13. ResNets/ResNets.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ResNets.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eMsndUm_XEFm"
      },
      "source": [
        "**INITIALIZATION**\n",
        "- I use these three lines of code on top of my each notebooks because it will help to prevent any problems while reloading the same project. And the third line of code helps to make visualization within the notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pwt1hyANW6LH"
      },
      "source": [
        "#@ INITIALIZATION: \n",
        "%reload_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZx_HgMkXQ0X"
      },
      "source": [
        "**LIBRARIES AND DEPENDENCIES**\n",
        "- I have downloaded all the libraries and dependencies required for the project in one particular cell."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t5mLbKDuXNu1"
      },
      "source": [
        "#@ INSTALLING DEPENDENCIES: UNCOMMENT BELOW: \n",
        "# !pip install -Uqq fastbook\n",
        "# import fastbook\n",
        "# fastbook.setup_book()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CMhC-Eu8XXqc"
      },
      "source": [
        "#@ DOWNLOADING LIBRARIES AND DEPENDENCIES: \n",
        "from fastbook import *                                  # Getting all the Libraries. \n",
        "from fastai.callback.fp16 import *\n",
        "from fastai.vision.all import *                         # Getting all the Libraries.\n",
        "#@ IGNORING WARNINGS: \n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFGgZh-PYYhp"
      },
      "source": [
        "**PREPARING THE DATASET**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "8j6zSbDfX1I7",
        "outputId": "26efed16-2233-4ced-c422-5e50de910215"
      },
      "source": [
        "#@ FUNCTION FOR PREPARING THE DATASET:\n",
        "def get_data(url, presize, resize):                                             # Defining Function. \n",
        "    path = untar_data(url)                                                      # Getting Path to the Dataset. \n",
        "    return DataBlock(blocks=(ImageBlock, CategoryBlock),                        # Initializing Data Blocks. \n",
        "                     get_items=get_image_files,                                 # Getting Image Files. \n",
        "                     splitter=GrandparentSplitter(valid_name=\"val\"),            # Splitting the Dataset. \n",
        "                     get_y=parent_label, item_tfms=Resize(presize),             # Initializing Item Transformations. \n",
        "                     batch_tfms=[*aug_transforms(min_scale=0.5, size=resize),   # Initializing Batch Transformations. \n",
        "                                 Normalize.from_stats(*imagenet_stats)],        # Normalizing the Dataset. \n",
        "                     ).dataloaders(path, bs=128)                                # Initializing Data Loaders. \n",
        "\n",
        "#@ INITIALIZING DATA LOADERS: \n",
        "dls = get_data(URLs.IMAGENETTE_160, 160, 128)                                   # Implementation of Function. \n",
        "dls.show_batch(max_n=4)                                                         # Inspecting the Data. "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqKOZ9gDcvRe"
      },
      "source": [
        "**FULLY CONVOLUTIONAL NETWORKS**\n",
        "- The idea in **Fully Convolutional Networks** is to take the average of activations across a convolutional grid. A **Fully Convolutional Networks** has a number of convolutional layers, some of which will be stride 2 convolutions at the end of which is an adaptive average pooling layer, a flatten layer to remove the unit axis and finally a linear layer. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymTNHqOdbBeb"
      },
      "source": [
        "#@ FULLY CONVOLUTIONAL NEURAL NETWORKS: BASELINE MODEL: \n",
        "def block(ni, nf): return ConvLayer(ni, nf, stride=2)        # Defining Convolutional Layer. \n",
        "def get_model():                                             # Defining Fully Convolutional Network.             \n",
        "    return nn.Sequential(block(3, 16),                       # Initializing Convolutional Layer. \n",
        "                         block(16, 32),                      # Initializing Convolutional Layer. \n",
        "                         block(32, 64),                      # Initializing Convolutional Layer. \n",
        "                         block(64, 128),                     # Initializing Convolutional Layer. \n",
        "                         block(128, 256),                    # Initializing Convolutional Layer. \n",
        "                         nn.AdaptiveAvgPool2d(1),            # Initializing Adaptive Average Pooling Layer. \n",
        "                         Flatten(),                          # Removing Unit Axis. \n",
        "                         nn.Linear(256, dls.c))              # Initializing Final Linear Layer. "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "50X2CS20iln2",
        "outputId": "11b047a4-cc28-4c9a-c0ad-1c1f02f7e872"
      },
      "source": [
        "#@ FUNCTION FOR TRAINING THE MODEL: \n",
        "def get_learner(m):                                          # Defining the Function. \n",
        "    return Learner(dls, m, loss_func=nn.CrossEntropyLoss(), \n",
        "                   metrics=accuracy).to_fp16()               # Initializing Learner.\n",
        "\n",
        "#@ IMPLEMENTATION OF FUNCTION: \n",
        "learn = get_learner(get_model())                             # Initializing the Learner. \n",
        "learn.lr_find()                                              # Getting Optimal Learning Rate. "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              ""
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SuggestedLRs(valley=0.0012022644514217973)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "EWCvIRXhjsfq",
        "outputId": "490a31a0-7bcb-4483-f864-80f82f95be49"
      },
      "source": [
        "#@ TRAINING THE MODEL: \n",
        "learn.fit_one_cycle(5, 3e-3)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.878547</td>\n",
              "      <td>1.759683</td>\n",
              "      <td>0.401019</td>\n",
              "      <td>00:35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.554601</td>\n",
              "      <td>1.779296</td>\n",
              "      <td>0.456561</td>\n",
              "      <td>00:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.306426</td>\n",
              "      <td>1.296768</td>\n",
              "      <td>0.571975</td>\n",
              "      <td>00:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.164156</td>\n",
              "      <td>1.178858</td>\n",
              "      <td>0.619108</td>\n",
              "      <td>00:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.061545</td>\n",
              "      <td>1.093980</td>\n",
              "      <td>0.652484</td>\n",
              "      <td>00:36</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCwihu1wmdcV"
      },
      "source": [
        "**IDENTITY MAPPING**\n",
        "- **Identity Mapping** is the process of returning the input without changing it at all which is performed by the identity function. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dby-oOuWqSNT"
      },
      "source": [
        "**RESIDUAL NETWORKS: RESNETS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DgYm36pQko_-"
      },
      "source": [
        "#@ DEFINING RESNET BLOCK: BASELINE: \n",
        "class ResBlock(Module):                                               # Defining ResNet Block. \n",
        "    def __init__(self, ni, nf):                                       # Initializing Constructor Function. \n",
        "        self.convs = nn.Sequential(                                   # Initializing Sequential Model. \n",
        "            ConvLayer(ni, nf),                                        # Initializing Convolutional Layer. \n",
        "            ConvLayer(nf, nf, norm_type=NormType.BatchZero))          # Initializing Gamma to Zero of Batchnorm Layer. \n",
        "    \n",
        "    def forward(self, x):                                             # Initializing Forward Propagation Function. \n",
        "        return x + self.convs(x)                                      # Initializing Skip Connection: Direct Input to Output. "
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCQNoggiukBp"
      },
      "source": [
        "**Note:**\n",
        "- The architecture defined above requires ni==nf. Incase of stride 2 convolutions, the input x and output activations will have different dimensions. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8b52M__Buf8f"
      },
      "source": [
        "#@ DEFINING RESNET BLOCK:\n",
        "def _conv_block(ni, nf, stride):                                          # Defining Convolutions. \n",
        "    return nn.Sequential(                                                 # Initializing Sequential Model. \n",
        "        ConvLayer(ni, nf, stride=stride),                                 # Initializing Convolutional Layer. \n",
        "        ConvLayer(nf, nf, act_cls=None, norm_type=NormType.BatchZero))    # Gamma to Zero of Batchnorm: gamma*y + beta. \n",
        "\n",
        "class ResBlock(Module):                                                   # Defining ResNet Block. \n",
        "    def __init__(self, ni, nf, stride=1):                                 # Initializing Constructor Function. \n",
        "        self.convs = _conv_block(ni, nf, stride)                          # Implementation of Convolutions. \n",
        "        self.idconv = noop if ni==nf else ConvLayer(ni,nf,1,act_cls=None) # Initializing Identity Mapping. \n",
        "        self.pool = noop if stride==1 else nn.AvgPool2d(2,ceil_mode=True) # Initializing Average Pooling. \n",
        "\n",
        "    def forward(self, x):                                                 # Forward Propagation Function. \n",
        "        return F.relu(self.convs(x) + self.idconv(self.pool(x)))          # Skip Connections: Direct Input to Output."
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMKd8kNKy9Cs"
      },
      "source": [
        "**Note:**\n",
        "- I have used noop function here which simply returns its input unchanged which stands for no operation. Here, idconv does nothing when ni==nf and pool does nothing when stride==1 in **Skip Connections**. I have removed **RELU** from the final convolution using act_cls and moved it after the **Skip Connection**. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "LCcBQvfHf-LR",
        "outputId": "89bae6ec-2944-447f-9baa-0277f62032d9"
      },
      "source": [
        "#@ TRAINING THE MODEL:\n",
        "def block(ni, nf):                                                        # Defining the Function. \n",
        "    return ResBlock(ni, nf, stride=2)                                     # Initializing ResNet Block. \n",
        "learn = get_learner(get_model())                                          # Initializing Learner. \n",
        "learn.fit_one_cycle(5, 3e-3)                                              # Training the Learner. "
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.958813</td>\n",
              "      <td>1.829833</td>\n",
              "      <td>0.367389</td>\n",
              "      <td>00:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.663266</td>\n",
              "      <td>1.863226</td>\n",
              "      <td>0.439490</td>\n",
              "      <td>00:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.390777</td>\n",
              "      <td>1.285444</td>\n",
              "      <td>0.591338</td>\n",
              "      <td>00:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.171478</td>\n",
              "      <td>1.115473</td>\n",
              "      <td>0.648408</td>\n",
              "      <td>00:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.040321</td>\n",
              "      <td>1.031484</td>\n",
              "      <td>0.668535</td>\n",
              "      <td>00:38</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "36SCkWEUjRII",
        "outputId": "771175f3-6d44-4bcc-e415-0028ec0939ab"
      },
      "source": [
        "#@ TRAINING THE MODEL: DEEPER MODELS: \n",
        "def block(ni, nf):                                                        # Defining the Function. \n",
        "    return nn.Sequential(ResBlock(ni, nf, stride=2), ResBlock(nf, nf))    # Initializing ResNet Block. \n",
        "learn = get_learner(get_model())                                          # Initializing Learner. \n",
        "learn.fit_one_cycle(5, 3e-3)                                              # Training the Learner. "
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.949008</td>\n",
              "      <td>1.805414</td>\n",
              "      <td>0.386752</td>\n",
              "      <td>00:41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.637782</td>\n",
              "      <td>1.498865</td>\n",
              "      <td>0.510828</td>\n",
              "      <td>00:42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.369267</td>\n",
              "      <td>1.349340</td>\n",
              "      <td>0.577834</td>\n",
              "      <td>00:42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.126183</td>\n",
              "      <td>1.061420</td>\n",
              "      <td>0.658089</td>\n",
              "      <td>00:42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.973899</td>\n",
              "      <td>0.993142</td>\n",
              "      <td>0.681783</td>\n",
              "      <td>00:41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tSC7fc9qz5Os"
      },
      "source": [
        "**STATE OF ART RESNET**\n",
        "- I will define the stem of the **ResNet** architecture which consist of convolutional layers followed by a max pooling layer. **Stem** is defined as the first few layers of **CNN**. It has different structure than the main body of CNN. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UpHHStAaq8W_",
        "outputId": "14d2b98c-3519-4eb5-b00f-3c2563bc9cef"
      },
      "source": [
        "#@ DEFINING THE STEAM OF RESNET: \n",
        "def _resnet_stem(*sizes):                                                 # Defining ResNet Stem Function. \n",
        "    return [ConvLayer(sizes[i], sizes[i+1], 3, stride=2 if i==0 else 1)   # Initializing Convolutional Layers. \n",
        "            for i in range(len(sizes)-1)] + \\\n",
        "            [nn.MaxPool2d(kernel_size=3, stride=2, padding=1)]            # Initializing Max Pooling Layer. \n",
        "\n",
        "#@ IMPLEMENTATION OF RESNET STEM: \n",
        "_resnet_stem(3, 32, 32, 64)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[ConvLayer(\n",
              "   (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
              "   (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "   (2): ReLU()\n",
              " ), ConvLayer(\n",
              "   (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "   (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "   (2): ReLU()\n",
              " ), ConvLayer(\n",
              "   (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
              "   (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
              "   (2): ReLU()\n",
              " ), MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9HdYZ2Z2fH30"
      },
      "source": [
        "#@ DEFINING THE RESNET ARCHITECTURE: \n",
        "class ResNet(nn.Sequential):                                               # Defining ResNet Block. \n",
        "    def __init__(self, n_out, layers, expansion=1):                        # Initializing Constructor Function. \n",
        "        stem = _resnet_stem(3, 32, 32, 64)                                 # Initializing ResNet Stem. \n",
        "        self.block_szs = [64, 64, 128, 256, 512]                           # Initializing ResNet Blocks. \n",
        "        for i in range(1, 5): self.block_szs[i] *= expansion\n",
        "        blocks = [self._make_layer(*o) for o in enumerate(layers)]         # Implementation of Blocks of Layers. \n",
        "        super().__init__(*stem, *blocks, nn.AdaptiveAvgPool2d(1),          # Initializing Adaptive Average Pooling Layer. \n",
        "                         Flatten(), nn.Linear(self.block_szs[-1],n_out))   # Initializing Flatten and Linear Layer. \n",
        "    \n",
        "    def _make_layer(self, idx, n_layers):                                  # Creating Series of Layers. \n",
        "        stride = 1 if idx==0 else 2                                        # Initializing Stride. \n",
        "        ch_in, ch_out = self.block_szs[idx:idx+2]                          # Initializing Channels. \n",
        "        return nn.Sequential(*[ResBlock(ch_in if i==0 else ch_out,ch_out, \n",
        "                                        stride if i==0 else 1) \\\n",
        "                               for i in range(n_layers)])                  # Initializing Sequential Model. "
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egOii3fg41ua"
      },
      "source": [
        "**Note:**\n",
        "- The **_make_layer** function creates a series of **n_layers** blocks. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "gUfGjcF-4JBY",
        "outputId": "cd566403-d7a0-4463-db77-960f185f57d4"
      },
      "source": [
        "#@ DEFINING RESNET 18:\n",
        "rn = ResNet(dls.c, [2, 2, 2, 2])                                           # Initializing ResNet Model. \n",
        "learn = get_learner(rn)                                                    # Initializing Learner. \n",
        "learn.fit_one_cycle(5, 3e-3)                                               # Training the Learner. "
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.653791</td>\n",
              "      <td>1.801401</td>\n",
              "      <td>0.462420</td>\n",
              "      <td>00:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.320263</td>\n",
              "      <td>1.421961</td>\n",
              "      <td>0.540637</td>\n",
              "      <td>00:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.084387</td>\n",
              "      <td>1.145610</td>\n",
              "      <td>0.637452</td>\n",
              "      <td>00:56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.908041</td>\n",
              "      <td>0.840922</td>\n",
              "      <td>0.729936</td>\n",
              "      <td>00:57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.761859</td>\n",
              "      <td>0.787444</td>\n",
              "      <td>0.749299</td>\n",
              "      <td>00:56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ddvwhFEx5iN5"
      },
      "source": [
        "**RESNET: BOTTLENECK LAYERS**\n",
        "- **Bottleneck Layers** use three convolutions: Two 1X1 at the begining and the end and one 3X3. The 1X1 convolutions are much faster which facilitates to use higher number of filters in and out. The 1X1 convolutions diminish and then restore the number of channels so called **Bottleneck**. The overall impact is to facilitate the use of more filters in the same amount of time. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9cRu3CqY4xZT"
      },
      "source": [
        "#@ DEFINING RESNET BLOCKS WITH BOTTLENECK: \n",
        "def _conv_block(ni, nf, stride):                                            # Defining ResNet Block. \n",
        "    return nn.Sequential(ConvLayer(ni, nf//4, 1),                           # Initializing Convolutional Layers. \n",
        "                         ConvLayer(nf//4, nf//4, stride=stride),            # Initializing Convolutional Layers.\n",
        "                         ConvLayer(nf//4, nf, 1, act_cls=None,              \n",
        "                                   norm_type=NormType.BatchZero))           # Initializing Convolutional Layers."
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHZq9S7d8IEm"
      },
      "source": [
        "#@ INITIALIZING DATALOADERS: BIGGER IMAGES: \n",
        "dls = get_data(URLs.IMAGENETTE_320, presize=320, resize=224)                # Initializing DataLoaders. "
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "v-gfcO2g8uCV",
        "outputId": "27afb62b-d8ab-420f-c21a-1b8df0ea9eef"
      },
      "source": [
        "#@ TRAINING THE MODEL: RESNET 50:\n",
        "rn = ResNet(dls.c, [3, 4, 6, 3], 4)                                         # Initializing ResNet. \n",
        "learn = get_learner(rn)                                                     # Initializing Learner. \n",
        "learn.fit_one_cycle(20, 3e-3)                                               # Training the Learner. "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>epoch</th>\n",
              "      <th>train_loss</th>\n",
              "      <th>valid_loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>1.625662</td>\n",
              "      <td>1.517696</td>\n",
              "      <td>0.506242</td>\n",
              "      <td>04:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.378292</td>\n",
              "      <td>2.124779</td>\n",
              "      <td>0.427261</td>\n",
              "      <td>04:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.275633</td>\n",
              "      <td>3.212142</td>\n",
              "      <td>0.336815</td>\n",
              "      <td>04:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.184293</td>\n",
              "      <td>3.041894</td>\n",
              "      <td>0.397962</td>\n",
              "      <td>04:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.063301</td>\n",
              "      <td>1.656148</td>\n",
              "      <td>0.520000</td>\n",
              "      <td>04:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.946526</td>\n",
              "      <td>1.692558</td>\n",
              "      <td>0.516688</td>\n",
              "      <td>04:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.854449</td>\n",
              "      <td>2.157224</td>\n",
              "      <td>0.529172</td>\n",
              "      <td>04:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.782050</td>\n",
              "      <td>1.434335</td>\n",
              "      <td>0.621401</td>\n",
              "      <td>04:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.710266</td>\n",
              "      <td>1.389060</td>\n",
              "      <td>0.621401</td>\n",
              "      <td>04:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.661915</td>\n",
              "      <td>2.337995</td>\n",
              "      <td>0.534013</td>\n",
              "      <td>04:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.594688</td>\n",
              "      <td>0.824551</td>\n",
              "      <td>0.747771</td>\n",
              "      <td>04:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.551067</td>\n",
              "      <td>0.994663</td>\n",
              "      <td>0.706752</td>\n",
              "      <td>04:40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.497955</td>\n",
              "      <td>0.863518</td>\n",
              "      <td>0.741911</td>\n",
              "      <td>04:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.457664</td>\n",
              "      <td>0.625676</td>\n",
              "      <td>0.798471</td>\n",
              "      <td>04:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.396666</td>\n",
              "      <td>0.673864</td>\n",
              "      <td>0.803822</td>\n",
              "      <td>04:39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.352256</td>\n",
              "      <td>0.515008</td>\n",
              "      <td>0.845096</td>\n",
              "      <td>04:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.323628</td>\n",
              "      <td>0.407027</td>\n",
              "      <td>0.875414</td>\n",
              "      <td>04:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.296713</td>\n",
              "      <td>0.406436</td>\n",
              "      <td>0.877452</td>\n",
              "      <td>04:36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.261624</td>\n",
              "      <td>0.409618</td>\n",
              "      <td>0.874395</td>\n",
              "      <td>04:37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.251217</td>\n",
              "      <td>0.422054</td>\n",
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