713973701000214\n",
      ">>> val loss at epoch 5/5: 0.258364795347837\n",
      "=========================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+amZXBs2+BHzWzF4GHgA+6e79u25kFMpnaBgCEYleSjSN3H0VkQOlfefd2ufxJuDc2JYmfaUkJ3FSkQ6qikh09AvVCaSiJIfNe5rRlyIRGYrCfQJZWJzLgSMdNBxuD7sUEYlzCvcJpKL3gtnqmhGRISjcJ5AKnTEjIlFSuE8g0zLTKJmawWsKdxEZgsJ9gqkoyWWzRocUkSEo3CeYipIctjZoGAIROT6F+wSzsDgyDMG3H3+TxiMdYZcjInFK4T7BXFRRyMUVhdz59FaWfv1JvvzrV9i2X+PNiMjbWVg/iKmqqvLq6upQ1p0I3th3mLufreHXL+6ms6eHSyqKuPHd8zh7znTMBhrIU0QSgZmtd/eqIdsp3Ce2+sNt/OyPO/j56h00He3kzLJp3LhsHpeeXExykkJeJNEo3CeZ1o4uflFdx4+e28bOxlbK8jL59Hlz+WhVKZlpUQ0hJCITgMJ9kurucR7buJcfPFPDS7WHmDolleuWzOETS+dQmJMRdnkiMkoK90nO3Vm/4yA/fLaGxzbtIzUpiQ+cOZPPLpvHgqKcsMsTkRGKNtz1fT1BmRlV5XlUleexbf8RfvRcDb+oruOh6jqWLcjnrLLpzJmRyZwZWZTPyCQvK00HYkUSiPbcJ5HGIx3cu3oHD6+vo/ZgK33/6bPTU4KwjwT+nLzgfkYmxbkZJOngrEhcULeMHFd7Vze1jUfZ2XiE7ftb2XHgCDsaW9lxoJW6g610dr/1uUhLSaIsL5PyGZmU5WVRljeFzPQU0lOSSE9JIi0lifSU5OD+7dNpyUmkpwb3KUn6diAySuqWkeNKT0lmfmE28wuz37Gsq7uHPU1t7DjQyvYDR9jZ2Mr2/ZH757bsp62zZ8TrTUtOIicjhXtuWMRppdNG8xZE5DgU7vIOKclJzM7LZHZeJuctyH/bMnfnwJEOjnZ0097VQ0dXDx3dPbR3dgf3kemOrh7au7qD+55jbdu7erhv9Q5+9Nw2vnv1mSG9Q5HEp3CXYTEz8rPTR/UabZ3d3L9mJ7e+v50Zo3wtERmYxpaRcXfNOWV0dPfwUHVd2KWIJCyFu4y7BUU5nDM3j/vX7qCnRxf7FhkLCncJxbVL5lDbeJSn32wIuxSRhKRwl1C89+Ri8rPTuW/1jrBLEUlICncJRVpKEn++qJTfv1bPrkNHwy5HJOEo3CU0yxeX4cADa3aGXYpIwlG4S2hKp2dy4UmFrFhXS0fXyH8YJSLvpHCXUF27ZA77W9p5fNO+sEsRSSgKdwnVu08soHT6FO7VgVWRmFK4S6iSk4yPn1PGn2oOsKW+JexyRBKGwl1C97Gq2aQmG/et0d67SKwo3CV0+dnpXH5KCb9cX8fRju6wyxFJCAp3iQvXLplDc1sXv3l5d9iliCSEqMLdzC4zs9fNbIuZ3TxIm4+Z2SYz22hm98e2TEl0i8qnc2JRNveqa0YkJoYMdzNLBu4ALgcqgeVmVtmvzQLg/wDnuvvJwBfGoFZJYGbGNefMYUNdExvqDoVdjsiEF82e+2Jgi7vXuHsHsAK4ql+bzwJ3uPtBAHevj22ZMhl88KxZTElN1mmRIjEQTbjPAmr7TNcF8/o6ETjRzJ43s9VmdtlAL2RmN5pZtZlVNzRoNEB5u9yMVD5w5kxWvrybptbOsMsRmdCiCfeBrmjcfxDuFGABcAGwHLjbzN5xgUx3v8vdq9y9qqCgYLi1yiRwzTlzaOvs4Zcv6EIeIqMRTbjXAbP7TJcC/U9pqAP+y9073X0b8DqRsBcZllNmTeWM2dO4b80O3HUhD5GRiibc1wELzGyumaUBVwMr+7X5NfAeADPLJ9JNUxPLQmXyuHbJHLY2HOFPNQfCLkVkwhoy3N29C7gJeBTYDDzk7hvN7KtmdmXQ7FHggJltAp4C/sHd9T9TRuT9p5UwdUoq963WUMAiI5USTSN3XwWs6jfv1j6PHfhicBMZlYzUZD56dik/+eN26pvbKMzNCLskkQlHv1CVuHTNkjl09TgPrqsdurGIvIPCXeLS3PwszpufzwNrd9LdowOrIsOlcJe4de2SMnY3tfHUa/pNnMhwKdwlbl1cUURRbrrGmxEZAYW7xK2U5CSuXlTG0280UNvYGnY5IhOKwl3i2vLFZSSZcd8anRYpMhwKd4lrxVMzuLiikIeqa2nv0oU8RKKlcJe4d+2SOTQe6eB/Xt0bdikiE4bCXeLeuSfkUz4jU0MBiwyDwl3iXlJS5EIe67Yf5LW9zWGXIzIhKNxlQvjI2aWkpSRpvBmRKCncZUKYnpXG+08r4ZEXd3GkvSvsckTinsJdJoxrl8yhpb2LX7+0K+xSROKewl0mjDNnT6OyJJd7V+/UhTxEhqBwlwnDzLh2yRw272nmSw+9zGMb93K0Q+e+iwwkqvHcReLFh86axUu1B/ndq3v51Yu7yEhN4rz5BVxaWcSFFYXkZ6eHXaJIXLCwvt5WVVV5dXV1KOuWia+zu4e12xp5fNM+Ht+0j12HjmIGZ5VN55LKIi6uKGJ+YXbYZYrEnJmtd/eqIdsp3GWic3c27Wnm8U37eGLzPl7dFTkXfl5+FpdUFnFJZRFnlk0nOclCrlRk9BTuMmntPnSUJzZH9uhX1xygs9uZkZXGhQsLuaSyiGULCpiSlhx2mSIjonAXAZrbOnn69QYe37SPp16v53BbFxmpSVx1+ixuOK+chcW5YZcoMiwKd5F+Oroi/fT//cpuHnlxF22dPSw9YQY3nDuXCxcWqttGJgSFu8hxHGrt4IG1tfz8T9vZ3dRGWV4m1y8t52NVpeRkpIZdnsigFO4iUejq7uHRjfu45/ltVO84SFZaMh+tms31S8uZm58Vdnki76BwFxmmV+qauOf5bfxmw266epwLTyrkhnPncu78GZipy0big8JdZITqm9u4d81O7l+zg/0tHZxYlM0nl87lg2fOGvZZNt09zoEj7TQcbmd/SwcNh9tp7ejiytNnMi0zbYzegSQyhbvIKLV3dfObl/dwz/Pb2Li7mWmZqSxfXMZ1S+aQlZZCQ0sb9Ycjwd03vBta3prXeKSdngH+i509Zzr3feYcMlJ1SqYMj8JdJEbcnbXbGrnn+e08tmnvgGENkJpsFGSnU5DT5xZM5/eZ/1LtIf52xUtcefpMvnv1GerykWGJNtw1tozIEMyMc+bN4Jx5M6htbOW3G/ZEgjwI68KcdAqyM8idkhJVUM+ZkUXdwaN869HXmVeQxRcuPnEc3oVMNgp3kWGYnZfJX15wwqhf568uOIGahiN854k3mZufxVVnzIpBdSJv0ZC/IiEwM/7lQ6eyeG4e//CLDazf0Rh2SZJgFO4iIUlLSeIH157NzGkZ3Piz9ew80Bp2SZJAFO4iIZqelcaPP7mIrh7nUz9dR9PRzrBLkgShcBcJ2byCbP7z2rPYvv8IN93/Ap3dPWGXJAkgqnA3s8vM7HUz22JmNx+n3UfMzM1syNN0ROQtS0/I5/9+6FSefXM/X1m5UdeIlVEb8mwZM0sG7gAuAeqAdWa20t039WuXA3weWDMWhYokuo9Vzaam4Qh3Pr2VeflZfGbZvLBLkgksmj33xcAWd69x9w5gBXDVAO3+Gfgm0BbD+kQmlf/13pO47ORibl+1mSc27Qu7HJnAogn3WUBtn+m6YN4xZnYmMNvdf3u8FzKzG82s2syqGxoahl2sSKJLSjK+/edncOqsqXx+xYts3N0UdkkyQUUT7gP95O5Yh6CZJQHfBr401Au5+13uXuXuVQUFBdFXKTKJTElL5u5PVDF1Siqf/kk1+5r1ZViGL5pwrwNm95kuBXb3mc4BTgH+YGbbgSXASh1UFRm5wtwMfnT9IprbOvnMT6tp7egKuySZYKIJ93XAAjOba2ZpwNXAyt6F7t7k7vnuXu7u5cBq4Ep316hgIqNQOTOXf19+Jht3N/F3D75Ez2AjlokMYMhwd/cu4CbgUWAz8JC7bzSzr5rZlWNdoMhkdlFFEbe8r5JHN+7jG4++FnY5MoFENXCYu68CVvWbd+sgbS8YfVki0utT55ZT09DCD56uYV5+Fn++qCzskmQC0KiQInHOzLjtypPZ2djKLY+8yuzpmSydnx92WRLnNPyAyASQmpzEHdecxdz8LD5373p++sftbN7TrH54GZT23EUmiNyMVH78yUVcf89avrJyIwBTp6SyqHw6i+fmsag8j1NmTSU1WftsonAXmVBm52Xy5BfPp+7gUdZua2TttkbWbW/kic31AExJTebsOZGwXzw3jzNmT9N1WicphbvIBGNmzM7LZHZeJh8+uxSA+sNtrNt2kLXbDrBmWyPffuIN3CEtOYnTSqceC/uz50wnJyM15Hcg40EXyBZJQE2tnVTviOzZr9nWyKu7mujqcZIscv78+06dyUfOLqUgJz3sUmWYor1AtsJdZBJo7ejixZ2HWLOtkefebOCFnYdITTYurSxm+eIylp4wg6SkoS/uLeFTuIvIoLbUt7Bi7U4efqGOQ62dlOVlcvXi2Xzk7FIKczLGbL0HWtp5dXczp5dOZVpm2pitJ5Ep3EVkSG2d3Ty6cS8PrN3J6ppGUpKMSyqLWL64jPPm5496b/5ASztrtzWyuuYAq2saeX3fYQBmZKVx659VcuXpMzHTN4bhULiLyLBsbWjhwXW1PLy+jsYjHZROn8LyxWV89OxSCnOj25s/0NLOmmNhfoA39rUAkJmWTFV5Hkvm5bGgMIfvP7WFl2sPcf6JBXztA6cwOy9zLN9aQlG4i8iItHd189jGfTywdid/3HqA5CTj4opCli8uY9mCApL77M3vf9ue+cBhvmTeDE7td/59d4/zsz9t51uPvo47fOnSE/nk0nJSdI7+kBTuIjJq2/YfYcW6nTxcXceBIx3MmjaFD581i0NHO98R5ovKI0G+ZF70P6badegot/76VZ58rZ5TZ03l6x8+lZNnTh3rtzWhKdxFJGY6unp4fFNkb/65LftHHOYDcXf++5U93LZyEwdbO/jMsrl84aITmZKmH18NROEuImOi8UgHORkpMR/moKm1k3/53WZWrKulLC+T2z94CssW6Ipt/UUb7urgEpFhyctKG5Pxa6ZmpvL1D5/GA59dQnKScd2P1vLFh16i8UhHzNc1GSjcRSSuvOuEGfzub5dx03vms/Kl3Vz8b0/zyIt1hNXLMFEp3EUk7mSkJvP37z2J337+PMryMvm7B1/m+nvWUdvYGnZpE4bCXUTi1sLiXH75l0v5pytPZv32Ri799jP88Jkaurp7wi4t7incRSSuJScZ1y8t5/Evns+582dw+6rNnP+tP3D3szUcbusMu7y4pbNlRGTCcHeeer2eO5+uYe22RnLSU1h+ThmfXFrOzGlTwi5vXOhUSBFJaBvqDvHDZ7ex6pU9GPC+00r47LJ5nDIrsX8EpXAXkUmh7mAr9zy/nQfX1dLS3sW75s3gs++eywUnFibkMMYKdxGZVJrbOlmxdif3PL+dPU1tnFCQxWeXzeMDZ85KqEsNKtxFZFLq7O5h1St7uOuZGjbubiY/O43rlpRz3bvmkJcV/RjyRzu62dN0lL1NbexpamNvc1sw3U52ejIzp01h5rQpzJo2hVnTI4+z08f+yqUKdxGZ1NydP9Uc4O5nt/H71+pJT0niw2eX8unz5lKUm8HepqPs6Q3uY/dvzWs6+s4zcaZnplKUm0FLexd7m9ro6nl7fuZmpBwL/JnHbhmUBuFfmJPxtlE1R0LhLiIS2FJ/mB89t41fvrCLjq6Bz5HPz06jeGoGxblTKJmaQcm0DEr6TBdPzXhb9053j9NwuJ1dh1rZdaiN3YeOHrv1Tvf/A5GcZBTnZnDDueV8Ztm8Eb0XhbuISD/7W9r51Qt19DiRAJ8aCe7C3HTSU2LfL3+4rZM9TW3s6hP8uw+1ccFJBVx1xqwRvWa04T72HUQiInEiPzudG999writLycjlZyMVE4syhm3dfbSL1RFRBKQwl1EJAEp3EVEEpDCXUQkASncRUQSUFThbmaXmdnrZrbFzG4eYPkXzWyTmW0wsyfNbE7sSxURkWgNGe5mlgzcAVwOVALLzayyX7MXgSp3Pw14GPhmrAsVEZHoRbPnvhjY4u417t4BrACu6tvA3Z9y997rX60GSmNbpohtR8BRAAAFjElEQVSIDEc0P2KaBdT2ma4DzjlO+08DvxtogZndCNwYTLaY2evRFDmAfGD/CJ87HuK9Poj/GlXf6Ki+0Ynn+qLq9o4m3Aca5WbAMQvM7FqgCjh/oOXufhdwVzSFHbcgs+pofn4blnivD+K/RtU3OqpvdOK9vmhEE+51wOw+06XA7v6NzOxi4BbgfHdvj015IiIyEtH0ua8DFpjZXDNLA64GVvZtYGZnAj8ArnT3+tiXKSIiwzFkuLt7F3AT8CiwGXjI3Tea2VfN7Mqg2beAbOAXZvaSma0c5OViZdRdO2Ms3uuD+K9R9Y2O6hudeK9vSKEN+SsiImNHv1AVEUlACncRkQQU1+EexbAH6Wb2YLB8jZmVj2Nts83sKTPbbGYbzexvB2hzgZk1BcchXjKzW8ervmD9283slWDd77jslUV8L9h+G8zsrHGs7aQ+2+UlM2s2sy/0azPu28/Mfmxm9Wb2ap95eWb2uJm9GdxPH+S51wdt3jSz68exvm+Z2WvBv+EjZjZtkOce9/MwhvXdZma7+vw7XjHIc4/7/30M63uwT23bzeylQZ475tsvptw9Lm9AMrAVmAekAS8Dlf3a/BVwZ/D4auDBcayvBDgreJwDvDFAfRcAvw1xG24H8o+z/AoiPzgzYAmwJsR/673AnLC3H/Bu4Czg1T7zvgncHDy+GfjGAM/LA2qC++nB4+njVN+lQErw+BsD1RfN52EM67sN+PsoPgPH/f8+VvX1W/6vwK1hbb9Y3uJ5z33IYQ+C6Z8Gjx8GLjKz0V1aPEruvsfdXwgeHyZyJtHILooYnquAn3nEamCamZWEUMdFwFZ33xHCut/G3Z8BGvvN7vs5+ynwgQGe+l7gcXdvdPeDwOPAZeNRn7s/5pGz2iDk4T8G2X7RiOb/+6gdr74gOz4GPBDr9YYhnsN9oGEP+ofnsTbBh7sJmDEu1fURdAedCawZYPG7zOxlM/udmZ08roVFfkn8mJmtD4Z+6C+abTwermbw/1Bhbr9eRe6+ByJ/1IHCAdrEy7b8FIMM/8HQn4exdFPQbfTjQbq14mH7LQP2ufubgywPc/sNWzyHezTDHkQ9NMJYMbNs4JfAF9y9ud/iF4h0NZwO/Dvw6/GsDTjX3c8iMqLnX5vZu/stj4ftlwZcCfxigMVhb7/hiIdteQvQBdw3SJOhPg9j5T+BE4AzgD1Euj76C337Acs5/l57WNtvROI53KMZ9uBYGzNLAaYysq+EI2JmqUSC/T53/1X/5e7e7O4tweNVQKqZ5Y9Xfe6+O7ivBx4h8tW3r6iGlhhjlwMvuPu+/gvC3n597OvtrgruB/oVdqjbMjiA+37gGg86iPuL4vMwJtx9n7t3u3sP8MNB1hv29ksBPgQ8OFibsLbfSMVzuA857EEw3XtWwkeA3w/2wY61oH/uR8Bmd/+3QdoU9x4DMLPFRLb3gXGqL8vMcnofEzno9mq/ZiuBTwRnzSwBmnq7H8bRoHtLYW6/fvp+zq4H/muANo8Cl5rZ9KDb4dJg3pgzs8uA/01k+I/WQdpE83kYq/r6Hsf54CDrjeb/+1i6GHjN3esGWhjm9huxsI/oHu9G5GyON4gcRb8lmPdVIh9igAwiX+e3AGuBeeNY23lEvjZuAF4KblcAnwM+F7S5CdhI5Mj/amDpONY3L1jvy0ENvduvb31G5EIsW4FXiFxwZTz/fTOJhPXUPvNC3X5E/tDsATqJ7E1+mshxnCeBN4P7vKBtFXB3n+d+KvgsbgFuGMf6thDpr+79HPaeQTYTWHW8z8M41ffz4PO1gUhgl/SvL5h+x//38agvmP+T3s9dn7bjvv1iedPwAyIiCSieu2VERGSEFO4iIglI4S4ikoAU7iIiCUjhLiKSgBTuIiIJSOEuIpKA/j8kaw3DdKdXdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loader = {'train': train_loader, 'val': val_loader}\n",
    "\n",
    "epochs = 5\n",
    "log_interval = 2\n",
    "\n",
    "# Let's train the model for 5 epochs !\n",
    "train_losses, val_losses, batch_train_losses, batch_val_losses = trainer(loader, model, loss_fn, optimizer, epochs = epochs, log_interval = log_interval)\n",
    "\n",
    "# Ploting the epoch losses\n",
    "plt.plot(train_losses)\n",
    "plt.plot(val_losses)\n",
    "plt.legend(['train losses', 'val_losses'])\n",
    "plt.title('Loss vs Epoch')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(batch_train_losses)\n",
    "plt.title('batch_train_losses')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(batch_val_losses)\n",
    "plt.title('batch_val_losses')\n",
    "\n",
    "\n",
    "# Saving the model(architecture and weights)\n",
    "torch.save(model, 'stage1.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv1.weight False\n",
      "bn1.weight False\n",
      "bn1.bias False\n",
      "layer1.0.conv1.weight False\n",
      "layer1.0.bn1.weight False\n",
      "layer1.0.bn1.bias False\n",
      "layer1.0.conv2.weight False\n",
      "layer1.0.bn2.weight False\n",
      "layer1.0.bn2.bias False\n",
      "layer1.1.conv1.weight False\n",
      "layer1.1.bn1.weight False\n",
      "layer1.1.bn1.bias False\n",
      "layer1.1.conv2.weight False\n",
      "layer1.1.bn2.weight False\n",
      "layer1.1.bn2.bias False\n",
      "layer1.2.conv1.weight False\n",
      "layer1.2.bn1.weight False\n",
      "layer1.2.bn1.bias False\n",
      "layer1.2.conv2.weight False\n",
      "layer1.2.bn2.weight False\n",
      "layer1.2.bn2.bias False\n",
      "layer2.0.conv1.weight False\n",
      "layer2.0.bn1.weight False\n",
      "layer2.0.bn1.bias False\n",
      "layer2.0.conv2.weight False\n",
      "layer2.0.bn2.weight False\n",
      "layer2.0.bn2.bias False\n",
      "layer2.0.downsample.0.weight False\n",
      "layer2.0.downsample.1.weight False\n",
      "layer2.0.downsample.1.bias False\n",
      "layer2.1.conv1.weight False\n",
      "layer2.1.bn1.weight False\n",
      "layer2.1.bn1.bias False\n",
      "layer2.1.conv2.weight False\n",
      "layer2.1.bn2.weight False\n",
      "layer2.1.bn2.bias False\n",
      "layer2.2.conv1.weight False\n",
      "layer2.2.bn1.weight False\n",
      "layer2.2.bn1.bias False\n",
      "layer2.2.conv2.weight False\n",
      "layer2.2.bn2.weight False\n",
      "layer2.2.bn2.bias False\n",
      "layer2.3.conv1.weight False\n",
      "layer2.3.bn1.weight False\n",
      "layer2.3.bn1.bias False\n",
      "layer2.3.conv2.weight False\n",
      "layer2.3.bn2.weight False\n",
      "layer2.3.bn2.bias False\n",
      "layer3.0.conv1.weight False\n",
      "layer3.0.bn1.weight False\n",
      "layer3.0.bn1.bias False\n",
      "layer3.0.conv2.weight False\n",
      "layer3.0.bn2.weight False\n",
      "layer3.0.bn2.bias False\n",
      "layer3.0.downsample.0.weight False\n",
      "layer3.0.downsample.1.weight False\n",
      "layer3.0.downsample.1.bias False\n",
      "layer3.1.conv1.weight False\n",
      "layer3.1.bn1.weight False\n",
      "layer3.1.bn1.bias False\n",
      "layer3.1.conv2.weight False\n",
      "layer3.1.bn2.weight False\n",
      "layer3.1.bn2.bias False\n",
      "layer3.2.conv1.weight False\n",
      "layer3.2.bn1.weight False\n",
      "layer3.2.bn1.bias False\n",
      "layer3.2.conv2.weight False\n",
      "layer3.2.bn2.weight False\n",
      "layer3.2.bn2.bias False\n",
      "layer3.3.conv1.weight False\n",
      "layer3.3.bn1.weight False\n",
      "layer3.3.bn1.bias False\n",
      "layer3.3.conv2.weight False\n",
      "layer3.3.bn2.weight False\n",
      "layer3.3.bn2.bias False\n",
      "layer3.4.conv1.weight False\n",
      "layer3.4.bn1.weight False\n",
      "layer3.4.bn1.bias False\n",
      "layer3.4.conv2.weight False\n",
      "layer3.4.bn2.weight False\n",
      "layer3.4.bn2.bias False\n",
      "layer3.5.conv1.weight False\n",
      "layer3.5.bn1.weight False\n",
      "layer3.5.bn1.bias False\n",
      "layer3.5.conv2.weight False\n",
      "layer3.5.bn2.weight False\n",
      "layer3.5.bn2.bias False\n",
      "layer4.0.conv1.weight True\n",
      "layer4.0.bn1.weight True\n",
      "layer4.0.bn1.bias True\n",
      "layer4.0.conv2.weight True\n",
      "layer4.0.bn2.weight True\n",
      "layer4.0.bn2.bias True\n",
      "layer4.0.downsample.0.weight True\n",
      "layer4.0.downsample.1.weight True\n",
      "layer4.0.downsample.1.bias True\n",
      "layer4.1.conv1.weight True\n",
      "layer4.1.bn1.weight True\n",
      "layer4.1.bn1.bias True\n",
      "layer4.1.conv2.weight True\n",
      "layer4.1.bn2.weight True\n",
      "layer4.1.bn2.bias True\n",
      "layer4.2.conv1.weight True\n",
      "layer4.2.bn1.weight True\n",
      "layer4.2.bn1.bias True\n",
      "layer4.2.conv2.weight True\n",
      "layer4.2.bn2.weight True\n",
      "layer4.2.bn2.bias True\n",
      "fc.weight True\n",
      "fc.bias True\n"
     ]
    }
   ],
   "source": [
    "# Loading the saved model \n",
    "model = torch.load('stage1.pth')\n",
    "\n",
    "# Once we are done with loading the model, let's now have a look at the requires_grad attribute of all the parameters\n",
    "for name, param in model.named_parameters():\n",
    "    print(name, param.requires_grad)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The requires_grad settings are intact. Saving the weights using model.state_dict() and then loading \n",
    "# those weights would have reset all the requires_grad to True\n",
    "\n",
    "# For details, check out the following blog which discusses this topic in more detail\n",
    "# https://medium.com/@animesh7pointer/everything-you-need-to-know-about-saving-weights-in-pytorch-572651f3f8de?source=your_stories_page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training started...\n",
      "epoch >>> 1/3\n",
      "___TRAINING___\n",
      "batch_loss at batch_idx 01/16: 0.20289725065231323\n",
      "batch_loss at batch_idx 03/16: 0.2349197268486023\n",
      "batch_loss at batch_idx 05/16: 0.2194989025592804\n",
      "batch_loss at batch_idx 07/16: 0.20219461619853973\n",
      "batch_loss at batch_idx 09/16: 0.27012479305267334\n",
      "batch_loss at batch_idx 11/16: 0.20639048516750336\n",
      "batch_loss at batch_idx 13/16: 0.1523684412240982\n",
      "batch_loss at batch_idx 15/16: 0.14577656984329224\n",
      ">>> train loss at epoch 1/3: 0.2009116342887577\n",
      "___VALIDATION___\n",
      "batch_loss at batch_idx 01/16: 0.20299889147281647\n",
      "batch_loss at batch_idx 03/16: 0.19083364307880402\n",
      ">>> val loss at epoch 1/3: 0.20429044950196124\n",
      "=========================\n",
      "epoch >>> 2/3\n",
      "___TRAINING___\n",
      "batch_loss at batch_idx 01/16: 0.14590243995189667\n",
      "batch_loss at batch_idx 03/16: 0.10861243307590485\n",
      "batch_loss at batch_idx 05/16: 0.14622969925403595\n",
      "batch_loss at batch_idx 07/16: 0.1130327433347702\n",
      "batch_loss at batch_idx 09/16: 0.1342758983373642\n",
      "batch_loss at batch_idx 11/16: 0.13757610321044922\n",
      "batch_loss at batch_idx 13/16: 0.15501776337623596\n",
      "batch_loss at batch_idx 15/16: 0.11977922171354294\n",
      ">>> train loss at epoch 2/3: 0.14645167593310474\n",
      "___VALIDATION___\n",
      "batch_loss at batch_idx 01/16: 0.16367006301879883\n",
      "batch_loss at batch_idx 03/16: 0.16462600231170654\n",
      ">>> val loss at epoch 2/3: 0.17527046447663797\n",
      "=========================\n",
      "epoch >>> 3/3\n",
      "___TRAINING___\n",
      "batch_loss at batch_idx 01/16: 0.1762229949235916\n",
      "batch_loss at batch_idx 03/16: 0.10568083077669144\n",
      "batch_loss at batch_idx 05/16: 0.14333905279636383\n",
      "batch_loss at batch_idx 07/16: 0.08794888854026794\n",
      "batch_loss at batch_idx 09/16: 0.1599852591753006\n",
      "batch_loss at batch_idx 11/16: 0.15842339396476746\n",
      "batch_loss at batch_idx 13/16: 0.08625025302171707\n",
      "batch_loss at batch_idx 15/16: 0.12491285800933838\n",
      ">>> train loss at epoch 3/3: 0.13451774695174026\n",
      "___VALIDATION___\n",
      "batch_loss at batch_idx 01/16: 0.15565256774425507\n",
      "batch_loss at batch_idx 03/16: 0.13937778770923615\n",
      ">>> val loss at epoch 3/3: 0.15460531577819914\n",
      "=========================\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We will now freeze the 'layer4' and train just the 'fc' layer of the model for 2 more epochs\n",
    "\n",
    "for name, param in model.named_parameters():\n",
    "    if 'layer4' in name:\n",
    "        param.requires_grad = False # layer4 parameters would not get trained now\n",
    "        \n",
    "\n",
    "# Define the new learning rate and the new optimizer which would contain only the parameters with requires_grad = True \n",
    "lr = 0.0003\n",
    "optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr = lr)\n",
    "        \n",
    "epochs = 3\n",
    "log_interval = 2\n",
    "\n",
    "# Let's train the model for 3 more epochs !\n",
    "train_losses, val_losses, batch_train_losses, batch_val_losses = trainer(loader, model, loss_fn, optimizer, epochs = epochs, log_interval = log_interval)\n",
    "\n",
    "# Ploting the epoch losses\n",
    "plt.plot(train_losses)\n",
    "plt.plot(val_losses)\n",
    "plt.legend(['train losses', 'val_losses'])\n",
    "plt.title('Loss vs Epoch')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(batch_train_losses)\n",
    "plt.title('batch_train_losses')\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(batch_val_losses)\n",
    "plt.title('batch_val_losses')\n",
    "\n",
    "\n",
    "# Saving the model(architecture and weights)\n",
    "torch.save(model, 'stage2.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This Neural Network thinks that the given image belongs to >>> Memes <<< class with confidence of 95.21%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import torch.nn.functional as F\n",
    "\n",
    "# Making a 'predict' function which would take the 'model' and the path of the 'test image' as inputs, and predict the class that the test image belongs to.\n",
    "def predict(model, test_img_path):\n",
    "    img = cv2.imread(test_img_path)\n",
    "\n",
    "    # Visualizing the test image\n",
    "    plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
    "    \n",
    "    img = transforms.Compose([transforms.ToPILImage()])(img)    \n",
    "    img = data_transforms(img)\n",
    "    img = img.view(1, img.shape[0], img.shape[2], img.shape[2]) # Expanding dimension\n",
    "    \n",
    "    model.eval()\n",
    "    with torch.no_grad():\n",
    "        logits = model(img)\n",
    "        \n",
    "    probs = F.softmax(logits, dim = 1)\n",
    "    max_prob, ind = torch.max(probs, 1)\n",
    "    \n",
    "    print('This Neural Network thinks that the given image belongs to >>> {} <<< class with confidence of {}%'.format(dataset.classes[ind], round(max_prob.item()*100, 2)))\n",
    "    \n",
    "test_data_dir = 'mobile-gallery-image-classification-data/mobile_gallery_image_classification/test'\n",
    "test_img_list = []\n",
    "for class_dir in glob.glob(test_data_dir + os.sep + '*'):\n",
    "    test_img_list.append(class_dir)\n",
    "\n",
    "# Loading the trained model(architecture as well as the weights) for making inferences\n",
    "model = torch.load('stage2.pth')\n",
    "\n",
    "# Select the test image index(choose a number from 0 to 6)\n",
    "test_img_index = 3\n",
    "predict(model, test_img_list[test_img_index])"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}