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Diffstat (limited to 'docs')
| -rw-r--r-- | docs/pytorch_vol2.ipynb | 388 | ||||
| -rw-r--r-- | docs/researchML.ipynb | 1953 |
2 files changed, 2341 insertions, 0 deletions
diff --git a/docs/pytorch_vol2.ipynb b/docs/pytorch_vol2.ipynb new file mode 100644 index 00000000..7f931149 --- /dev/null +++ b/docs/pytorch_vol2.ipynb @@ -0,0 +1,388 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "pytorch.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lxhgSo4rOWCg", + "outputId": "5242373c-1d80-4a96-ef17-1243f7eea994" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (1.10.0+cu111)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch) (3.10.0.2)\n" + ] + } + ], + "source": [ + "pip install torch" + ] + }, + { + "cell_type": "code", + "source": [ + "print(torch.__version__)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "458HSdE3WK68", + "outputId": "be8888f7-f9ee-474f-b393-1d3545382b27" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1.10.0+cu111\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "from torchvision import datasets, transforms\n", + "\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "import torch.optim as optim\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from tqdm import tqdm\n", + "\n", + "if torch.cuda.is_available():\n", + " device = torch.device(\"cuda:0\")\n", + " print(\"GPU\")\n", + "else:\n", + " device = torch.device(\"cpu\")\n", + " print(\"CPU\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "WoWY-yVoPKiI", + "outputId": "707345d2-53fb-4b01-f417-0c5b2db0aafa" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "GPU\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "class Network(nn.Module):\n", + " def __init__(self):\n", + " super().__init__()\n", + " self.input_layer = nn.Linear(784, 64)\n", + " self.hidden1 = nn.Linear(64, 64)\n", + " self.hidden2 = nn.Linear(64, 64)\n", + " self.output = nn.Linear(64, 10)\n", + "\n", + " def forward(self, data):\n", + " data = F.relu(self.input_layer(data))\n", + " data = F.relu(self.hidden1(data))\n", + " data = F.relu(self.hidden2(data))\n", + " data = F.relu(self.output(data))\n", + "\n", + " return F.log_softmax(data, dim=1)" + ], + "metadata": { + "id": "zHPjts1vPjDo" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from torch.utils.data.dataset import T\n", + "training = datasets.MNIST(\"\", train = True, download = True,\n", + " transform = transforms.Compose([transforms.ToTensor()]))\n", + "\n", + "testing = datasets.MNIST(\"\", train = False, download = True,\n", + " transform = transforms.Compose([transforms.ToTensor()]))\n", + "\n", + "train_set = torch.utils.data.DataLoader(training, batch_size=10, shuffle=True)\n", + "test_set = torch.utils.data.DataLoader(testing, batch_size=10, shuffle=True)" + ], + "metadata": { + "id": "Phj4o7piR4FU" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from torch.autograd import backward\n", + "network = Network().to(device)\n", + "learn_rate = optim.Adam(network.parameters(), lr=0.001)\n", + "epochs = 4\n", + "\n", + "for i in tqdm(range(epochs)):\n", + " for data in train_set:\n", + " image, output = data\n", + " image = image.to(device)\n", + " output = output.to(device)\n", + " network.zero_grad()\n", + " result = network(image.view(-1, 784))\n", + " loss = F.nll_loss(result, output)\n", + " loss.backward()\n", + " learn_rate.step()\n", + " print(loss)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eqIk--nMTYIm", + "outputId": "44638ff7-e230-4f03-e4a7-cda5f75ea10d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + " 25%|██▌ | 1/4 [00:15<00:45, 15.29s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor(0.7714, device='cuda:0', grad_fn=<NllLossBackward0>)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\r 50%|█████ | 2/4 [00:30<00:30, 15.16s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor(0.9215, device='cuda:0', grad_fn=<NllLossBackward0>)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\r 75%|███████▌ | 3/4 [00:45<00:15, 15.05s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor(1.3817, device='cuda:0', grad_fn=<NllLossBackward0>)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 4/4 [01:00<00:00, 15.09s/it]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor(1.6124, device='cuda:0', grad_fn=<NllLossBackward0>)\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Test Network\n", + "network.eval()\n", + "\n", + "correct = 0\n", + "total = 0\n", + "\n", + "with torch.no_grad():\n", + " for data in test_set:\n", + " image, output = data\n", + " image = image.to(device)\n", + " output = output.to(device)\n", + " result = network(image.view(-1, 784))\n", + " for index, tensor_value in enumerate(result):\n", + " total += 1\n", + " if torch.argmax(tensor_value) == output[index]:\n", + " correct += 1\n", + "\n", + "accuracy = correct/total\n", + "print(f\"Accuracy: {accuracy}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fLRRyQnuYmRn", + "outputId": "0dc1fc3c-bf92-40dc-fd3a-d7d0335fc94e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Accuracy: 0.7795\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/gdrive', force_remount=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "97UyaTsHcIft", + "outputId": "be688931-b92b-4efb-b20e-12be438f03d8" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/gdrive\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!ls '/content/gdrive/My Drive/TestPytorch'" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RQop3IL3csZo", + "outputId": "635017e3-b7d2-42a1-9e5c-72e6fefc2f73" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "5.png 7.png\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from PIL import Image\n", + "import numpy as np\n", + "import PIL.ImageOps\n", + "\n", + "img = Image.open(\"gdrive/My Drive/TestPytorch/5.png\")\n", + "img = img.resize((28, 28))\n", + "img = img.convert(\"L\")\n", + "img = PIL.ImageOps.invert(img)\n", + "\n", + "plt.imshow(img)\n", + "\n", + "img = np.array(img)\n", + "img = img / 255\n", + "image = torch.from_numpy(img)\n", + "image = image.float()\n", + "image = image.to(device)\n", + "\n", + "res = network.forward(image.view(-1, 28*28))\n", + "res = res.to(device)\n", + "print(torch.argmax(output))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283 + }, + "id": "GMf65cgLdlfa", + "outputId": "422c46d3-526c-402f-f7b9-cfa713d3fd72" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor(6, device='cuda:0')\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "#TensorFlow vs PyTorch" + ], + "metadata": { + "id": "vQG0yG7VjgGE" + } + }, + { + "cell_type": "markdown", + "source": [ + "Dve najpopularnije biblioteke deep-learning-a zasnovane na Python-u su PyTorch i TensorFlow. Početniku koji se bavi mašinskim učenjem može biti teško da odluči koju od ove dve biblioteke će koristiti kada radi sa modelom deep-learning-a. Razliku je najbolje uočiti tako što će biti kreiran klasifikator koji koristi oba framework-a za rešavanje istog problema." + ], + "metadata": { + "id": "5wnwB9o0gs2L" + } + }, + { + "cell_type": "markdown", + "source": [ + "MNIST je akronim za skup podataka The Modified National Institute of Standards and Technology. To je kolekcija od 60.000 malih kvadratnih slika u nijansama sive boje pisanih cifara (od 0 do 9). Cilj je klasifikovati sliku koja je pisana rukom u jednu od deset klasa koje predstavljaju celobrojne vrednosti u rasponu od 0 do 9." + ], + "metadata": { + "id": "HLVo2_kPi0Sg" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Ukratko o TensorFlow-u" + ], + "metadata": { + "id": "O9uIKVoGkG1b" + } + }, + { + "cell_type": "markdown", + "source": [ + "Google je razvio TensorFlow, koji je open source od 2015. godine. Razvio se iz Google-ovog internog softvera za mašinsko učenje, koji je refaktorisan i optimizovan za upotrebu u proizvodnji.\n", + "\n", + "Termin \"TensorFlow\" se odnosi na način na koji su podaci organizovani i obrađeni. Tenzor je najosnovnija struktura podataka u TensorFlow-u i PyTorch-u.\n", + "\n", + "TensorFlow je biblioteka za deep learning visokih performansi." + ], + "metadata": { + "id": "Hu-RncH-kKgz" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Ukratko o PyTorch-u" + ], + "metadata": { + "id": "kk_iv5_Pxt5o" + } + }, + { + "cell_type": "markdown", + "source": [ + "PyTorch je jedan od najnovijih framework-a za deep learning, koji je razvio Facebook tim i objavljen na GitHub-u 2017. PyTorch dobija na popularnosti zbog svoje lakoće korišćenja, jednostavnosti i efikasnog korišćenja memorije. To je imperativ, što znači da se pokreće odmah, a korisnik može da ga testira da vidi da li radi pre nego što napiše ceo kod.\n", + "\n", + "Možemo napisati deo koda i pokrenuti ga u realnom vremenu jer ima ugrađenu Python implementaciju koja obezbeđuje kompatibilnost kao platforma za deep learning. Brzo je stekao popularnost zbog svog korisničkog interfejsa, što je navelo Tensorflow tim da ugradi svoje najpopularnije feature-e u Tensorflow 2.0." + ], + "metadata": { + "id": "y_zQUFxdx7XH" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Pravljenje deep learning modela za klasifikaciju slika" + ], + "metadata": { + "id": "cJpjDu_Vy33y" + } + }, + { + "cell_type": "markdown", + "source": [ + "U daljem tekstu ćemo uporediti upotrebljivost koda i lakoću upotrebe TensorFlow-a i PyTorch-a na najčešće korišćenom skupu podataka MNIST za klasifikaciju rukom pisanih cifara. Koristeći oba okvira, proverićemo minimalne procedure koje treba sprovesti kako bismo imali odgovarajući model klasifikacije. U oba koraka modela koje treba preduzeti su učitavanje podataka, prethodna obrada, pravljenje modela, obuka i vizuelizacija rezultata. Za oba modela slojevi i konfiguracije hiperparametara su isti.\n", + "\n", + "Počećemo prvo sa TensorFlow-om." + ], + "metadata": { + "id": "0ayWMoHuzIuQ" + } + }, + { + "cell_type": "markdown", + "source": [ + "####TensorFlow - Model building" + ], + "metadata": { + "id": "_vMOqp_Zjsv4" + } + }, + { + "cell_type": "markdown", + "source": [ + "Pravimo model konvolucione neuronske mreže za klasifikaciju slika u TensorFlow-u." + ], + "metadata": { + "id": "UM6BB4oj88C3" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hwAWAPoMjQhN" + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.keras.datasets import mnist \n", + "from tensorflow.keras.utils import to_categorical\n", + "from tensorflow.keras.layers import Conv2D, Flatten, Dense, MaxPooling2D\n", + "from tensorflow.keras.models import Sequential\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Proveravamo verziju TensorFlow-a." + ], + "metadata": { + "id": "W9qjmDOX9WJM" + } + }, + { + "cell_type": "code", + "source": [ + "#pip install --upgrade tensorflow\n", + "print(tensorflow.__version__)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X0sojNoGlLJh", + "outputId": "9971bca4-15b5-4cef-b7b6-bb380c8523fe" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "2.8.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Učitavamo skup podataka mnist i pravimo training i test skupove:" + ], + "metadata": { + "id": "o1VR27929iWZ" + } + }, + { + "cell_type": "code", + "source": [ + "mnist = tf.keras.datasets.mnist\n", + "(x_train, y_train),(x_test, y_test) = mnist.load_data()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Yk04SZJXsTeZ", + "outputId": "1f88f77e-fa4d-4238-b4a9-98deb2242f74" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", + "11493376/11490434 [==============================] - 0s 0us/step\n", + "11501568/11490434 [==============================] - 0s 0us/step\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Takođe je neophodno da se srede ulazni podaci." + ], + "metadata": { + "id": "Tei7ILqu9t_O" + } + }, + { + "cell_type": "code", + "source": [ + "# reshaping and one hot encoding\n", + "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n", + "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n", + "y_train = to_categorical(y_train)\n", + "y_test = to_categorical(y_test)" + ], + "metadata": { + "id": "vxC2d4mfsez3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# scaling\n", + "x_train = x_train.astype('float32')\n", + "x_test = x_test.astype('float32')\n", + "x_train = x_train / 255.0\n", + "x_test = x_test / 255.0" + ], + "metadata": { + "id": "fVHrrpMIsgTY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = Sequential()\n", + "model.add(Conv2D(32, (3,3), input_shape = (28,28,1), activation='relu'))\n", + "model.add(Conv2D(64,(3,3), activation='relu'))\n", + "model.add(MaxPooling2D((2,2)))\n", + "model.add(Flatten())\n", + "model.add(Dense(1024, activation='relu'))\n", + "model.add(Dense(10, activation='softmax'))\n", + "# compile\n", + "model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])" + ], + "metadata": { + "id": "UK3Mr-QPslnC" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# training \n", + "history = model.fit(x_train, y_train, validation_split=0.3, epochs=5)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5g4Lj3_MspM8", + "outputId": "a0dfaa33-1d5b-4a60-e210-af127cd88dc9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/5\n", + "1313/1313 [==============================] - 209s 158ms/step - loss: 0.4175 - accuracy: 0.8820 - val_loss: 0.2058 - val_accuracy: 0.9382\n", + "Epoch 2/5\n", + "1313/1313 [==============================] - 209s 159ms/step - loss: 0.1549 - accuracy: 0.9522 - val_loss: 0.1585 - val_accuracy: 0.9532\n", + "Epoch 3/5\n", + "1313/1313 [==============================] - 206s 157ms/step - loss: 0.1115 - accuracy: 0.9653 - val_loss: 0.1066 - val_accuracy: 0.9688\n", + "Epoch 4/5\n", + "1313/1313 [==============================] - 206s 157ms/step - loss: 0.0881 - accuracy: 0.9725 - val_loss: 0.1051 - val_accuracy: 0.9675\n", + "Epoch 5/5\n", + "1313/1313 [==============================] - 205s 156ms/step - loss: 0.0749 - accuracy: 0.9763 - val_loss: 0.0923 - val_accuracy: 0.9718\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### PyTorch - Model building" + ], + "metadata": { + "id": "aqZ7Nc5TxDm3" + } + }, + { + "cell_type": "markdown", + "source": [ + "Pravimo model konvolucione neuronske mreže za klasifikaciju slika u PyTorch-u." + ], + "metadata": { + "id": "x5sFtN51-vqI" + } + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "from torchvision import datasets, transforms" + ], + "metadata": { + "id": "Al-YoL-VxHYS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Učitavanje i preprocesiranje podataka." + ], + "metadata": { + "id": "U9Tawkpb_umB" + } + }, + { + "cell_type": "code", + "source": [ + "# pre-processor\n", + "transform = transforms.Compose([\n", + " transforms.Resize((8, 8)),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize((0.1307,), (0.3081,))])" + ], + "metadata": { + "id": "FNqQvbBrxU3s" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# load the data\n", + "train_dataset = datasets.MNIST(\n", + " 'data', train=True, download=True, transform=transform)\n", + "test_dataset = datasets.MNIST(\n", + " 'data', train=False, download=True, transform=transform)\n", + " \n", + "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=512)\n", + "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=512)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 436, + "referenced_widgets": [ + "7aaf00f20d77493fadb2b6ad0b96cf74", + "4b92939e2d45451da2334595d97bd68d", + "12969beb96f7497181f7167e39c40bd0", + "74a47efac60c4af396b9d1e1f282827f", + "3d1920917c9947148a40db3bc30a4132", + "d3ab8f1573e54fb49837c7675c8a237e", + "b84a67505e3c4030a3839cbe0b5a2d6d", + "9fa408a5efc84e64be85faca9e0701b4", + "3041321e29fc4fab892a3e6b3b5c9bbd", + "3ebefec2c2aa499a96c4644d7855cd7d", + "2fe247e330224176a25f48abca95cda2", + "526a499287d146c7b3c429d3460c6736", + "692b9b0b9f16464eb0426266b5123a9c", + "cc94aeb409fd4393815cb855d952af12", + "cef5db1a425b481ab21d898a22a73763", + "13c1cd14c03248559d8b5a604269d800", + "bf19654df95e43289ed8071ad717045c", + "0597828f149d4901ba240137e35c5bf8", + "bd60eec9f2314e96ae5bde2cc2b5bd90", + "0daf5c329df64e39b169cc136ceafcfd", + "6b03227602be48588c11acd7c06e599f", + "2efd7ec451034ab8be703274233277ce", + "b5eae176555e47b582757cd8543969d1", + "73209e9211ac4b929f3c112500ebed41", + "752919f2449647039640f2b0cd2a1e40", + "8e577096756c42fdb6d2d0aafc080610", + "3c56e65e3c8c4bde81f72adc6eca04ce", + "66b17d032f8741bbb482737603af5a15", + "f3938db95eee4a218798a65ed2a9ec5d", + "b2a53beffd864607b83f9e21662f1aec", + "0280a38e8c6e4414b6fcde5e5f23b4f2", + "856cc2055cce4a61be024950840a9e43", + "c75692c664d04f19990180f3aab5ba5f", + "e599af99f6cc4feeb0f3f20b61d59fc0", + "06a8f00a70e44ffaafa9e8d6c851020e", + "b457e56a4a4a45578b0ab348b2be3a41", + "c7cb67769284415b99735bb819bb575b", + "2b926da18fd247b294fab2bf5c352238", + "55aa50982905491eaa854d26cd7e79c9", + "cf701b836c794ddea46ba7242fe7125e", + "630ce4632ce346d5a24fc7e619fc8c4e", + "31fc6a8e3e8f42c08be70f8a8d6f72b3", + "fb030304a11345a4a88f618281ac1508", + "36f61cfa17944f54b74701c57e12b526" + ] + }, + "id": "SuSa2oifxaWN", + "outputId": "8223ab58-4a27-4bde-ba95-f6c9d78ed907" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7aaf00f20d77493fadb2b6ad0b96cf74", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + " 0%| | 0/9912422 [00:00<?, ?it/s]" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "526a499287d146c7b3c429d3460c6736", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + " 0%| | 0/28881 [00:00<?, ?it/s]" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b5eae176555e47b582757cd8543969d1", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + " 0%| | 0/1648877 [00:00<?, ?it/s]" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e599af99f6cc4feeb0f3f20b61d59fc0", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + " 0%| | 0/4542 [00:00<?, ?it/s]" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "" + ], + "metadata": { + "id": "qhPhR1gB_2Hu" + } + }, + { + "cell_type": "code", + "source": [ + "# Build a model\n", + "class CNNModel(nn.Module):\n", + " def __init__(self):\n", + " super(CNNModel, self).__init__()\n", + " self.conv1 = nn.Conv2d(1, 32, 3, 1)\n", + " self.conv2 = nn.Conv2d(32, 64, 3, 1)\n", + " self.fc = nn.Linear(1024, 10)\n", + " \n", + " def forward(self, x):\n", + " x = F.relu(self.conv1(x))\n", + " x = F.relu(self.conv2(x))\n", + " x = F.max_pool2d(x, 1)\n", + " x = torch.flatten(x, 1)\n", + " x = self.fc(x)\n", + " output = F.log_softmax(x, dim=1)\n", + " return output" + ], + "metadata": { + "id": "khflrhn9xgZy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "net = CNNModel()\n", + " \n", + "# compiling\n", + "optimizer = optim.SGD(net.parameters(), lr=0.01)\n", + "criterion = nn.CrossEntropyLoss()" + ], + "metadata": { + "id": "Yab4Lz95xlLR" + }, + "execution_count": null, + "outputs": [] + } + ] +}
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