{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Свёрточная сеть: MNIST\n", "\n", "Александр Дьяконов, 2020\n", "\n", "#### Использованы материалы:\n", "* https://atcold.github.io/pytorch-Deep-Learning/" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import torch\n", "import torchvision\n", "import torchvision.transforms as transforms\n", "from torchvision import datasets, transforms\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Датасет" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "input_size = 28*28 # размер изображения\n", "output_size = 10 # 10 классов\n", "\n", "train_loader = torch.utils.data.DataLoader(\n", " datasets.MNIST('../data', train=True, download=True,\n", " transform=transforms.Compose([\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.1307,), (0.3081,))\n", " ])),\n", " batch_size=64, shuffle=True)\n", "\n", "test_loader = torch.utils.data.DataLoader(\n", " datasets.MNIST('../data', train=False, transform=transforms.Compose([\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.1307,), (0.3081,))\n", " ])),\n", " batch_size=1000, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show some images\n", "plt.figure(figsize=(16, 6))\n", "for i in range(10):\n", " plt.subplot(2, 5, i + 1)\n", " image, _ = train_loader.dataset.__getitem__(i)\n", " plt.imshow(image.squeeze().numpy())\n", " plt.axis('off');" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([[ 1.9432, 2.7960, 2.7960, 1.4850, -0.0806],\n", " [-0.2206, 0.7595, 2.7833, 2.7960, 1.9560],\n", " [-0.4242, -0.4242, 2.7451, 2.7960, 2.7451],\n", " [ 1.2305, 1.9051, 2.7960, 2.7960, 2.2105],\n", " [ 2.7960, 2.7960, 2.7960, 2.7578, 1.8923]])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_loader.dataset.__getitem__(0)[0].squeeze()[15:20, 15:20]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Нейросеть" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [], "source": [ "class Mlinear(nn.Module):\n", " \"\"\"\n", " линейная\n", " \"\"\"\n", " def __init__(self, input_size, output_size):\n", " super(Mlinear, self).__init__()\n", " self.conv = nn.Conv2d(in_channels=1, out_channels=10, kernel_size=28)\n", " # self.fc = nn.Linear(28*28, output_size)\n", " \n", " def forward(self, x, verbose=False):\n", " x = self.conv(x)\n", " x = x.view(-1, 10)\n", " x = F.log_softmax(x, dim=1)\n", " return x" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "accuracy_list = []\n", "\n", "def train(epoch, model):\n", " model.train()\n", " for batch_idx, (data, target) in enumerate(train_loader):\n", " # send to device\n", " data, target = data.to(device), target.to(device)\n", "\n", " optimizer.zero_grad()\n", " output = model(data)\n", " loss = F.nll_loss(output, target)\n", " loss.backward()\n", " optimizer.step()\n", " if batch_idx % 100 == 0:\n", " print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n", " epoch, batch_idx * len(data), len(train_loader.dataset),\n", " 100. * batch_idx / len(train_loader), loss.item()))\n", " \n", "def test(model):\n", " model.eval()\n", " test_loss = 0\n", " correct = 0\n", " for data, target in test_loader:\n", " # send to device\n", " data, target = data.to(device), target.to(device)\n", "\n", " output = model(data)\n", " test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss \n", " pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability \n", " correct += pred.eq(target.data.view_as(pred)).cpu().sum().item()\n", "\n", " test_loss /= len(test_loader.dataset)\n", " accuracy = 100. * correct / len(test_loader.dataset)\n", " accuracy_list.append(accuracy)\n", " print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n", " test_loss, correct, len(test_loader.dataset),\n", " accuracy))" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of parameters: 14070\n", "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.283597\n", "Train Epoch: 0 [6400/60000 (11%)]\tLoss: 0.512199\n", "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 0.525875\n", "Train Epoch: 0 [19200/60000 (32%)]\tLoss: 0.345186\n", "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.178718\n", "Train Epoch: 0 [32000/60000 (53%)]\tLoss: 0.506424\n", "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.202364\n", "Train Epoch: 0 [44800/60000 (75%)]\tLoss: 0.322217\n", "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.226272\n", "Train Epoch: 0 [57600/60000 (96%)]\tLoss: 0.338484\n", "\n", "Test set: Average loss: 0.3061, Accuracy: 9151/10000 (92%)\n", "\n", "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.218785\n", "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 0.269072\n", "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.366377\n", "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 0.290509\n", "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.251645\n", "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.261202\n", "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.258060\n", "Train Epoch: 1 [44800/60000 (75%)]\tLoss: 0.233534\n", "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.273988\n", "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 0.356423\n", "\n", "Test set: Average loss: 0.2911, Accuracy: 9176/10000 (92%)\n", "\n", "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.418493\n", "Train Epoch: 2 [6400/60000 (11%)]\tLoss: 0.511104\n", "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.163205\n", "Train Epoch: 2 [19200/60000 (32%)]\tLoss: 0.419903\n", "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.322113\n", "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.124521\n", "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.266031\n", "Train Epoch: 2 [44800/60000 (75%)]\tLoss: 0.305101\n", "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.322588\n", "Train Epoch: 2 [57600/60000 (96%)]\tLoss: 0.277742\n", "\n", "Test set: Average loss: 0.2833, Accuracy: 9195/10000 (92%)\n", "\n" ] } ], "source": [ "\n", "model = Mlinear(input_size, output_size)\n", "model.to(device)\n", "optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)\n", "print('Number of parameters: {}'.format(get_n_params(model_cnn)))\n", "\n", "for epoch in range(0, 3):\n", " train(epoch, model)\n", " test(model)\n", "# Train Epoch: 0 [57600/60000 (96%)]\tLoss: 0.122960\n", "#Test set: Average loss: 0.4662, Accuracy: 8823/10000 (88%)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## визуализируем параметры" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "conv.weight tensor([[[[ 0.0226, 0.0314, -0.0149, ..., -0.0147, -0.0005, -0.0037],\n", " [ 0.0301, 0.0315, 0.0082, ..., -0.0155, 0.0063, 0.0263],\n", " [-0.0069, -0.0016, 0.0011, ..., -0.0034, 0.0063, -0.0007],\n", " ...,\n", " [-0.0143, 0.0260, 0.0359, ..., 0.0212, -0.0014, 0.0079],\n", " [-0.0086, 0.0406, 0.0243, ..., 0.0045, -0.0114, 0.0303],\n", " [-0.0223, 0.0289, 0.0297, ..., -0.0161, 0.0109, 0.0090]]],\n", "\n", "\n", " [[[ 0.0372, -0.0093, -0.0227, ..., -0.0186, -0.0234, -0.0084],\n", " [-0.0021, 0.0118, -0.0075, ..., -0.0204, -0.0042, -0.0095],\n", " [-0.0171, 0.0390, -0.0163, ..., -0.0231, 0.0450, -0.0163],\n", " ...,\n", " [ 0.0337, -0.0048, 0.0227, ..., 0.0343, -0.0059, -0.0232],\n", " [ 0.0364, 0.0253, 0.0053, ..., -0.0078, 0.0340, 0.0336],\n", " [-0.0073, -0.0214, 0.0097, ..., 0.0100, 0.0346, -0.0243]]],\n", "\n", "\n", " [[[ 0.0179, -0.0104, -0.0209, ..., -0.0145, -0.0295, -0.0445],\n", " [ 0.0157, 0.0008, 0.0187, ..., -0.0127, 0.0226, 0.0146],\n", " [-0.0152, -0.0182, 0.0007, ..., -0.0065, 0.0029, -0.0177],\n", " ...,\n", " [ 0.0226, 0.0092, -0.0215, ..., -0.0273, 0.0170, -0.0367],\n", " [ 0.0053, 0.0043, -0.0378, ..., -0.0387, 0.0227, -0.0377],\n", " [ 0.0021, 0.0102, 0.0192, ..., 0.0236, -0.0358, -0.0168]]],\n", "\n", "\n", " ...,\n", "\n", "\n", " [[[-0.0392, 0.0213, 0.0231, ..., -0.0304, -0.0163, -0.0082],\n", " [-0.0027, -0.0191, 0.0062, ..., 0.0007, 0.0132, -0.0334],\n", " [-0.0396, -0.0168, 0.0098, ..., -0.0361, -0.0246, -0.0291],\n", " ...,\n", " [-0.0095, 0.0250, -0.0040, ..., 0.0004, -0.0078, 0.0257],\n", " [ 0.0312, 0.0038, 0.0069, ..., -0.0081, 0.0238, -0.0151],\n", " [ 0.0059, 0.0230, 0.0299, ..., 0.0052, -0.0314, 0.0218]]],\n", "\n", "\n", " [[[ 0.0098, 0.0200, 0.0365, ..., 0.0001, 0.0372, 0.0097],\n", " [ 0.0156, 0.0131, 0.0190, ..., 0.0357, 0.0061, 0.0299],\n", " [-0.0277, 0.0351, -0.0051, ..., 0.0199, 0.0011, -0.0021],\n", " ...,\n", " [ 0.0211, 0.0105, -0.0317, ..., 0.0391, -0.0150, -0.0245],\n", " [ 0.0068, 0.0194, 0.0189, ..., 0.0385, -0.0274, -0.0284],\n", " [-0.0209, 0.0247, 0.0258, ..., -0.0041, 0.0297, -0.0160]]],\n", "\n", "\n", " [[[ 0.0144, 0.0129, 0.0034, ..., 0.0358, 0.0403, 0.0397],\n", " [ 0.0325, -0.0279, 0.0264, ..., 0.0209, 0.0158, -0.0078],\n", " [ 0.0061, -0.0172, 0.0012, ..., -0.0117, 0.0024, 0.0112],\n", " ...,\n", " [ 0.0002, -0.0011, -0.0235, ..., 0.0125, 0.0040, 0.0332],\n", " [-0.0248, 0.0313, 0.0207, ..., -0.0183, -0.0133, 0.0145],\n", " [-0.0121, 0.0245, -0.0039, ..., 0.0285, 0.0236, 0.0286]]]],\n", " device='cuda:0')\n", "conv.bias tensor([-0.0448, 0.0074, 0.0315, 0.0370, 0.0048, 0.0503, -0.0397, 0.0155,\n", " 0.0160, -0.0418], device='cuda:0')\n" ] } ], "source": [ "# параметры модели\n", "for name, param in model.named_parameters():\n", " if param.requires_grad:\n", " print (name, param.data)" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "tensor([-0.0448, 0.0074, 0.0315, 0.0370, 0.0048, 0.0503, -0.0397, 0.0155,\n", " 0.0160, -0.0418], device='cuda:0')" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "param.data" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [], "source": [ "h = list(model.named_parameters())[0][1][2].detach().to('cpu').numpy()[0,:,:]" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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eRV/fdDBeSdLzAKCS2BVTk+H2vACwSCwJAIh08MXKcjjFtSVib7W18nTI/j5uOR45ym1DlqaaKeH/z5fJ8wKA65ES2xZJkb15LWw75nLhsscA0LiLr2t2hY9/7/vD6bWLkTLVMWstU8bXtbmxisazq+G59w/M0LEtbeHjibXB1pldiESQ2IVIBIldiESQ2IVIBIldiESQ2IVIBIldiEQoqM+eyZSgrZV7p4yRwbBHf+yBjmAMAKoj7aD37W2gcVaWeHXV6diLl0ZpPOajx5icWgzGrJG3bN5NUlCBeIrrxQu8ZfN9D4bTc2PptdPTvHVxWaREN0uR7d7HqybVkPLcALCvmx8vrLw3AJw9NxiMZSN7H1rawyXXM2R/gM7sQiSCxC5EIkjsQiSCxC5EIkjsQiSCxC5EIkjsQiRCYVs2Y62cdIhdrbxN7tVL4ZbNsbLDJyLlmIeG52h8ajrsZdfV8tbBPQfD7XkBYHiEP3ZDpDUx20NQEfGixyKthUdG+dwam7lP/zbx4WNttGvq+PNejtQo6OhqDMbYcQgAFRV8brNk3wUALCzwPQSzs3w8Y3w0XDY9mw3nyevMLkQiSOxCJILELkQiSOxCJILELkQiSOxCJILELkQiFNRnz2ZzGB0L13eP5Tez1saxuu+lpfwGmUiL3sWFsKfbEqlvnsnwxz50gOdWX4u0hGa51f/+b9fo2IOHw22wAeDWVZ6Lf/xB3m6a1bSfnuQe/0qkLvwfPLqfxvsHw/XXu/aEc8IBYD7ik8fqzvf189rvbF/IWGRvQ1tHeO7sSIue2c2s28x+bmYXzOy8mX02f32zmb1sZpfzl/yIFUIUlfW8jc8C+Ly7HwfwAQCfNrMTAL4A4BV3PwzglfzfQogdSlTs7j7g7m/kf58BcAFAJ4CnALyQv9kLAD6yXZMUQmye3+sLOjPbD+BhAL8E0ObuA8DaPwQArYExp8ys18x65+Z4TzMhxPaxbrGbWS2AHwH4nLuHKz++C3c/7e4n3f1kTQ0v0ieE2D7WJXYzK8Oa0L/n7j/OXz1kZh35eAcAXmZUCFFUotabmRmAbwO44O5fuSv0IoBnADyXv/xp7L6qqsvxwMOdwfjlt4bo+Gw2XGJ3KJLiOjLA34xU1fDSv0eP3/NTCgBgImIh3b7F59bZxd/xTEzw+2cpsLFUzgHSQhsAyiNttO/c4i2dWRprLD12X3c4RRUAlld4yWVGLK04VvJ8coqXuW5o4Om5mbJdwVhdA2/3vLJMbD/iva3HZ38UwCcAnDWzM/nrvog1kf/QzD4J4BaAj63jvoQQRSIqdnf/BcL/Lz60tdMRQmwX2i4rRCJI7EIkgsQuRCJI7EIkgsQuRCIUNMU1l8thbiZckjnmCa+SlMeDR8M+OAAM9vGtuk2RNNXJyfC8Y+mSuyJ+8sIiT6esj5SSniBzK42k7o4N81RMd/6aXLkyTuP3k5bNYyPhksgA0NQY8ZsjPnvP/nAi5ghJtQaAyNPG9Az32Ssj+xOGyL6Pikh78WuXx4KxpaXwmujMLkQiSOxCJILELkQiSOxCJILELkQiSOxCJILELkQiFNZnX3XMk1a1zbu4H720FPaje/+b187YFynXfOn8II0zzv26j8ZjedsLkfa/S5HWxKxMdnNLLR07Pc394traSJ7/sRYav3o5XIr6flIaHACmpsL7BwBgzx7e4vu13vDrYpHS4lURr3sqUmNgPLKHoJ20k+67wfcudHSG93WUl4XP3zqzC5EIErsQiSCxC5EIErsQiSCxC5EIErsQiSCxC5EIhW3ZvJrDJPEnW9u5b3r7Rrj+elmG/98auMPz2XO8OzCGSH319i6ezz7Yz2uzZyJzv/7WCI13HwnXIK9v5E9snrSiBoC6Ou6zDw3wfPgu0k56dJCvy50bfG63rofzugGgoSm8v2E00lJ5epp7/LE6Aay9OAD8+le3g7FYi+8Gtm+D9C7XmV2IRJDYhUgEiV2IRJDYhUgEiV2IRJDYhUgEiV2IRFhPf/ZuAN8F0A4gB+C0u3/NzJ4F8FcA3jGBv+juL7H7qqwsw5H7wnXEF+Z4bnVNTTjHuIzk8QLAIqmnDQANDZU0vkJ6w7O67QDQGLnvO9d5j/MDJ3jO+M23wznjg7f5/oIH39dF4yUlkbzvGl7TnvUJGBzkPdIPHmqm8aUFXm9/bDicU17fxGvSZyLHU3Utf95XLw7ReEVluG/9/kO76di3z4Xve3k5fJyuZ1NNFsDn3f0NM6sD8LqZvZyPfdXd/2Ed9yGEKDLr6c8+AGAg//uMmV0A0LndExNCbC2/12d2M9sP4GEAv8xf9Rkze9PMnjeze9Z9MrNTZtZrZr1TU7zcjhBi+1i32M2sFsCPAHzO3acBfBNAD4CHsHbm//K9xrn7aXc/6e4nGxr4ZzAhxPaxLrGbWRnWhP49d/8xALj7kLuvunsOwLcAPLJ90xRCbJao2M3MAHwbwAV3/8pd13fcdbOPAji39dMTQmwV6/k2/lEAnwBw1szO5K/7IoCnzewhAA7gBoBPxe4ol8thdjqc4hprkzs3F7Za9h3kpaIXSIoqAIyOcBuoojxslRhJKwSA4w920PjuFl5qOlMWfmwAmCHlufeRtsUAcPtWOG0YAPbuC5c8BtZeU8bMTHhuLZHnPRhJQ43ZrbPkeBmLtGxu7+Dp1jOTvJR0WeQ1m5oK28yZDB+7UdbzbfwvANzraKaeuhBiZ6EddEIkgsQuRCJI7EIkgsQuRCJI7EIkgsQuRCIUtJS0O7CaDZvp5RXcXzz2QDg9NlaOeTLiq8ZaG7PWw8fJvADg9rXwWABo7+Re9lvneDvpttaaYGwgsr+gPOIHV1XzUtKLkTTT7GrYh+86wLdPX36Lt+HO5fjGjJ5D4RLbQwN8XZpb+fEwOsj3AJRGjscH3hvOJTt/pp+ObWgIp9ey9t06swuRCBK7EIkgsQuRCBK7EIkgsQuRCBK7EIkgsQuRCOaxJPKtfDCzEQA377pqNwBuQhePnTq3nTovQHPbKFs5t33ufs/a4wUV++88uFmvu58s2gQIO3VuO3VegOa2UQo1N72NFyIRJHYhEqHYYj9d5Mdn7NS57dR5AZrbRinI3Ir6mV0IUTiKfWYXQhQIiV2IRCiK2M3sCTN728yumNkXijGHEGZ2w8zOmtkZM+st8lyeN7NhMzt313XNZvaymV3OX/LC8IWd27Nm1pdfuzNm9mSR5tZtZj83swtmdt7MPpu/vqhrR+ZVkHUr+Gd2MysFcAnAnwG4A+A1AE+7+1sFnUgAM7sB4KS7F30Dhpn9MYBZAN919/vz1/09gHF3fy7/j7LJ3f92h8ztWQCzxW7jne9W1HF3m3EAHwHwlyji2pF5/TkKsG7FOLM/AuCKu19z92UAPwDwVBHmseNx91cBvLv17VMAXsj//gLWDpaCE5jbjsDdB9z9jfzvMwDeaTNe1LUj8yoIxRB7J4Dbd/19Bzur37sD+JmZvW5mp4o9mXvQ5u4DwNrBA6C1yPN5N9E23oXkXW3Gd8zabaT9+WYphtjvVSRrJ/l/j7r7ewF8GMCn829XxfpYVxvvQnGPNuM7go22P98sxRD7HQDdd/3dBYBX2Csg7t6fvxwG8BPsvFbUQ+900M1f8qqMBWQntfG+V5tx7IC1K2b782KI/TUAh83sgJmVA/g4gBeLMI/fwcxq8l+cwMxqADyOndeK+kUAz+R/fwbAT4s4l99ip7TxDrUZR5HXrujtz9294D8AnsTaN/JXAfxdMeYQmNdBAL/J/5wv9twAfB9rb+tWsPaO6JMAdgF4BcDl/GXzDprbPwE4C+BNrAmro0hz+0OsfTR8E8CZ/M+TxV47Mq+CrJu2ywqRCNpBJ0QiSOxCJILELkQiSOxCJILELkQiSOxCJILELkQi/A+LejeCnZbY3gAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.imshow(h, cmap='Purples_r')" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# выученные патерны классов\n", "plt.figure(figsize=(16, 6))\n", "for i in range(10):\n", " plt.subplot(2, 5, i + 1)\n", " image = list(model.named_parameters())[0][1][i].detach().to('cpu').numpy()[0,:,:]\n", " plt.imshow(image, cmap='Purples_r')\n", " plt.axis('off');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Сложная модель" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "class CNN(nn.Module):\n", " def __init__(self, input_size, n_feature, output_size):\n", " super(CNN, self).__init__()\n", " self.n_feature = n_feature\n", " self.conv1 = nn.Conv2d(in_channels=1, out_channels=n_feature, kernel_size=5)\n", " self.conv2 = nn.Conv2d(n_feature, n_feature, kernel_size=5)\n", " self.fc1 = nn.Linear(n_feature*4*4, 50)\n", " self.fc2 = nn.Linear(50, 10)\n", " \n", " def forward(self, x, verbose=False):\n", " x = self.conv1(x)\n", " x = F.relu(x)\n", " x = F.max_pool2d(x, kernel_size=2)\n", " x = self.conv2(x)\n", " x = F.relu(x)\n", " x = F.max_pool2d(x, kernel_size=2)\n", " x = x.view(-1, self.n_feature*4*4) # torch.flatten(x, 1 или 2)\n", " x = self.fc1(x)\n", " x = F.relu(x)\n", " x = self.fc2(x)\n", " x = F.log_softmax(x, dim=1)\n", " return x" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def get_n_params(model):\n", " \"\"\"\n", " число параметров\n", " \"\"\"\n", " np=0\n", " for p in list(model.parameters()):\n", " np += p.nelement()\n", " return np" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of parameters: 11330\n", "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.298711\n", "Train Epoch: 0 [6400/60000 (11%)]\tLoss: 1.123118\n", "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 0.357951\n", "Train Epoch: 0 [19200/60000 (32%)]\tLoss: 0.267037\n", "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.251017\n", "Train Epoch: 0 [32000/60000 (53%)]\tLoss: 0.254121\n", "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.247766\n", "Train Epoch: 0 [44800/60000 (75%)]\tLoss: 0.222735\n", "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.214110\n", "Train Epoch: 0 [57600/60000 (96%)]\tLoss: 0.292420\n", "\n", "Test set: Average loss: 0.1361, Accuracy: 9603/10000 (96%)\n", "\n", "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.185601\n", "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 0.121008\n", "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.135417\n", "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 0.052468\n", "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.061729\n", "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.110413\n", "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.115863\n", "Train Epoch: 1 [44800/60000 (75%)]\tLoss: 0.076380\n", "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.028216\n", "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 0.035985\n", "\n", "Test set: Average loss: 0.0748, Accuracy: 9772/10000 (98%)\n", "\n", "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.110379\n", "Train Epoch: 2 [6400/60000 (11%)]\tLoss: 0.178392\n", "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.233576\n", "Train Epoch: 2 [19200/60000 (32%)]\tLoss: 0.026292\n", "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.099997\n", "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.158976\n", "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.063617\n", "Train Epoch: 2 [44800/60000 (75%)]\tLoss: 0.032139\n", "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.075119\n", "Train Epoch: 2 [57600/60000 (96%)]\tLoss: 0.101296\n", "\n", "Test set: Average loss: 0.0689, Accuracy: 9768/10000 (98%)\n", "\n" ] } ], "source": [ "# Training settings \n", "n_features = 10 # number of feature maps\n", "\n", "model_cnn = CNN(input_size, n_features, output_size)\n", "model_cnn.to(device)\n", "optimizer = torch.optim.SGD(model_cnn.parameters(), lr=0.01, momentum=0.5)\n", "print('Number of parameters: {}'.format(get_n_params(model_cnn)))\n", "\n", "for epoch in range(0, 3):\n", " train(epoch, model_cnn)\n", " test(model_cnn)\n", "\n", "# Test set: Average loss: 0.2849, Accuracy: 9203/10000 (92%)\n", "# Test set: Average loss: 0.1034, Accuracy: 9679/10000 (97%)\n", "# Test set: Average loss: 0.0784, Accuracy: 9753/10000 (98%)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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NN9c8u6H5ZlXVpN0nddkdiyeFAAAAwUQhAABAMFEIAAAQTBQCAAAEE4UAAADBRCEAAEAwUQgAABBMFAIAAAQThQAAAMFEIQAAQDBRCAAAEEwUAgAABBOFAAAAwUQhAABAMFEIAAAQTBQCAAAEE4UAAADBRCEAAEAwUQgAABBMFAIAAAQThQAAAMFEIQAAQDBRCAAAEEwUAgAABBOFAAAAwUQhAABAMFEIAAAQTBQCAAAEE4UAAADBhkZHR3vfAwAAAJ14UggAABBMFAIAAAQThQAAAMFEIQAAQDBRCAAAEEwUAgAABPs/1nOdYFuareoAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show some images\n", "plt.figure(figsize=(16, 6))\n", "for i in range(10):\n", " plt.subplot(2, 5, i + 1)\n", " image = list(model_cnn.named_parameters())[0][1][i].detach().to('cpu').numpy()[0,:,:]\n", " plt.imshow(image, cmap='Purples_r')\n", " plt.axis('off');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Дополнение: как меняют размеры тензоры" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([1, 1, 28, 28])\n", "torch.Size([1, 1, 26, 26])\n", "torch.Size([1, 1, 13, 13])\n", "torch.Size([1, 1, 11, 11])\n", "torch.Size([1, 1, 5, 5])\n" ] } ], "source": [ "f = nn.Conv2d(in_channels=1, out_channels=1, kernel_size=3)\n", "x = torch.randn(1, 1, 28, 28)\n", "print (x.shape)\n", "x = f(x)\n", "print (x.shape)\n", "x = F.max_pool2d(x, kernel_size=2)\n", "print (x.shape)\n", "x = f(x)\n", "print (x.shape)\n", "x = F.max_pool2d(x, kernel_size=2)\n", "print (x.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Сложная модель, свёртки 3x3" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "class CNN(nn.Module):\n", " def __init__(self, input_size, n_feature, output_size):\n", " super(CNN, self).__init__()\n", " self.n_feature = n_feature\n", " self.conv1 = nn.Conv2d(in_channels=1, out_channels=n_feature, kernel_size=3)\n", " self.conv2 = nn.Conv2d(n_feature, n_feature, kernel_size=3)\n", " self.fc1 = nn.Linear(n_feature*5*5, 50)\n", " self.fc2 = nn.Linear(50, 10)\n", " \n", " def forward(self, x, verbose=False):\n", " x = self.conv1(x)\n", " x = F.relu(x)\n", " x = F.max_pool2d(x, kernel_size=2)\n", " x = self.conv2(x)\n", " x = F.relu(x)\n", " x = F.max_pool2d(x, kernel_size=2)\n", " x = x.view(-1, self.n_feature*5*5)\n", " x = self.fc1(x)\n", " x = F.relu(x)\n", " x = self.fc2(x)\n", " x = F.log_softmax(x, dim=1)\n", " return x" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of parameters: 14070\n", "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.331818\n", "Train Epoch: 0 [6400/60000 (11%)]\tLoss: 1.055310\n", "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 0.538627\n", "Train Epoch: 0 [19200/60000 (32%)]\tLoss: 0.351977\n", "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.215159\n", "Train Epoch: 0 [32000/60000 (53%)]\tLoss: 0.555545\n", "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.221845\n", "Train Epoch: 0 [44800/60000 (75%)]\tLoss: 0.319424\n", "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.169147\n", "Train Epoch: 0 [57600/60000 (96%)]\tLoss: 0.065263\n", "\n", "Test set: Average loss: 0.1448, Accuracy: 9565/10000 (96%)\n", "\n", "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.130074\n", "Train Epoch: 1 [6400/60000 (11%)]\tLoss: 0.156131\n", "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.127868\n", "Train Epoch: 1 [19200/60000 (32%)]\tLoss: 0.061610\n", "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.156055\n", "Train Epoch: 1 [32000/60000 (53%)]\tLoss: 0.246336\n", "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.159108\n", "Train Epoch: 1 [44800/60000 (75%)]\tLoss: 0.185926\n", "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.067308\n", "Train Epoch: 1 [57600/60000 (96%)]\tLoss: 0.090610\n", "\n", "Test set: Average loss: 0.1085, Accuracy: 9661/10000 (97%)\n", "\n", "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.136817\n", "Train Epoch: 2 [6400/60000 (11%)]\tLoss: 0.069054\n", "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.024138\n", "Train Epoch: 2 [19200/60000 (32%)]\tLoss: 0.058952\n", "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.025060\n", "Train Epoch: 2 [32000/60000 (53%)]\tLoss: 0.162402\n", "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.078908\n", "Train Epoch: 2 [44800/60000 (75%)]\tLoss: 0.073329\n", "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.130581\n", "Train Epoch: 2 [57600/60000 (96%)]\tLoss: 0.072167\n", "\n", "Test set: Average loss: 0.0972, Accuracy: 9719/10000 (97%)\n", "\n" ] } ], "source": [ "# Training settings \n", "n_features = 10 # number of feature maps\n", "\n", "model_cnn = CNN(input_size, n_features, output_size)\n", "model_cnn.to(device)\n", "optimizer = torch.optim.SGD(model_cnn.parameters(), lr=0.01, momentum=0.5)\n", "print('Number of parameters: {}'.format(get_n_params(model_cnn)))\n", "\n", "for epoch in range(0, 3):\n", " train(epoch, model_cnn)\n", " test(model_cnn)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# show some images\n", "plt.figure(figsize=(16, 6))\n", "for i in range(10):\n", " plt.subplot(2, 5, i + 1)\n", " image = list(model_cnn.named_parameters())[0][1][i].detach().to('cpu').numpy()[0,:,:]\n", " plt.imshow(image, cmap='Purples_r')\n", " plt.axis('off');" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'torchsummary'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtorchsummary\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msummary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0msummary\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_cnn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m28\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m28\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torchsummary'" ] } ], "source": [ "from torchsummary import summary\n", "summary(model_cnn, (1, 28, 28))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "conv1.weight tensor([[[[-0.0514, -0.2431, -0.1723],\n", " [ 0.0057, -0.3095, 0.3371],\n", " [ 0.0058, -0.1519, 0.1029]]],\n", "\n", "\n", " [[[ 0.3809, 0.6067, 0.4013],\n", " [-0.0851, 0.5524, 0.4146],\n", " [ 0.3475, -0.2006, -0.2027]]],\n", "\n", "\n", " [[[-0.1586, -0.4720, -0.3614],\n", " [ 0.1608, 0.2759, 0.0646],\n", " [ 0.4119, 0.1763, 0.4591]]],\n", "\n", "\n", " [[[ 0.1649, -0.0511, -0.0633],\n", " [-0.1093, -0.3075, -0.0859],\n", " [ 0.3718, 0.0793, -0.0369]]],\n", "\n", "\n", " [[[ 0.4346, -0.1011, 0.2801],\n", " [ 0.2101, 0.2285, 0.3284],\n", " [ 0.0127, -0.1007, 0.2325]]],\n", "\n", "\n", " [[[-0.0070, -0.2006, -0.0871],\n", " [ 0.4075, 0.0946, 0.3187],\n", " [-0.2273, 0.3887, 0.0815]]],\n", "\n", "\n", " [[[ 0.4000, 0.4220, -0.4082],\n", " [ 0.2722, 0.5044, -0.1749],\n", " [ 0.2318, 0.2525, 0.2803]]],\n", "\n", "\n", " [[[-0.2414, 0.3981, 0.3849],\n", " [-0.0811, -0.3138, -0.1542],\n", " [-0.0118, -0.1257, -0.2086]]],\n", "\n", "\n", " [[[ 0.1675, 0.3784, 0.4560],\n", " [ 0.6253, 0.6030, 0.5062],\n", " [ 0.3326, -0.0977, 0.0698]]],\n", "\n", "\n", " [[[ 0.4483, -0.0163, 0.2090],\n", " [ 0.5148, 0.0363, -0.4205],\n", " [ 0.4959, 0.2006, -0.2910]]]], device='cuda:0')\n", "conv1.bias tensor([-0.1804, 0.4074, 0.2444, -0.0375, 0.1065, 0.1235, -0.1803, 0.0294,\n", " 0.2389, 0.2232], device='cuda:0')\n", "conv2.weight tensor([[[[ 5.9076e-02, -2.6565e-02, -4.1788e-02],\n", " [ 3.1889e-03, -8.6533e-02, -6.9778e-04],\n", " [ 8.4063e-02, 3.7424e-02, 4.5176e-02]],\n", "\n", " [[-1.1259e-02, -4.1894e-02, -2.0995e-02],\n", " [-6.3136e-02, -4.9346e-02, -1.1085e-01],\n", " [-6.6448e-02, -4.8364e-04, 9.2690e-02]],\n", "\n", " [[-9.4987e-04, -6.7392e-02, 1.9795e-02],\n", " [ 9.0485e-02, 7.4686e-02, -2.7720e-02],\n", " [-3.7392e-02, -1.0458e-01, -5.3981e-02]],\n", "\n", " [[-2.5824e-02, 1.1920e-02, -7.6085e-04],\n", " [ 9.5956e-02, -5.3208e-02, -8.6919e-02],\n", " [ 4.7483e-02, 4.9886e-02, 1.8662e-02]],\n", "\n", " [[ 1.5804e-02, 3.7792e-02, -9.3772e-02],\n", " [-7.1214e-02, 3.0196e-03, -5.9971e-02],\n", " [-2.0813e-02, 3.1865e-02, -2.4242e-02]],\n", "\n", " [[-6.1230e-03, 5.3320e-02, 9.7085e-02],\n", " [ 8.1711e-02, 8.2846e-02, 8.0976e-02],\n", " [-7.0254e-02, 5.1828e-02, 2.8898e-02]],\n", "\n", " [[ 7.3385e-02, 4.1816e-02, -7.7529e-03],\n", " [ 8.9649e-02, 3.4419e-02, -7.2960e-02],\n", " [-7.5113e-02, 3.4371e-02, -6.8647e-02]],\n", "\n", " [[ 2.2018e-02, 6.1263e-02, -7.4162e-02],\n", " [ 6.5115e-02, 2.3076e-02, -9.9376e-02],\n", " [ 7.5677e-02, -8.0766e-02, -5.5521e-02]],\n", "\n", " [[ 3.7780e-02, -5.8972e-02, 4.0146e-02],\n", " [-1.6778e-02, 6.0969e-02, 4.8875e-02],\n", " [-5.8669e-02, -6.5347e-02, -5.1612e-02]],\n", "\n", " [[-4.7206e-02, -7.8889e-03, -1.0336e-01],\n", " [ 7.8692e-02, 6.1455e-02, 1.7561e-02],\n", " [-1.0706e-01, -1.9978e-02, -6.2109e-02]]],\n", "\n", "\n", " [[[-1.8537e-02, 9.9011e-02, -2.0159e-02],\n", " [ 8.4831e-02, -5.9867e-02, 8.3410e-03],\n", " [ 8.7308e-02, 8.9856e-02, 6.6945e-02]],\n", "\n", " [[-3.9370e-02, -4.3701e-02, 1.5527e-01],\n", " [ 2.1149e-02, 1.9016e-01, -6.6300e-02],\n", " [-7.1316e-02, 1.0533e-01, 9.6015e-02]],\n", "\n", " [[-8.2563e-02, -1.1281e-01, -1.1413e-01],\n", " [ 4.3033e-02, -7.5594e-02, 4.6499e-03],\n", " [ 1.6978e-02, 5.7545e-02, 9.3755e-02]],\n", "\n", " [[ 7.2455e-02, 4.9544e-02, 5.8619e-02],\n", " [ 5.3484e-02, 2.9096e-02, -4.0237e-02],\n", " [ 3.5845e-02, -1.0365e-02, 7.9267e-02]],\n", "\n", " [[-8.3150e-02, -5.5840e-02, 4.2487e-02],\n", " [-5.2144e-02, -1.5745e-02, -6.1907e-02],\n", " [ 7.9476e-02, 6.5762e-02, 8.1716e-02]],\n", "\n", " [[ 3.7845e-02, 5.4487e-03, -3.9383e-02],\n", " [ 3.4140e-02, -5.4639e-02, -1.1093e-01],\n", " [ 1.6301e-02, 4.6800e-03, 1.0656e-01]],\n", "\n", " [[-9.9955e-02, -3.3375e-03, 1.4250e-01],\n", " [-6.9353e-02, 8.3617e-02, 1.2884e-02],\n", " [-7.5606e-03, 9.2606e-02, 3.9810e-02]],\n", "\n", " [[-1.9099e-02, -6.1345e-02, -2.5744e-02],\n", " [-6.4966e-03, -1.9361e-02, -9.7550e-02],\n", " [-1.2468e-01, -7.2318e-02, 1.8926e-02]],\n", "\n", " [[-7.4540e-02, 4.2869e-02, 1.5220e-01],\n", " [ 7.9301e-02, 5.4725e-02, 7.3146e-02],\n", " [ 4.0899e-02, 7.8887e-02, -6.2909e-02]],\n", "\n", " [[-5.7160e-02, 1.2252e-01, 6.7105e-02],\n", " [-4.6142e-02, -1.2378e-02, 2.3991e-03],\n", " [ 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-0.1077], device='cuda:0')\n", "fc1.weight tensor([[-0.0195, 0.0407, -0.0497, ..., 0.0387, 0.0073, 0.0591],\n", " [-0.0337, 0.0293, -0.0566, ..., -0.1021, -0.0571, -0.0017],\n", " [ 0.0021, 0.0311, 0.0280, ..., -0.0186, -0.0534, 0.0147],\n", " ...,\n", " [ 0.0150, -0.0585, 0.0503, ..., -0.0338, -0.0676, 0.0110],\n", " [-0.0219, -0.0225, -0.0213, ..., 0.0353, 0.0634, 0.0807],\n", " [-0.0557, 0.0409, 0.0342, ..., 0.0260, 0.0347, 0.0383]],\n", " device='cuda:0')\n", "fc1.bias tensor([-0.0015, 0.0131, -0.0586, 0.0559, 0.0051, 0.0570, -0.0521, -0.0528,\n", " 0.0492, 0.0417, 0.0464, 0.0089, 0.0383, 0.0204, -0.0518, 0.0489,\n", " -0.0118, 0.0068, -0.0457, -0.0373, -0.0361, 0.0346, 0.0443, -0.0285,\n", " 0.0407, 0.0626, 0.0078, 0.0578, -0.0300, -0.0030, 0.0601, -0.0393,\n", " -0.0550, 0.0210, -0.0345, 0.0270, -0.0119, 0.0304, -0.0331, 0.0514,\n", " 0.0085, -0.0688, 0.0424, -0.0420, -0.0274, 0.0356, -0.0328, -0.0116,\n", " 0.0268, -0.0090], device='cuda:0')\n", "fc2.weight tensor([[ 1.6251e-02, 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6.1961e-02, 1.7859e-02, 7.1698e-02,\n", " 4.0755e-02, 6.4862e-02, 7.0479e-03, -1.7442e-02, 2.3635e-01,\n", " -2.1890e-01, -1.0068e-01, 1.2437e-01, -1.7878e-01, 3.0244e-02,\n", " 1.0967e-01, 5.9266e-02, 2.9795e-01, 1.6951e-01, -1.3309e-02,\n", " -2.1733e-02, -1.5670e-01, -2.0466e-01, -1.0576e-01, -1.3450e-01,\n", " 6.5420e-02, -1.5616e-01, -4.6581e-02, -1.7628e-01, 2.5693e-01,\n", " -1.0783e-01, 5.0509e-02, 1.7677e-01, 4.8392e-02, -2.0880e-01,\n", " 3.9134e-02, 1.5694e-02, 2.2392e-01, -8.7742e-02, -1.1996e-01],\n", " [ 2.6303e-02, 7.5723e-02, 7.3892e-02, -1.3478e-01, -2.1879e-02,\n", " 1.0987e-03, -2.6124e-02, 1.6876e-01, 2.3617e-01, -9.9598e-02,\n", " -8.9752e-02, -1.6628e-01, -1.3200e-01, 5.0376e-02, -2.4802e-01,\n", " -8.1146e-03, -6.6615e-02, -1.3746e-01, 1.1526e-01, 1.6665e-01,\n", " -2.1015e-02, -2.2107e-02, 2.9302e-02, 1.7231e-01, -2.1866e-02,\n", " 9.2823e-02, -1.2384e-01, -1.1497e-01, -1.0558e-01, 7.4382e-02,\n", " 2.2958e-01, 6.3308e-02, -1.4583e-02, -1.6542e-01, 1.0382e-01,\n", " 4.6560e-02, -2.6075e-01, 1.6569e-01, -2.3854e-01, -2.3205e-01,\n", " -1.2321e-01, 1.2136e-01, 5.2090e-02, 8.2703e-02, 1.7197e-01,\n", " -8.2319e-03, -3.2253e-02, -1.0089e-01, 1.0214e-01, 6.3957e-02],\n", " [-1.0272e-02, -2.1517e-03, 2.2925e-01, 4.0837e-02, -1.1391e-01,\n", " -2.4873e-02, 1.0643e-01, -2.4538e-02, 6.0559e-02, 7.1217e-02,\n", " 9.5310e-02, 2.2550e-01, -3.6936e-01, -1.0303e-01, 9.5995e-02,\n", " 1.6429e-02, -5.9848e-02, 1.5635e-01, -1.5026e-01, 2.7174e-02,\n", " 2.4192e-01, -1.0771e-01, 1.1466e-01, 1.9647e-01, -1.6442e-01,\n", " -1.3025e-01, -1.0987e-01, -2.5069e-01, -1.0778e-01, 5.3117e-02,\n", " -9.3216e-02, -8.1073e-02, 1.2159e-02, 1.5550e-01, -6.3092e-02,\n", " 1.4193e-01, 1.6516e-01, -4.7558e-02, -1.3505e-01, 2.4961e-01,\n", " 6.4121e-02, 7.6099e-02, 1.7728e-01, -4.8860e-02, 1.8123e-01,\n", " 1.1346e-01, 2.6336e-03, -8.3779e-02, -2.8640e-01, -5.4847e-02]],\n", " device='cuda:0')\n", "fc2.bias tensor([ 0.0657, -0.0037, 0.1450, -0.1473, -0.0027, -0.1079, -0.1483, 0.0354,\n", " 0.0860, 0.0722], device='cuda:0')\n" ] } ], "source": [ "for name, param in model_cnn.named_parameters():\n", " if param.requires_grad:\n", " print (name, param.data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Автокодировщик" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "class Autoencoder(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.encoder = nn.Sequential(\n", " nn.Linear(28 * 28, 10),\n", " nn.Tanh(),\n", " )\n", " self.decoder = nn.Sequential(\n", " nn.Linear(10, 28 * 28),\n", " nn.Tanh(),\n", " )\n", "\n", " def forward(self, x):\n", " x = self.encoder(x)\n", " x = self.decoder(x)\n", " return x\n", " \n", "model = Autoencoder().to(device)\n", "criterion = nn.MSELoss()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# Convert vector to image\n", "\n", "def to_img(x):\n", " x = 0.5 * (x + 1)\n", " x = x.view(x.size(0), 28, 28)\n", " return x\n", "\n", "# Displaying routine\n", "\n", "def display_images(in_, out, n=1):\n", " for N in range(n):\n", " if in_ is not None:\n", " in_pic = to_img(in_.cpu().data)\n", " plt.figure(figsize=(18, 6))\n", " for i in range(4):\n", " plt.subplot(1,4,i+1)\n", " plt.imshow(in_pic[i+4*N])\n", " plt.axis('off')\n", " out_pic = to_img(out.cpu().data)\n", " plt.figure(figsize=(18, 6))\n", " for i in range(4):\n", " plt.subplot(1,4,i+1)\n", " plt.imshow(out_pic[i+4*N])\n", " plt.axis('off')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "learning_rate = 1e-3\n", "\n", "optimizer = torch.optim.Adam(\n", " model.parameters(),\n", " lr=learning_rate,\n", ")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "epoch [1/20], loss:0.5505\n", "epoch [2/20], loss:0.4810\n", "epoch [3/20], loss:0.5168\n", "epoch [4/20], loss:0.4971\n", "epoch [5/20], loss:0.4869\n", "epoch [6/20], loss:0.5054\n", "epoch [7/20], loss:0.4592\n", "epoch [8/20], loss:0.5120\n", "epoch [9/20], loss:0.4975\n", "epoch [10/20], loss:0.5090\n", "epoch [11/20], loss:0.4751\n", "epoch [12/20], loss:0.4523\n", "epoch [13/20], loss:0.5073\n", "epoch [14/20], loss:0.4510\n", "epoch [15/20], loss:0.5025\n", "epoch [16/20], loss:0.5128\n", "epoch [17/20], loss:0.4349\n", "epoch [18/20], loss:0.4624\n", "epoch [19/20], loss:0.4192\n", "epoch [20/20], loss:0.5033\n" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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Wn33nbt2X/dD0t7zZTKLvP8uC0Xv7ny7eJfP//E1/9+3kM7ove9iUsetu0+WwD97zpMzvHH9R5ndMn/Rmy3392edP6/m4dsl4cI2bLh6OsPfWmPOuK1antTVcnQtjsDUvDFs6788OZH7fpqMy350ueLPY6fv3XK4XOyeH62TeiPVn3xh3vNn6RM859Ug/GwuFnhf+/OJ9Mt/yZf1OVvdUZ1Z/tmTF6LseGO8xY41q5UlfrHWufZX2DalIjPVsTU8ccash8+GM7mQ/c7f/Hv3NHZ+XY8fjauuJU7n+7h97+pXebP/XL8ixxcKi/uHGGtF676Vzl2T+vkff7s3iTD+YzVN6zmnM68+edXRuvYuUUc0L/EUfAAAAAICAsNEHAAAAACAgbPQBAAAAAAgIG30AAAAAAALCRh8AAAAAgICw0QcAAAAAICBs9AEAAAAACEiFxr/LIDoBrb7AxOg4TXX9q2ueM/ozL+p+zGLO32G592un9Ni+7rWN9u+W+cGfn5L5B3/mz7zZG5vzcmwr1t/bMiz1d/vuN3fJfG9+xB8mRqduXfcRu1SPL5NV/L1WQHXZVbu/Vf9w1e7iwrhFBlO69/mVG3Tf+67U33mdOl1G3y11X/bHF2/T+cd/Uua3fqXtzdJzel4opnTR+NLNui/4y1v3ynz3vnMyf+W4v6d8Odd9wg/PrJf54Lx+zVnvorSr83hYofzW6hu+nlSd49S5KKsdPDdee7UJ/WzuqOvnJxMfb+D0nLNQZDIvjL/HnBtOylyt8gqnb+6pWH/2QwO9VvnkZ+6X+e4vHZJ5JN7pY3s2y7HDMWN5a7zHSmM5MmyMbj1R8Xa/YZTGJSgy/Q+Kln72yrq+CZa364ll/CUXvdnudFmOjZ1+J/fKoczP5fqdHV0UiyXjvRSP6c9WrvT0zzbW4mVNX5d1T/g/e3dWPzyNC/q71Rf1IjPp6Xwwpu+ZQsSjeu75iz4AAAAAAAFhow8AAAAAQEDY6AMAAAAAEBA2+gAAAAAABISNPgAAAAAAAWGjDwAAAABAQNjoAwAAAAAQEKNodHSs7mKjetalHaMLcUHnrTnd81j7/mlvls/7uzGdcy7esU3mB39Bd8/+4Vv/ROavb/r7N+uRUSRe0VKh+4bHThi/Oyr9HZRRXX/2MtO3a5ka3ZxxxZLKCsPpxf0x4tGMc/3cFsaJLIwZLZ4YyPz21nGZtyJ/CWqhvphz7kWjb/33vvH3ZH7LVzsyz14UXfWF7n6Njd7ctKt7c1+19UWZ76/751PnnLutdsGbnRnq+fJ/Te+V+WBc3xRxT99TkfEuklXjxnnF/6XWBEadu72eMP+B1in0u6kj5qVerj/8SqmPnRsvnom4K/P1Ip8xTmwr1p3QJwfrZL7zc0syLy4tyDxq1L1ZdkaPTaaMrm+jy7toGuuNSHd9FzVe+leFOI2ltdxMjfVCzVgzive9c871Jo1ns+HfZ/SMKWlobIJ65VAfwLLe/9nO3btBDk2762W+7plpmVtr9aXdkzIfiPOe6mXSZeTGWqmv88K651rX/u/r/EUfAAAAAICAsNEHAAAAACAgbPQBAAAAAAgIG30AAAAAAALCRh8AAAAAgICw0QcAAAAAICBs9AEAAAAACIjROr16okKXTNaWdd445++IdM657OiczIdzopNadME751y+bkzmt995VOavqM/LvB7p44/S033db7nlq0Yv7pi/27bMjNuxrvuGy0x3nlqdqmVi5CKO9C1RtcY5KOpcWOfRUuhqYzc5oTunt2UXZR6L340WTn/4c7l+buNz+v6OBssyl1KjE7rl76t2zrkz9+pn64+3fEHme9KmzJdL//E3p3pOqTcGMu/X9cNXZMZzP8pfh0d0bf+tCvOClccDfZ67bf3sPbW4XeYbUn9ffBbpvutdtfMy35wsynxr0pf5TOJ/tlOnn/sk0je/9WwOJvV5zQb6s7vYf93irl7jxcazVVrrCaPrOx5WmFeMx571wmWyrnFiXCNjzVfUjMnfuI7nl/zv/EuFvv9my1zm1v29M9Xzxvte4X9nb7pHj92VXpD5rxz6RzKfrut12MEX9XohPeG/LuPH5VBXW9Yvi6Snz3tkPPfxQN8z6l1lrTWudF7gL/oAAAAAAASEjT4AAAAAAAFhow8AAAAAQEDY6AMAAAAAEBA2+gAAAAAABISNPgAAAAAAAVm1ej1VVeacc4lupHG1Rf0P0rO6HqJY0LkrdMWCEuW6vuF8V9dsZUalzSjlRnXgY+39Ml/YNyHz+qy/Xi9r62sa9fVns+pwhg1dEVYY9XtWlQouU+nvCIkK4yQb9SJFpv/BdEvXujQiXdWmKvSMRlDXinUdVGOPMWfVjO5AUReVz4zLoadeq2szv/TOD8h8NtF1OEOn59PM+Z/NLNJj01jPCz3rLVdxupW1m9RkXTZ1rqzTaNwiLtGPvUvP6mfr68kumT8/NevNtkzq5/o9O74k8901Xau5wXj2ski/96q4r+GvFXTOueGvG1XBZ/V6wh3xd2WVfT1XR7HxYKfGebFuOvEec05XRFtVvrg81j7Cqs+z1oxVq1eHQ/8/yI0Fparydc65uvFcJ4l+Pv7JxAvi2PqlmUS6lvORA5+S+XKxIvMPrXuZzP/LwgPeLO3o85Yt6X1GbO0zrG1ClartEa0X+Is+AAAAAAABYaMPAAAAAEBA2OgDAAAAABAQNvoAAAAAAASEjT4AAAAAAAFhow8AAAAAQEDY6AMAAAAAEBCrYXjVxANdKJgu9WUetXVxrllXGPs7KqNMn7bBVEPmzUz36q6mk3lH5h8/dJfMh2/T4/vz/nMz/R3dBzx2RpclW/2VeU0XYOZGZ2ok+9/1zx5VP2ZorI5So3rWFcaMZnWuXypaMu+U/nnF+q1pUequ7pumL8n89F079Q94pf+zD/WU5FZu0/PlTem4PoBJd/52Sv983i5qcmx/qI9tdaxbz6bsvb2MHJepwnlM+npwXT9aLmvrPD+jH6Ay9ucHb52QY38wOyvzV9YvyDy2JsURakb62fyT/f9V5u/pvFPm0bppf5hbD7YhsgqxKx5evGqs/vVVvKTXF+M8Wee5MNZ8eV0foD+px2+d8a/118c9Odbqqreee+vZTKLV+ztvPdJroTuax2Qez/jXC2ouds65ODdeNIWRpxXPm9hHjOrB5y/6AAAAAAAEhI0+AAAAAAABYaMPAAAAAEBA2OgDAAAAABAQNvoAAAAAAASEjT4AAAAAAAFhow8AAAAAQECM1unRMbuJjS7DaGWoDxDr32FETd21mLT8ne7FzVvl2KMP6dP64Z0PyzwzOqdH6a1P/pLM8+emZP7H7/ywzJ/o7PGPnb5fjl05OCbzbEnGLh4YudHFnHVUxyVl2leD2UlesdN8vuN/rp1z7uCKfran4443y43fmz7WvlXmR85slHlLps6pWt5Y19a67bMXjaOPVqf0P5xHe5v02AV9TVtt3U2bdvVNEw+Mm0r24uJqsNYDaVePry2JUnNnz/32vKT+ge6zfuyO3TL/++Pfkfm6WH+4Ua4mhk532U/F+tmb+6nNMl//nP/CpueMF77xXJaZPjNloufz0vhuVoc7qiut2nHjGuX60XS9CT2+s03f/w/MHvJm1rMRG+uJJFq7N9ig1OelcHo+vr12VuZ7t855s+Prd8mx46f0eYt7+rNbzHtyFazdOwUAAAAAAPydsdEHAAAAACAgbPQBAAAAAAgIG30AAAAAAALCRh8AAAAAgICw0QcAAAAAICBs9AEAAAAACIgufF9NumbRudToOB03WqenxmXc3eHviz/9z1bk2I+9QnfJH6jpQvcs0qXXeek/OYVRNH7HH71H5s2zRpf8pIzdvXXdQbk7fdof3q6P/YnJO2V+7rT/mjnnXHZen9fGOV2AmfT852aUXcU3FLOvWsdpR1/D+XP6Bv7i+H6ZH5uc8WbPXtwqx774bZ1veVR/uYmD52Wej9e92YWf0PPdF176CZk7p58di5qznHPuXO6/bk/M75JjszP6s9Xn9XmtLek8Hhgd6+Kr0aV9dcRDoyu+q++vbEm/c+PuUOd9nZeR//4dn56WY584ukvmj2/YKfNW80WZzyT+ecGyVPRl3jG66i8Veom58EBX5slK05tZPeRJV1/zoqbf2nndWGMmRmG2itdg1/Z1yTiP1vyb1/UBBpM6TzfqvcAdzePerBHp+y82vpz1TrX2Asqg1Ov4BWNeSMR86Jxz6+KGzGeTmswf2vxtb/ZbN2+XYyeO6znJet+b91xqzQvX/uFnGQIAAAAAQEDY6AMAAAAAEBA2+gAAAAAABISNPgAAAAAAAWGjDwAAAABAQNjoAwAAAAAQEDb6AAAAAAAERBcKViXqAs2GR6NqMG8avc4tnfdmdLfsmXv9HZf//k7dOX1HTXdQ1iPdEWn1X/ZKf6fvw11/z7dzzt30Fd1bu7BL91suvrYj88zoBp1N/L24b5t4Ro5t7NJdxp9rHpD5wWiLzLNFfV3oxB69yJgYEl2N7GoLOs9P63nh+cE2nWdbvVnrkL5/dj2mO3frh87IvOwYndMb/c9+f3JCjs2NPuyqvc/dUvfuPrGyy5s9f0hfk3XH9M9uXtB9w+mK/u7Wc6/6tK+8yRg/Lu7rM5kt63dDekk/O1G3pz9AVz+7LvffYxPjel5YfG5c5h+ceIPMD+/S780DzRPe7MxwSo59YuFmme8fm5P5oNTrgX/80m/I/KOnXyNSvVZpndXLW+tdUxh92MZXc+Uq9GXj/2Veo1hfo6G+xVytruedLNK5Yu0DCqffa+dyPac92dvkzR6+dLsc+4qJozJ/cEznsbGgqEf62b2n+QNvNr39khzb3bhe5mlP3zSR3t65vKa/WyEOb81JV4qtCwAAAAAAAWGjDwAAAABAQNjoAwAAAAAQEDb6AAAAAAAEhI0+AAAAAAABYaMPAAAAAEBA2OgDAAAAABAQXVZYVZVOQKOCtKzp31EMm/qrtTcbBZu3tL3RtkSXddcj3dVtsfox53J/J/W/euQdcuzWTfq8TbzzpMy/fttnZG7JIv95n030Z7uzqbs5D036e0Gdc+5QTefW/RqL/szSuF9H1Y8ZmqjQJyrRdeyuph9NF+X6QtUu6Wc3Eo9m86z+7PVj8zIvLhkf3lLzf/bsdeerHduQl9acpfuEP3L8Xm82fkhfk9Z5XWybLevPZrG6mPOEvuyrISr9z0/c19cwaeuJIWp3ZV52Voy8o/Oev7M6flaPnditO6sXoxmZ/9mR18q8TP3ndeM39Dt3Zb2+t+cfGpP5X96q1ws/GAxkfvQ+f+f13xQvlWPzw3oNWFuy3jU6t/q0cZVUWDuV0WjnZjFlOeecWyn9766B8c7M1GLDOdcp9LPzdH+DzN9/+M3ebG5uWo79mfuflPmGRM8LVW2M/euJAxtPy7GP36Tn06SrX/hZ15gXrOWGuCWtW/1K9xH8RR8AAAAAgICw0QcAAAAAICBs9AEAAAAACAgbfQAAAAAAAsJGHwAAAACAgLDRBwAAAAAgIKOt1xOsmoAy1rUYhVHFVhq/whg29fHXTfgrcazai8IoSVAVc845s2MhEx/9rv1GBd3GjTJ/tGJ93ijlxu+l5vu60qNo65ou0djxQ+q60LB1dVgVhwP9D2pDnacr+kJZ84aqWKzP67obV+h5o+zr8ZEx5+VTDW/2zt2PyLHmnGTolfrh+ev2Ppkfe3aLN9twxrimHX1ercpG611j5zLG5RKXKc6NSqO+MXn3jF7OoR5fVsitsdOfflbmyU8fkPnghL4BU3/znxs7YdQGxi2Zf3bv52XunH7n7hTVf8459/YN3/Rmz+3zzxnOOdfu6hotd1I/13Wj7TQx5hWMnlVlFlkbDWPdFht1vN2evr/nBv6aukHzlP7hhizSz/3mZFHmd2044c1eu+eLcuwDTT1vWM99VYmoTZyp6c82HNc3zWBcr4Wsey4x1qhm/d4IsEQBAAAAACAgbPQBAAAAAAgIG30AAAAAAALCRh8AAAAAgICw0QcAAAAAICBs9AEAAAAACAgbfQAAAAAAApKO9OiigrI0+iuL1OguNvKq8sL/O5DBiIuTE6Mfc0zk/27Hp+XYvXt01/yo9Up/V/j5XJSUO+ee6I1zOvoAAAnASURBVOyR+VOnt8m8dl73Y6ZGNajqcl6Nbswbkdlh2tP/IO5bnev6+PHA/w/Si105tmwb+dD/bDjnXJnreeHYm5re7L7WITk2i3TvbV7qE3Mi15/9v598hcybc/7vlhjXzOq5L4y3nD1+tO8a/Ig4zUVirAcyo/tY9C4755wz7oEoNW4i8e4qjfda2RNF9865iYe/q8fv3yXzuVdNeLNTr6vLsUcf+o8yr0r1YTvn3HTifymvH9Mv7MXmjMytNagzKthxjajrZFyjyMr1o+nSZZ3nl2oyf6691Zs9MHZQjm0ZHy42/k67PdXv5F/e+BVvdkuqj12P9PeualDq735q6J+PDy9tlGPjrv5uq7mWt+7XK8Vf9AEAAAAACAgbfQAAAAAAAsJGHwAAAACAgLDRBwAAAAAgIGz0AQAAAAAICBt9AAAAAAACwkYfAAAAAICAGOWw1ci6eaMv0Oo+zmtWF6L+AfUFnZ89Me3NHt19ixy7Nf2ezKdi3UFp9WO2Yn/n9d5E92FXZfVpd8u+zA8P/KWon13SXdsfP3SXzIfPT8q8dUYX59aW9D0ha03p3L06KlaWR7m+EGlnKPOkre/fqOPvvI6W2nJsvrgoc1fqzx7Xr/zZvrvCWOecWyxWZP5Xyy+V+Yun18t8TEwrw4bRcV7oPM6r3VTyPeacK40Odj2YieP/KEWnemG87/Mx/U6NJsZ0viRj54y+9yirsJSq6y774ibdC/39f9qS+Sff8nve7GXGzx61TqG7vl/ob/FmF9r6e6cdfc3SFWO+Nd4l5ju/4rsMP1SK82idYqOK3rzGzQt6vVsc1M/9w/Ht/rEv13PaW2aelnkr8q9FnHPu1HCdzB8cO+o/dtyQY6salPrCHBt2Zf7Ri6/xZt85sk2OHTuv75qsre+JpG/sE/Qtsyr4iz4AAAAAAAFhow8AAAAAQEDY6AMAAAAAEBA2+gAAAAAABISNPgAAAAAAAWGjDwAAAABAQNjoAwAAAAAQkArlrzbVJ6i6MZ1zrkz0P8iNmsdY12W7+qLucdz4eOLNPnTpQTn28/cckPmDm56R+XTSkflMsuzN3tjSvbR5qUsel0vdzXl44D8vzjn32cU7Zf6lM/u82fEjui947EV9u05cMDrUu/q7x0bnKlaf2WmeGhOL1Yfd1xNHtNT2ZsWS/7n84T8wepsbelKLbvJ3SjvnXNL1f7eqz/0TPd3J+8h5/3PtnHNlWz+7hYjNuT7X17TUU6KLjOti3TOVjPLYASlq+jz1p2r6ANGEjJOmHh919PMRDcS8YVzjYmpM5udfPinzW19yXOYTsXoA6nJsVcvFisyf7Ovr8lcX7vBmF09MybGT8zI2+7CdEVsl7rL/3To2/laVc2WNjYw1X62j/0HzvH65bPq2/737g0/sl2M/OK33ESszejHU3ahv0D993Slv9siBT8mxVdcTT/X0nPcHp94k8299Z7c3Gz+i1xoNc5+gc7WvvSyr8MrnL/oAAAAAAASEjT4AAAAAAAFhow8AAAAAQEDY6AMAAAAAEBA2+gAAAAAABISNPgAAAAAAAWGjDwAAAABAQHTh4BpWJNXKCOOh7kpsnfX34o6f0mN7n52V+adWXi9zV+rjr2xqebP3v/escWh93s4ujsu8e97/s51zLptPZF5b8P/86SU51GUdfV6sXlyz/5Ju21Vn3J5mB+mwYfzucjIzPoC+v9PYf/y4aRS+G6w+7fZN+tkcTPlv4D9d3CrHzg10J/XfzPt7a51z7tDcRpnHXX1dqnQll1YXvXHwMtbj6by+NiLx3rOucWHUwffW1WQej+ulUNJryjzK/Z992NLvxOVtek6af7l+cd3S6Mj82f5mb3ZTelGOXSn96yDnnHtxqK/L1zq3yvxTp18m88NH/J+9dVxfs2y54nrAehdZHe3MG1eHOo/GNSqN5UBh7IBi4/627oG4639+smXdNd/4vn6uJ/t9/cML4733uQ3e7F1/9Bo5NjcWakcW/Md2zrlTp9fJvHZSz9dTc/6fX1uquE+wnttqW89VwV/0AQAAAAAICBt9AAAAAAACwkYfAAAAAICAsNEHAAAAACAgbPQBAAAAAAgIG30AAAAAAALCRh8AAAAAgIAYLZIViT7Cqh2j5nir4zTXedr1/4OkPZBj447Oo77OXaJ//1K/4M8Hv6P7rIdjutN3uq5LIseN3OoljUS3Z2xck3hYsReXXtu1z+pEF13bPxyv47yhn61+rPtbB5P+vIwm5djhmP7ZnY1GvkXGbjDj7+x9/1M/LceWhfFc9/S8ES3rB7++oL9bKiqDY6MuWM0pl8N+l1j33HVYrLsGlRXOozk21dewMP7mYd0jiVgvZIv6fT/d1l3146cymb/w2F6Z/5ud+73Ze7fol25Zt16qBqOHPF3S88rYvH98/aK+KFnXWC/kFdcTuP4Z80ahHz1XZMa8E1d4N6TGOzeuy7ys67XMcMI//tDv3CbH9sf1fDlsythNG+fFeqcnPX9u7e1uRPxFHwAAAACAgLDRBwAAAAAgIGz0AQAAAAAICBt9AAAAAAACwkYfAAAAAICAsNEHAAAAACAgo63XG6HSaq0wfoVh1WIMW/5qiyLRY5OmcVrzhs4NRc3/2cqKtRWx0fxntBS50vrVkfp4VotVxUpFrBEjrCMz5wXdWOOGRv2e/NnGvDA0qimt+7e2YMw7K/4uoDLRPUFWJU2sG8DMCrzUqLpKeuLYA2POsup0qs4L1OddG1VOs/Veqlq/V2FiiQfVnvt4oHveGhet8f5seFLPd3ld59ZayJIYz3YkKnVTUbHlnHNJ37opdIw1YoTTr7VeLYz1glXXOyj9FXdFqsfGQ71PsOohreOXIs/aes6xqiezrlFbmFSrSa7iRqzN5C/6AAAAAAAEhI0+AAAAAAABYaMPAAAAAEBA2OgDAAAAABAQNvoAAAAAAASEjT4AAAAAAAFhow8AAAAAQECMwvfrmNGbWxjfvEhFgeaYUa45amWFAljjvJh1wdbhb8COSlxDFTvNq97fVVi9zonRRe8u6fEj/W7Gsa3nfqTzQpX50LnK9xTWgIqXsDTugbxm5Sod8XrB+O6qqz4WPfXOOeeWdWzNOZH1aFp12qPsureOzbRwwysTfRMMG1Yu/pa6Tm9CRnrvV2SuNUY8L+Dvhr/oAwAAAAAQEDb6AAAAAAAEhI0+AAAAAAABYaMPAAAAAEBA2OgDAAAAABAQNvoAAAAAAASEjT4AAAAAAAGJyqodxAAAAAAAYM3gL/oAAAAAAASEjT4AAAAAAAFhow8AAAAAQEDY6AMAAAAAEBA2+gAAAAAABISNPgAAAAAAAfnf9dGccOwX/UsAAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "num_epochs = 20\n", "# do = nn.Dropout() # comment out for standard AE\n", "for epoch in range(num_epochs):\n", " for data in train_loader:\n", " img, _ = data\n", " img = img.to(device)\n", " img = img.view(img.size(0), -1)\n", " # noise = do(torch.ones(img.shape)).to(device)\n", " # img_bad = (img * noise).to(device) # comment out for standard AE\n", " # ===================forward=====================\n", " output = model(img) # feed (for std AE) or (for denoising AE)\n", " loss = criterion(output, img.data)\n", " # ===================backward====================\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", " # ===================log========================\n", " print(f'epoch [{epoch + 1}/{num_epochs}], loss:{loss.item():.4f}')\n", " display_images(None, output) # pass (None, output) for std AE, (img_bad, output) for denoising AE" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Производные по входам + DeepDream для нашего модельного случая" ] }, { "cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [], "source": [ "for name, param in model_cnn.named_parameters():\n", " if (\"bn\" not in name):\n", " param.requires_grad = False" ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [], "source": [ "x = torch.randn(1, 1, 28, 28, device=device, requires_grad=True)" ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [], "source": [ "class myDD(nn.Module):\n", " \"\"\"\n", " делаем модель, у которой параметры - входное изображение\n", " \"\"\"\n", " def __init__(self, model, x):\n", " super().__init__()\n", " self.weights = nn.Parameter(x)\n", " self.model = model\n", " # self.do = nn.Dropout2d(0.5)\n", "\n", " def forward(self):\n", " return self.model(self.weights)\n", "\n", "model = myDD(model_cnn, x)" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [], "source": [ "def trainx(nepoch, model, class2learn=0):\n", " \"\"\"\n", " - сколько эпох\n", " - какая модель\n", " - на какой класс\n", " \"\"\"\n", " images = []\n", " target = torch.tensor([class2learn]).to(device) #torch.tensor([1,0,0,0,0,0,0,0,0,0]).unsqueeze(0).to(device)\n", " \n", " # ядро для сглаживания\n", " cnv = nn.Conv2d(in_channels = 1, out_channels = 1, kernel_size = (3,3), stride = 1, padding=1).to(device)\n", " cnv.weight.data = cnv.weight.data*0 + (1.0/9.0)\n", " cnv.bias[0] = 0.0\n", " \n", " for i in range(nepoch):\n", " model.train()\n", " optimizer.zero_grad()\n", " output = model()\n", " #print(output.shape, target.shape) \n", " loss = F.nll_loss(output, target)\n", "\n", " loss.backward()\n", " optimizer.step()\n", " if i % 100 == 25:\n", " # сглаживаем\n", " model.weights.data = cnv(model.weights.data) \n", " \n", " if i % 100 == 0:\n", " print(f'i={i} loss={loss}')\n", " images.append(model.weights.squeeze().detach().cpu())\n", " return images" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "i=0 loss=0.7772314548492432\n", "i=100 loss=0.00047588348388671875\n", "i=200 loss=0.00027561187744140625\n", "i=300 loss=0.0002117156982421875\n", "i=400 loss=0.000156402587890625\n", "i=500 loss=0.000125885009765625\n", "i=600 loss=0.00010585784912109375\n", "i=700 loss=9.250640869140625e-05\n", "i=800 loss=8.296966552734375e-05\n", "i=900 loss=7.534027099609375e-05\n" ] } ], "source": [ "optimizer = torch.optim.Adam(model.parameters(), lr=0.1)\n", "images = trainx(1000, model, class2learn=0)" ] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(18, 6))\n", "for i in range(10):\n", " plt.subplot(2,5,i+1)\n", " plt.imshow(images[i])\n", " # (f'i = {i*100}')\n", " plt.axis('off')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 166, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.imshow(x.squeeze().detach().cpu())\n", "plt.show()\n", "plt.imshow(model.weights.squeeze().detach().cpu())" ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "i=0 loss=0.00019741058349609375\n", "i=100 loss=0.0001697540283203125\n", "i=200 loss=0.00014209747314453125\n", "i=300 loss=0.0001316070556640625\n", "i=400 loss=0.0001201629638671875\n", "i=500 loss=0.000110626220703125\n", "i=600 loss=0.0001010894775390625\n", "i=700 loss=8.487701416015625e-05\n", "i=800 loss=8.106231689453125e-05\n", "i=900 loss=7.2479248046875e-05\n" ] }, { "data": { "image/png": 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\n", 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\n", 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