diff --git a/Chapter04/CNN_working_details.ipynb b/Chapter04/CNN_working_details.ipynb
index 06fa4bc..e93d5aa 100644
--- a/Chapter04/CNN_working_details.ipynb
+++ b/Chapter04/CNN_working_details.ipynb
@@ -1,398 +1,407 @@
{
- "nbformat": 4,
- "nbformat_minor": 0,
- "metadata": {
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "colab_type": "text",
+ "id": "view-in-github"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "nJnTbZVIF55X"
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from torch import nn\n",
+ "from torch.utils.data import TensorDataset, Dataset, DataLoader\n",
+ "from torch.optim import SGD, Adam\n",
+ "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+ "from torchvision import datasets\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "CW87aBN_F7h5"
+ },
+ "outputs": [],
+ "source": [
+ "X_train = torch.tensor([[[[1,2,3,4],[2,3,4,5],[5,6,7,8],[1,3,4,5]]],[[[-1,2,3,-4],[2,-3,4,5],[-5,6,-7,8],[-1,-3,-4,-5]]]]).to(device).float()\n",
+ "X_train /= 8\n",
+ "y_train = torch.tensor([0,1]).to(device).float()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WDr-XS8HF_UG"
+ },
+ "outputs": [],
+ "source": [
+ "def get_model():\n",
+ " model = nn.Sequential(\n",
+ " nn.Conv2d(1, 1, kernel_size=3),\n",
+ " nn.MaxPool2d(2),\n",
+ " nn.ReLU(),\n",
+ " nn.Flatten(),\n",
+ " nn.Linear(1, 1),\n",
+ " nn.Sigmoid(),\n",
+ " ).to(device)\n",
+ " loss_fn = nn.BCELoss()\n",
+ " optimizer = Adam(model.parameters(), lr=1e-2)\n",
+ " return model, loss_fn, optimizer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
"colab": {
- "name": "CNN_working_details.ipynb",
- "provenance": [],
- "include_colab_link": true
+ "base_uri": "https://localhost:8080/",
+ "height": 474
},
- "kernelspec": {
- "name": "python3",
- "display_name": "Python 3"
+ "id": "alInllQdGC13",
+ "outputId": "3ab3e397-93c2-4f14-f325-6fb304bf2d66"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Collecting torch_summary\n",
+ " Downloading https://files.pythonhosted.org/packages/83/49/f9db57bcad7246591b93519fd8e5166c719548c45945ef7d2fc9fcba46fb/torch_summary-1.4.3-py3-none-any.whl\n",
+ "Installing collected packages: torch-summary\n",
+ "Successfully installed torch-summary-1.4.3\n",
+ "==========================================================================================\n",
+ "Layer (type:depth-idx) Output Shape Param #\n",
+ "==========================================================================================\n",
+ "├─Conv2d: 1-1 [-1, 1, 2, 2] 10\n",
+ "├─MaxPool2d: 1-2 [-1, 1, 1, 1] --\n",
+ "├─ReLU: 1-3 [-1, 1, 1, 1] --\n",
+ "├─Flatten: 1-4 [-1, 1] --\n",
+ "├─Linear: 1-5 [-1, 1] 2\n",
+ "├─Sigmoid: 1-6 [-1, 1] --\n",
+ "==========================================================================================\n",
+ "Total params: 12\n",
+ "Trainable params: 12\n",
+ "Non-trainable params: 0\n",
+ "Total mult-adds (M): 0.00\n",
+ "==========================================================================================\n",
+ "Input size (MB): 0.00\n",
+ "Forward/backward pass size (MB): 0.00\n",
+ "Params size (MB): 0.00\n",
+ "Estimated Total Size (MB): 0.00\n",
+ "==========================================================================================\n"
+ ]
}
+ ],
+ "source": [
+ "!pip install torch_summary\n",
+ "from torchsummary import summary\n",
+ "model, loss_fn, optimizer = get_model()\n",
+ "summary(model, X_train);"
+ ]
},
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "view-in-github",
- "colab_type": "text"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "nJnTbZVIF55X"
- },
- "source": [
- "import torch\n",
- "from torch import nn\n",
- "from torch.utils.data import TensorDataset, Dataset, DataLoader\n",
- "from torch.optim import SGD, Adam\n",
- "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
- "from torchvision import datasets\n",
- "import numpy as np\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib inline"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "CW87aBN_F7h5"
- },
- "source": [
- "X_train = torch.tensor([[[[1,2,3,4],[2,3,4,5],[5,6,7,8],[1,3,4,5]]],[[[-1,2,3,-4],[2,-3,4,5],[-5,6,-7,8],[-1,-3,-4,-5]]]]).to(device).float()\n",
- "X_train /= 8\n",
- "y_train = torch.tensor([0,1]).to(device).float()"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "WDr-XS8HF_UG"
- },
- "source": [
- "def get_model():\n",
- " model = nn.Sequential(\n",
- " nn.Conv2d(1, 1, kernel_size=3),\n",
- " nn.MaxPool2d(2),\n",
- " nn.ReLU(),\n",
- " nn.Flatten(),\n",
- " nn.Linear(1, 1),\n",
- " nn.Sigmoid(),\n",
- " ).to(device)\n",
- " loss_fn = nn.BCELoss()\n",
- " optimizer = Adam(model.parameters(), lr=1e-2)\n",
- " return model, loss_fn, optimizer"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "alInllQdGC13",
- "outputId": "3ab3e397-93c2-4f14-f325-6fb304bf2d66",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 474
- }
- },
- "source": [
- "!pip install torch_summary\n",
- "from torchsummary import summary\n",
- "model, loss_fn, optimizer = get_model()\n",
- "summary(model, X_train);"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "Collecting torch_summary\n",
- " Downloading https://files.pythonhosted.org/packages/83/49/f9db57bcad7246591b93519fd8e5166c719548c45945ef7d2fc9fcba46fb/torch_summary-1.4.3-py3-none-any.whl\n",
- "Installing collected packages: torch-summary\n",
- "Successfully installed torch-summary-1.4.3\n",
- "==========================================================================================\n",
- "Layer (type:depth-idx) Output Shape Param #\n",
- "==========================================================================================\n",
- "├─Conv2d: 1-1 [-1, 1, 2, 2] 10\n",
- "├─MaxPool2d: 1-2 [-1, 1, 1, 1] --\n",
- "├─ReLU: 1-3 [-1, 1, 1, 1] --\n",
- "├─Flatten: 1-4 [-1, 1] --\n",
- "├─Linear: 1-5 [-1, 1] 2\n",
- "├─Sigmoid: 1-6 [-1, 1] --\n",
- "==========================================================================================\n",
- "Total params: 12\n",
- "Trainable params: 12\n",
- "Non-trainable params: 0\n",
- "Total mult-adds (M): 0.00\n",
- "==========================================================================================\n",
- "Input size (MB): 0.00\n",
- "Forward/backward pass size (MB): 0.00\n",
- "Params size (MB): 0.00\n",
- "Estimated Total Size (MB): 0.00\n",
- "==========================================================================================\n"
- ],
- "name": "stdout"
- }
- ]
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "NqnAmC52GEz0"
- },
- "source": [
- "def train_batch(x, y, model, opt, loss_fn):\n",
- " model.train()\n",
- " prediction = model(x)\n",
- " batch_loss = loss_fn(prediction, y)\n",
- " batch_loss.backward()\n",
- " optimizer.step()\n",
- " optimizer.zero_grad()\n",
- " return batch_loss.item()"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "GPFRvgAlGIbp"
- },
- "source": [
- "trn_dl = DataLoader(TensorDataset(X_train, y_train))"
- ],
- "execution_count": null,
- "outputs": []
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NqnAmC52GEz0"
+ },
+ "outputs": [],
+ "source": [
+ "def train_batch(x, y, model, opt, loss_fn):\n",
+ " model.train()\n",
+ " prediction = model(x)\n",
+ " batch_loss = loss_fn(prediction.squeeze(0), y)\n",
+ " batch_loss.backward()\n",
+ " opt.step()\n",
+ " opt.zero_grad()\n",
+ " return batch_loss.item()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GPFRvgAlGIbp"
+ },
+ "outputs": [],
+ "source": [
+ "trn_dl = DataLoader(TensorDataset(X_train, y_train))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 74
},
+ "id": "yHALwqudGJzh",
+ "outputId": "1104beb4-326c-4ad7-8952-a5056c73bf9b"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "metadata": {
- "id": "yHALwqudGJzh",
- "outputId": "1104beb4-326c-4ad7-8952-a5056c73bf9b",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 74
- }
- },
- "source": [
- "for epoch in range(2000):\n",
- " for ix, batch in enumerate(iter(trn_dl)):\n",
- " x, y = batch\n",
- " batch_loss = train_batch(x, y, model, optimizer, loss_fn)"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([1, 1])) is deprecated. Please ensure they have the same size.\n",
- " return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)\n"
- ],
- "name": "stderr"
- }
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py:529: UserWarning: Using a target size (torch.Size([1])) that is different to the input size (torch.Size([1, 1])) is deprecated. Please ensure they have the same size.\n",
+ " return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction)\n"
+ ]
+ }
+ ],
+ "source": [
+ "for epoch in range(2000):\n",
+ " for ix, batch in enumerate(iter(trn_dl)):\n",
+ " x, y = batch\n",
+ " batch_loss = train_batch(x, y, model, optimizer, loss_fn)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 35
},
+ "id": "YMEA3dcUGMA2",
+ "outputId": "db7abd8c-c17a-4b1c-9f4d-37795420c05f"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "metadata": {
- "id": "YMEA3dcUGMA2",
- "outputId": "db7abd8c-c17a-4b1c-9f4d-37795420c05f",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- }
- },
- "source": [
- "model(X_train[:1])"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "tensor([[0.0042]], grad_fn=)"
- ]
- },
- "metadata": {
- "tags": []
- },
- "execution_count": 8
- }
+ "data": {
+ "text/plain": [
+ "tensor([[0.0042]], grad_fn=)"
]
+ },
+ "execution_count": 8,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model(X_train[:1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "eHErutP4GNxI"
+ },
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 126
},
+ "id": "Zw-Ou9J4Gail",
+ "outputId": "3b16cd39-beef-429a-947d-3d2cc86d0398"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "metadata": {
- "id": "eHErutP4GNxI"
- },
- "source": [
- ""
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "Zw-Ou9J4Gail",
- "outputId": "3b16cd39-beef-429a-947d-3d2cc86d0398",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 126
- }
- },
- "source": [
- "list(model.children())"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "[Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1)),\n",
- " MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),\n",
- " ReLU(),\n",
- " Flatten(),\n",
- " Linear(in_features=1, out_features=1, bias=True),\n",
- " Sigmoid()]"
- ]
- },
- "metadata": {
- "tags": []
- },
- "execution_count": 9
- }
+ "data": {
+ "text/plain": [
+ "[Conv2d(1, 1, kernel_size=(3, 3), stride=(1, 1)),\n",
+ " MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False),\n",
+ " ReLU(),\n",
+ " Flatten(),\n",
+ " Linear(in_features=1, out_features=1, bias=True),\n",
+ " Sigmoid()]"
]
+ },
+ "execution_count": 9,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "list(model.children())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3ACidFxRGa_0"
+ },
+ "outputs": [],
+ "source": [
+ "(cnn_w, cnn_b), (lin_w, lin_b) = [(layer.weight.data, layer.bias.data) for layer in list(model.children()) if hasattr(layer, 'weight')]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Lj-SiW6sGcyy"
+ },
+ "outputs": [],
+ "source": [
+ "h_im, w_im = X_train.shape[2:]\n",
+ "h_conv, w_conv = cnn_w.shape[2:]\n",
+ "sumprod = torch.zeros((h_im - h_conv + 1, w_im - w_conv + 1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "RPPgrk51GgL1"
+ },
+ "outputs": [],
+ "source": [
+ "for i in range(h_im - h_conv + 1):\n",
+ " for j in range(w_im - w_conv + 1):\n",
+ " img_subset = X_train[0, 0, i:(i+3), j:(j+3)]\n",
+ " model_filter = cnn_w.reshape(3,3)\n",
+ " val = torch.sum(img_subset*model_filter) + cnn_b\n",
+ " sumprod[i,j] = val"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 54
},
+ "id": "GqaZ2WlpGjTN",
+ "outputId": "b6610f5d-d2eb-499e-e8d0-a0c2db5f9b74"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "metadata": {
- "id": "3ACidFxRGa_0"
- },
- "source": [
- "(cnn_w, cnn_b), (lin_w, lin_b) = [(layer.weight.data, layer.bias.data) for layer in list(model.children()) if hasattr(layer, 'weight')]"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "Lj-SiW6sGcyy"
- },
- "source": [
- "h_im, w_im = X_train.shape[2:]\n",
- "h_conv, w_conv = cnn_w.shape[2:]\n",
- "sumprod = torch.zeros((h_im - h_conv + 1, w_im - w_conv + 1))"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "RPPgrk51GgL1"
- },
- "source": [
- "for i in range(h_im - h_conv + 1):\n",
- " for j in range(w_im - w_conv + 1):\n",
- " img_subset = X_train[0, 0, i:(i+3), j:(j+3)]\n",
- " model_filter = cnn_w.reshape(3,3)\n",
- " val = torch.sum(img_subset*model_filter) + cnn_b\n",
- " sumprod[i,j] = val"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "GqaZ2WlpGjTN",
- "outputId": "b6610f5d-d2eb-499e-e8d0-a0c2db5f9b74",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 54
- }
- },
- "source": [
- "sumprod.clamp_min_(0)"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "tensor([[0., 0.],\n",
- " [0., 0.]])"
- ]
- },
- "metadata": {
- "tags": []
- },
- "execution_count": 13
- }
+ "data": {
+ "text/plain": [
+ "tensor([[0., 0.],\n",
+ " [0., 0.]])"
]
+ },
+ "execution_count": 13,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sumprod.clamp_min_(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "vpb3vZvuGkyX"
+ },
+ "outputs": [],
+ "source": [
+ "pooling_layer_output = torch.max(sumprod)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "NfeX-EYuGmJ3"
+ },
+ "outputs": [],
+ "source": [
+ "intermediate_output_value = pooling_layer_output * lin_w + lin_b"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 72
},
+ "id": "rwA6srUgGniP",
+ "outputId": "8893187a-7f02-4e23-e6ea-6eb7f1239594"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "metadata": {
- "id": "vpb3vZvuGkyX"
- },
- "source": [
- "pooling_layer_output = torch.max(sumprod)"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "NfeX-EYuGmJ3"
- },
- "source": [
- "intermediate_output_value = pooling_layer_output * lin_w + lin_b"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "rwA6srUgGniP",
- "outputId": "8893187a-7f02-4e23-e6ea-6eb7f1239594",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 72
- }
- },
- "source": [
- "from torch.nn import functional as F # torch library for numpy like functions\n",
- "print(F.sigmoid(intermediate_output_value))"
- ],
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "text": [
- "tensor([[0.0042]])\n"
- ],
- "name": "stdout"
- },
- {
- "output_type": "stream",
- "text": [
- "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1625: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
- " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
- ],
- "name": "stderr"
- }
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "tensor([[0.0042]])\n"
+ ]
},
{
- "cell_type": "code",
- "metadata": {
- "id": "h6QhLf0xGpIE"
- },
- "source": [
- ""
- ],
- "execution_count": null,
- "outputs": []
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1625: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
+ " warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
+ ]
}
- ]
-}
\ No newline at end of file
+ ],
+ "source": [
+ "from torch.nn import functional as F # torch library for numpy like functions\n",
+ "print(F.sigmoid(intermediate_output_value))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "h6QhLf0xGpIE"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "include_colab_link": true,
+ "name": "CNN_working_details.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}