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rasbt--deeplearning-models
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basic-cnn
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rasbt c8702ff168 upd header sand fix image links
2019-06-05 23:56:41 -05:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
"- Author: Sebastian Raschka\n",
"- GitHub Repository: https://github.com/rasbt/deeplearning-models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Sebastian Raschka \n",
"\n",
"CPython 3.6.8\n",
"IPython 7.2.0\n",
"\n",
"torch 1.0.1.post2\n"
]
}
],
"source": [
"%load_ext watermark\n",
"%watermark -a 'Sebastian Raschka' -v -p torch"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Runs on CPU or GPU (if available)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convolutional GAN with Label Smoothing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Label Smoothing: Replace Real images (1's) by 0.9, based on the idea in\n",
"\n",
"- Salimans, Tim, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. \"Improved techniques for training GANs.\" In Advances in Neural Information Processing Systems, pp. 2234-2242. 2016."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import numpy as np\n",
"import torch\n",
"import torch.nn.functional as F\n",
"from torchvision import datasets\n",
"from torchvision import transforms\n",
"import torch.nn as nn\n",
"from torch.utils.data import DataLoader\n",
"\n",
"\n",
"if torch.cuda.is_available():\n",
" torch.backends.cudnn.deterministic = True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Settings and Dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image batch dimensions: torch.Size([128, 1, 28, 28])\n",
"Image label dimensions: torch.Size([128])\n"
]
}
],
"source": [
"##########################\n",
"### SETTINGS\n",
"##########################\n",
"\n",
"# Device\n",
"device = torch.device(\"cuda:3\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"# Hyperparameters\n",
"random_seed = 123\n",
"generator_learning_rate = 0.0001\n",
"discriminator_learning_rate = 0.0001\n",
"num_epochs = 100\n",
"BATCH_SIZE = 128\n",
"LATENT_DIM = 100\n",
"IMG_SHAPE = (1, 28, 28)\n",
"IMG_SIZE = 1\n",
"for x in IMG_SHAPE:\n",
" IMG_SIZE *= x\n",
"\n",
"\n",
"\n",
"##########################\n",
"### MNIST DATASET\n",
"##########################\n",
"\n",
"# Note transforms.ToTensor() scales input images\n",
"# to 0-1 range\n",
"train_dataset = datasets.MNIST(root='data', \n",
" train=True, \n",
" transform=transforms.ToTensor(),\n",
" download=True)\n",
"\n",
"test_dataset = datasets.MNIST(root='data', \n",
" train=False, \n",
" transform=transforms.ToTensor())\n",
"\n",
"\n",
"train_loader = DataLoader(dataset=train_dataset, \n",
" batch_size=BATCH_SIZE,\n",
" num_workers=4,\n",
" shuffle=True)\n",
"\n",
"test_loader = DataLoader(dataset=test_dataset, \n",
" batch_size=BATCH_SIZE,\n",
" num_workers=4,\n",
" shuffle=False)\n",
"\n",
"# Checking the dataset\n",
"for images, labels in train_loader: \n",
" print('Image batch dimensions:', images.shape)\n",
" print('Image label dimensions:', labels.shape)\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"##########################\n",
"### MODEL\n",
"##########################\n",
"\n",
"class Flatten(nn.Module):\n",
" def forward(self, input):\n",
" return input.view(input.size(0), -1)\n",
" \n",
"class Reshape1(nn.Module):\n",
" def forward(self, input):\n",
" return input.view(input.size(0), 64, 7, 7)\n",
"\n",
"\n",
"class GAN(torch.nn.Module):\n",
"\n",
" def __init__(self):\n",
" super(GAN, self).__init__()\n",
" \n",
" \n",
" self.generator = nn.Sequential(\n",
" \n",
" nn.Linear(LATENT_DIM, 3136, bias=False),\n",
" nn.BatchNorm1d(num_features=3136),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
" Reshape1(),\n",
" \n",
" nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=(3, 3), stride=(2, 2), padding=1, bias=False),\n",
" nn.BatchNorm2d(num_features=32),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=(3, 3), stride=(2, 2), padding=1, bias=False),\n",
" nn.BatchNorm2d(num_features=16),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" nn.ConvTranspose2d(in_channels=16, out_channels=8, kernel_size=(3, 3), stride=(1, 1), padding=0, bias=False),\n",
" nn.BatchNorm2d(num_features=8),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001),\n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" nn.ConvTranspose2d(in_channels=8, out_channels=1, kernel_size=(2, 2), stride=(1, 1), padding=0, bias=False),\n",
" nn.Tanh()\n",
" )\n",
" \n",
" self.discriminator = nn.Sequential(\n",
" nn.Conv2d(in_channels=1, out_channels=8, padding=1, kernel_size=(3, 3), stride=(2, 2), bias=False),\n",
" nn.BatchNorm2d(num_features=8),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001), \n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" nn.Conv2d(in_channels=8, out_channels=32, padding=1, kernel_size=(3, 3), stride=(2, 2), bias=False),\n",
" nn.BatchNorm2d(num_features=32),\n",
" nn.LeakyReLU(inplace=True, negative_slope=0.0001), \n",
" #nn.Dropout2d(p=0.2),\n",
" \n",
" Flatten(),\n",
"\n",
" nn.Linear(7*7*32, 1),\n",
" #nn.Sigmoid()\n",
" )\n",
"\n",
" \n",
" def generator_forward(self, z):\n",
" img = self.generator(z)\n",
" return img\n",
" \n",
" def discriminator_forward(self, img):\n",
" pred = model.discriminator(img)\n",
" return pred.view(-1)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GAN(\n",
" (generator): Sequential(\n",
" (0): Linear(in_features=100, out_features=3136, bias=False)\n",
" (1): BatchNorm1d(3136, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): LeakyReLU(negative_slope=0.0001, inplace)\n",
" (3): Reshape1()\n",
" (4): ConvTranspose2d(64, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (5): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (6): LeakyReLU(negative_slope=0.0001, inplace)\n",
" (7): ConvTranspose2d(32, 16, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (8): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (9): LeakyReLU(negative_slope=0.0001, inplace)\n",
" (10): ConvTranspose2d(16, 8, kernel_size=(3, 3), stride=(1, 1), bias=False)\n",
" (11): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (12): LeakyReLU(negative_slope=0.0001, inplace)\n",
" (13): ConvTranspose2d(8, 1, kernel_size=(2, 2), stride=(1, 1), bias=False)\n",
" (14): Tanh()\n",
" )\n",
" (discriminator): Sequential(\n",
" (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): LeakyReLU(negative_slope=0.0001, inplace)\n",
" (3): Conv2d(8, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (5): LeakyReLU(negative_slope=0.0001, inplace)\n",
" (6): Flatten()\n",
" (7): Linear(in_features=1568, out_features=1, bias=True)\n",
" )\n",
")\n"
]
}
],
"source": [
"torch.manual_seed(random_seed)\n",
"\n",
"#del model\n",
"model = GAN()\n",
"model = model.to(device)\n",
"\n",
"print(model)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\noutputs = []\\ndef hook(module, input, output):\\n outputs.append(output)\\n\\n#for i, layer in enumerate(model.discriminator):\\n# if isinstance(layer, torch.nn.modules.conv.Conv2d):\\n# model.discriminator[i].register_forward_hook(hook)\\n\\nfor i, layer in enumerate(model.generator):\\n if isinstance(layer, torch.nn.modules.ConvTranspose2d):\\n model.generator[i].register_forward_hook(hook)\\n'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### ## FOR DEBUGGING\n",
"\n",
"\"\"\"\n",
"outputs = []\n",
"def hook(module, input, output):\n",
" outputs.append(output)\n",
"\n",
"#for i, layer in enumerate(model.discriminator):\n",
"# if isinstance(layer, torch.nn.modules.conv.Conv2d):\n",
"# model.discriminator[i].register_forward_hook(hook)\n",
"\n",
"for i, layer in enumerate(model.generator):\n",
" if isinstance(layer, torch.nn.modules.ConvTranspose2d):\n",
" model.generator[i].register_forward_hook(hook)\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"optim_gener = torch.optim.Adam(model.generator.parameters(), lr=generator_learning_rate)\n",
"optim_discr = torch.optim.Adam(model.discriminator.parameters(), lr=discriminator_learning_rate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Time elapsed: 11.20 min\n",
"Total Training Time: 11.20 min\n"
]
}
],
"source": [
"start_time = time.time() \n",
"\n",
"discr_costs = []\n",
"gener_costs = []\n",
"for epoch in range(num_epochs):\n",
" model = model.train()\n",
" for batch_idx, (features, targets) in enumerate(train_loader):\n",
"\n",
" \n",
" # Normalize images to [-1, 1] range\n",
" features = (features - 0.5)*2.\n",
" features = features.view(-1, IMG_SIZE).to(device) \n",
"\n",
" targets = targets.to(device)\n",
"\n",
" valid = torch.ones(targets.size(0)).float().to(device)\n",
" fake = torch.zeros(targets.size(0)).float().to(device)\n",
" \n",
"\n",
" ### FORWARD AND BACK PROP\n",
" \n",
" \n",
" # --------------------------\n",
" # Train Generator\n",
" # --------------------------\n",
" \n",
" # Make new images\n",
" z = torch.zeros((targets.size(0), LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
" generated_features = model.generator_forward(z)\n",
" \n",
" # Loss for fooling the discriminator\n",
" discr_pred = model.discriminator_forward(generated_features.view(targets.size(0), 1, 28, 28))\n",
" \n",
" gener_loss = F.binary_cross_entropy_with_logits(discr_pred, valid*0.9)\n",
" \n",
" optim_gener.zero_grad()\n",
" gener_loss.backward()\n",
" optim_gener.step()\n",
" \n",
" # --------------------------\n",
" # Train Discriminator\n",
" # -------------------------- \n",
" \n",
" discr_pred_real = model.discriminator_forward(features.view(targets.size(0), 1, 28, 28))\n",
" real_loss = F.binary_cross_entropy_with_logits(discr_pred_real, valid*0.9)\n",
" \n",
" discr_pred_fake = model.discriminator_forward(generated_features.view(targets.size(0), 1, 28, 28).detach())\n",
" fake_loss = F.binary_cross_entropy_with_logits(discr_pred_fake, fake)\n",
" \n",
" discr_loss = 0.5*(real_loss + fake_loss)\n",
"\n",
" optim_discr.zero_grad()\n",
" discr_loss.backward()\n",
" optim_discr.step() \n",
" \n",
" discr_costs.append(discr_loss.item())\n",
" gener_costs.append(gener_loss.item())\n",
" \n",
" \n",
" ### LOGGING\n",
" if not batch_idx % 100:\n",
" print ('Epoch: %03d/%03d | Batch %03d/%03d | Gen/Dis Loss: %.4f/%.4f' \n",
" %(epoch+1, num_epochs, batch_idx, \n",
" len(train_loader), gener_loss, discr_loss))\n",
"\n",
" print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\n",
" \n",
"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\\nfor i in outputs:\\n print(i.size())\\n'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"### For Debugging\n",
"\n",
"\"\"\"\n",
"for i in outputs:\n",
" print(i.size())\n",
"\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(range(len(gener_costs)), gener_costs, label='generator loss')\n",
"plt.plot(range(len(discr_costs)), discr_costs, label='discriminator loss')\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 1440x180 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"##########################\n",
"### VISUALIZATION\n",
"##########################\n",
"\n",
"\n",
"model.eval()\n",
"# Make new images\n",
"z = torch.zeros((5, LATENT_DIM)).uniform_(-1.0, 1.0).to(device)\n",
"generated_features = model.generator_forward(z)\n",
"imgs = generated_features.view(-1, 28, 28)\n",
"\n",
"fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(20, 2.5))\n",
"\n",
"\n",
"for i, ax in enumerate(axes):\n",
" axes[i].imshow(imgs[i].to(torch.device('cpu')).detach(), cmap='binary')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------------------------------------\n",
" Layer (type) Output Shape Param #\n",
"================================================================\n",
" Linear-1 [-1, 3136] 313,600\n",
" BatchNorm1d-2 [-1, 3136] 6,272\n",
" LeakyReLU-3 [-1, 3136] 0\n",
" Reshape1-4 [-1, 64, 7, 7] 0\n",
" ConvTranspose2d-5 [-1, 32, 13, 13] 18,432\n",
" BatchNorm2d-6 [-1, 32, 13, 13] 64\n",
" LeakyReLU-7 [-1, 32, 13, 13] 0\n",
" ConvTranspose2d-8 [-1, 16, 25, 25] 4,608\n",
" BatchNorm2d-9 [-1, 16, 25, 25] 32\n",
" LeakyReLU-10 [-1, 16, 25, 25] 0\n",
" ConvTranspose2d-11 [-1, 8, 27, 27] 1,152\n",
" BatchNorm2d-12 [-1, 8, 27, 27] 16\n",
" LeakyReLU-13 [-1, 8, 27, 27] 0\n",
" ConvTranspose2d-14 [-1, 1, 28, 28] 32\n",
" Tanh-15 [-1, 1, 28, 28] 0\n",
"================================================================\n",
"Total params: 344,208\n",
"Trainable params: 344,208\n",
"Non-trainable params: 0\n",
"----------------------------------------------------------------\n",
"Input size (MB): 0.00\n",
"Forward/backward pass size (MB): 0.59\n",
"Params size (MB): 1.31\n",
"Estimated Total Size (MB): 1.91\n",
"----------------------------------------------------------------\n",
"----------------------------------------------------------------\n",
" Layer (type) Output Shape Param #\n",
"================================================================\n",
" Conv2d-1 [-1, 8, 14, 14] 72\n",
" BatchNorm2d-2 [-1, 8, 14, 14] 16\n",
" LeakyReLU-3 [-1, 8, 14, 14] 0\n",
" Conv2d-4 [-1, 32, 7, 7] 2,304\n",
" BatchNorm2d-5 [-1, 32, 7, 7] 64\n",
" LeakyReLU-6 [-1, 32, 7, 7] 0\n",
" Flatten-7 [-1, 1568] 0\n",
" Linear-8 [-1, 1] 1,569\n",
"================================================================\n",
"Total params: 4,025\n",
"Trainable params: 4,025\n",
"Non-trainable params: 0\n",
"----------------------------------------------------------------\n",
"Input size (MB): 0.00\n",
"Forward/backward pass size (MB): 0.08\n",
"Params size (MB): 0.02\n",
"Estimated Total Size (MB): 0.10\n",
"----------------------------------------------------------------\n"
]
}
],
"source": [
"from torchsummary import summary\n",
"model = model.to('cuda:0')\n",
"summary(model.generator, input_size=(100,))\n",
"summary(model.discriminator, input_size=(1, 28, 28))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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": "3.7.1"
},
"toc": {
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
在新工单中引用 查看 Git Blame 复制永久链接