{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "id": "WsKxn6vY7caN" }, "outputs": [], "source": [ "!pip install torch torchvision tensorflow matplotlib pillow -q" ] }, { "cell_type": "code", "source": [ "# TensorFlow\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "\n", "# PyTorch\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "import torchvision\n", "import torchvision.transforms as transforms\n", "\n", "# Common\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from PIL import Image" ], "metadata": { "id": "ir0MVy4k7n7o" }, "execution_count": 8, "outputs": [] }, { "cell_type": "code", "source": [ "# TensorFlow dataset\n", "(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()\n", "\n", "print(\"Train shape:\", X_train.shape)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YGJAlyYv7rl7", "outputId": "1c82c6df-6bf1-4371-d2b0-b8fcd280ec8f" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train shape: (60000, 28, 28)\n" ] } ] }, { "cell_type": "code", "source": [ "# Normalize\n", "X_train = X_train / 255.0\n", "X_test = X_test / 255.0\n", "\n", "# Reshape for CNN\n", "X_train_tf = X_train.reshape(-1,28,28,1)\n", "X_test_tf = X_test.reshape(-1,28,28,1)" ], "metadata": { "id": "TPQeUR8S7r_J" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "model_tf = keras.Sequential([\n", " keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)),\n", " keras.layers.MaxPooling2D((2,2)),\n", "\n", " keras.layers.Conv2D(64,(3,3),activation='relu'),\n", " keras.layers.MaxPooling2D((2,2)),\n", "\n", " keras.layers.Flatten(),\n", " keras.layers.Dense(64,activation='relu'),\n", " keras.layers.Dense(10,activation='softmax')\n", "])\n", "\n", "model_tf.compile(\n", " optimizer='adam',\n", " loss='sparse_categorical_crossentropy',\n", " metrics=['accuracy']\n", ")\n", "\n", "model_tf.summary()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 353 }, "id": "ACDxgFdL7ti7", "outputId": "438de7ac-5d82-4fd6-a314-a9b79d54e2b5" }, "execution_count": 11, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" ], "text/html": [ "
Model: \"sequential_1\"\n",
              "
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              "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ conv2d_2 (Conv2D)               │ (None, 26, 26, 32)     │           320 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_2 (MaxPooling2D)  │ (None, 13, 13, 32)     │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_3 (Conv2D)               │ (None, 11, 11, 64)     │        18,496 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_3 (MaxPooling2D)  │ (None, 5, 5, 64)       │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten_1 (Flatten)             │ (None, 1600)           │             0 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_2 (Dense)                 │ (None, 64)             │       102,464 │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_3 (Dense)                 │ (None, 10)             │           650 │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "
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0.9867 - loss: 0.0413 - val_accuracy: 0.9895 - val_loss: 0.0392\n", "Epoch 4/5\n", "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9899 - loss: 0.0315 - val_accuracy: 0.9902 - val_loss: 0.0363\n", "Epoch 5/5\n", "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 4ms/step - accuracy: 0.9921 - loss: 0.0248 - val_accuracy: 0.9868 - val_loss: 0.0435\n" ] } ] }, { "cell_type": "code", "source": [ "loss, acc = model_tf.evaluate(X_test_tf, y_test)\n", "print(\"TensorFlow Test Accuracy:\", acc)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AlcPYWH77vbe", "outputId": "51187734-5b1a-4ab1-b462-061c86271dec" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 6ms/step - accuracy: 0.9887 - loss: 0.0354\n", "TensorFlow Test Accuracy: 0.9886999726295471\n" ] } ] }, { "cell_type": "code", "source": [ "transform = transforms.Compose([\n", " transforms.ToTensor()\n", "])\n", "\n", "train_dataset = torchvision.datasets.MNIST(\n", " root='./data', train=True, download=True, transform=transform\n", ")\n", "\n", "test_dataset = torchvision.datasets.MNIST(\n", " root='./data', train=False, download=True, transform=transform\n", ")\n", "\n", "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)\n", "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "smA8ZoA77wZ1", "outputId": "ebea3334-21f2-4589-9dc7-536c2bfcda93" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 9.91M/9.91M [00:00<00:00, 18.0MB/s]\n", "100%|██████████| 28.9k/28.9k [00:00<00:00, 493kB/s]\n", "100%|██████████| 1.65M/1.65M [00:00<00:00, 4.68MB/s]\n", "100%|██████████| 4.54k/4.54k [00:00<00:00, 11.2MB/s]\n" ] } ] }, { "cell_type": "code", "source": [ "class CNN(nn.Module):\n", " def __init__(self):\n", " super(CNN, self).__init__()\n", " self.conv1 = nn.Conv2d(1,32,3)\n", " self.conv2 = nn.Conv2d(32,64,3)\n", " self.pool = nn.MaxPool2d(2,2)\n", "\n", " self.fc1 = nn.Linear(64*5*5,64)\n", " self.fc2 = nn.Linear(64,10)\n", "\n", " def forward(self,x):\n", " x = self.pool(torch.relu(self.conv1(x)))\n", " x = self.pool(torch.relu(self.conv2(x)))\n", " x = x.view(-1,64*5*5)\n", " x = torch.relu(self.fc1(x))\n", " x = self.fc2(x)\n", " return x\n", "\n", "model_pt = CNN()\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = optim.Adam(model_pt.parameters(), lr=0.001)" ], "metadata": { "id": "a1zcLbgB7xki" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "for epoch in range(5):\n", " for images, labels in train_loader:\n", " outputs = model_pt(images)\n", " loss = criterion(outputs, labels)\n", "\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " print(f\"Epoch {epoch+1} done\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MPVOQcwz7ylW", "outputId": "22f79b52-7b12-4fdf-e2fe-5c4bf9b878b9" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1 done\n", "Epoch 2 done\n", "Epoch 3 done\n", "Epoch 4 done\n", "Epoch 5 done\n" ] } ] }, { "cell_type": "code", "source": [ "correct = 0\n", "total = 0\n", "\n", "with torch.no_grad():\n", " for images, labels in test_loader:\n", " outputs = model_pt(images)\n", " _, predicted = torch.max(outputs.data,1)\n", " total += labels.size(0)\n", " correct += (predicted == labels).sum().item()\n", "\n", "print(\"PyTorch Test Accuracy:\", correct/total)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xAdxIIkc7zwE", "outputId": "06307438-ab37-4b84-aadd-3a12f4c42358" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "PyTorch Test Accuracy: 0.9909\n" ] } ] }, { "cell_type": "code", "source": [ "from PIL import Image, ImageOps\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import torch\n", "\n", "img_path = \"test.jpg\"\n", "\n", "img = Image.open(img_path).convert('L')\n", "img = ImageOps.invert(img)\n", "img = img.resize((28,28))\n", "img_array = np.array(img)\n", "\n", "img_array = img_array / 255.0\n", "\n", "plt.imshow(img_array, cmap='gray')\n", "plt.title(\"Processed Image\")\n", "plt.axis('off')\n", "plt.show()\n", "\n", "img_tf = img_array.reshape(1,28,28,1)\n", "pred_tf = model_tf.predict(img_tf)\n", "print(\"TensorFlow Prediction:\", np.argmax(pred_tf))\n", "\n", "img_pt = torch.tensor(img_array).float().unsqueeze(0).unsqueeze(0)\n", "output = model_pt(img_pt)\n", "_, pred_pt = torch.max(output,1)\n", "print(\"PyTorch Prediction:\", pred_pt.item())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 480 }, "id": "vqE1BpGL716S", "outputId": "0790bd3a-91dd-4c38-a058-442ca9ae427e" }, "execution_count": 24, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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SKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAKmquRcAW0KlUik9s+2225ae6dq1a+mZdu3alZ6JiKiqKv+/6+Z8H1asWFF65sUXXyw9UxRF6Rm2PEcKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABILoj3FdOmTZvSM7W1tZ/LzO677156JiJi8ODBpWf22GOP0jP9+vUrPdOlS5fSM61bty4983las2ZN6Zm5c+eWnjnttNNKz0REvPfee5s1R9M4UgAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQKoURVE0acdKZUuvhf+Ca665pvTM+PHjS8906NCh9MwXXX19femZ5cuXl55ZvXp16ZmIiHXr1m3WXFmbc7HDXr16lZ6ZOHFi6ZmIzfsZ5xNNebl3pABAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKAKSq5l4A/10vv/xy6ZmGhobSMwsXLiw9s2jRotIzEREvvPBC6Znnnnuu9Mwbb7xReua9994rPbO5Vztt4gWNP/NjTZ48ufTMpZdeWnqmpqam9AxbniMFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgCkStHEq2xVKpUtvRaaSZ8+fUrPLFu2rPTMRx99VHqGz2bHHXcsPbM5Fzts2bJl6Zn999+/9EzE5l30kU805eXekQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAFJVcy+A5vfqq6829xJogs25KOXUqVNLz/To0aP0zPe///3SMy5s98XkSAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAKlSFEXRpB0342JcwH/PfvvtV3rm0UcfLT3zwAMPlJ4ZM2ZM6Zn169eXnuGzacrLvSMFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgVTX3AoCmGT9+fOmZFi3K/953ySWXlJ5xxdOvDkcKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIlaIoiibtWKls6bXAVqF79+6bNffss8+WnnnmmWdKzwwbNqz0TENDQ+kZPn9Nebl3pABAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgFTV3AuArc23vvWtzZrr0qVL6Znrr7++9IyL223dHCkAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACBViqIomrRjpbKl1wJfOu3bty898+c//3mzHmvNmjWlZ/bff//SM6tXry49w5dDU17uHSkAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACBVNfcC4MvsiCOOKD0zYMCAzXqscePGlZ5xcTvKcqQAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgCkSlEURZN2rFS29FqgWW3Oz/jdd99dembgwIGlZyIi9thjj9Iz9fX1m/VYfDU15eXekQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAFJVcy8Avih69OhRembo0KGlZ2bNmlV6JsLF7fh8OFIAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEByQTz4l7322qv0TLt27UrP3HvvvaVn4PPiSAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAMkF8eBfunfvXnpm7dq1pWeWLFlSegY+L44UAEiiAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQXBAP/uXNN98sPVNdXV16pm/fvqVnIiLeeOONzZqDMhwpAJBEAYAkCgAkUQAgiQIASRQASKIAQBIFAJIoAJBEAYAkCgAkUQAgiQIAqVIURdGkHSuVLb0WaFYdOnQoPbNw4cLSM++//37pmYiIoUOHlp5Zt27dZj0WX01Nebl3pABAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgFTV3AuAL4pVq1aVnjnxxBNLz+yzzz6lZyIiGhoaNmsOynCkAEASBQCSKACQRAGAJAoAJFEAIIkCAEkUAEiiAEASBQCSKACQRAGAVCmKomjSjpXKll4LAFtQU17uHSkAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBAEgUAkigAkEQBgCQKACRRACBVNXXHoii25DoA+AJwpABAEgUAkigAkEQBgCQKACRRACCJAgBJFABIogBA+n/zdEv44hKYqQAAAABJRU5ErkJggg==\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step\n", "TensorFlow Prediction: 7\n", "PyTorch Prediction: 7\n" ] } ] } ] }