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AppliedMachineAndDeepLearni.../autoencoder_mnist.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"source": [
"| Credentials | |\n",
"|----|----------------------------------|\n",
"|Host | Montanuniversitaet Leoben |\n",
"|Web | https://cps.unileoben.ac.at |\n",
"|Mail | cps@unileoben.ac.at |\n",
"|Author | Fotios Lygerakis |\n",
"|Corresponding Authors | fotios.lygerakis@unileoben.ac.at |\n",
"|Last edited | 28.09.2023 |"
],
"metadata": {
"collapsed": false
},
"id": "ae041e151c5c2222"
},
{
"cell_type": "markdown",
"source": [
"In the first part of this tutorial we will build a fully connected MLP Autoencoder on the MNIST dataset. Then we will perform linear probing on the encoder features to see how well they perform on a linear classification task."
],
"metadata": {
"collapsed": false
},
"id": "1490260facaff836"
},
{
"cell_type": "code",
"execution_count": 1,
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import matplotlib.pyplot as plt\n",
"from torchvision import datasets, transforms\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.metrics import adjusted_rand_score\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.cluster import KMeans\n",
"from tqdm import tqdm"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:27.590742473Z",
"start_time": "2023-10-03T12:40:25.356175335Z"
}
},
"id": "fc18830bb6f8d534"
},
{
"cell_type": "markdown",
"source": [
"Set random seed"
],
"metadata": {
"collapsed": false
},
"id": "1bad4bd03deb5b7e"
},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [
{
"data": {
"text/plain": "<torch._C.Generator at 0x7f10889c50d0>"
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Set random seed\n",
"torch.manual_seed(0)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:27.592356147Z",
"start_time": "2023-10-03T12:40:27.568457489Z"
}
},
"id": "27dd48e60ae7dd9e"
},
{
"cell_type": "markdown",
"source": [
"Load the MNIST dataset"
],
"metadata": {
"collapsed": false
},
"id": "cc7f167a33227e94"
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [],
"source": [
"# Define the transformations\n",
"transform = transforms.Compose([transforms.ToTensor(),\n",
" transforms.Normalize((0.5,), (0.5,))])\n",
"# Download and load the training data\n",
"trainset = datasets.MNIST('data', download=True, train=True, transform=transform)\n",
"# Download and load the test data\n",
"testset = datasets.MNIST('data', download=True, train=False, transform=transform)\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:27.639417871Z",
"start_time": "2023-10-03T12:40:27.577605311Z"
}
},
"id": "34248e8bc2678fd3"
},
{
"cell_type": "markdown",
"source": [
"Print some examples from the dataset"
],
"metadata": {
"collapsed": false
},
"id": "928dfac955d0d778"
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [
{
"data": {
"text/plain": "<Figure size 640x480 with 10 Axes>",
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Get the first 10 samples\n",
"dataiter = iter(trainset)\n",
"images, labels = [], []\n",
"\n",
"for i in range(10):\n",
" image, label = next(dataiter)\n",
" images.append(image)\n",
" labels.append(label)\n",
"\n",
"# Plot the samples\n",
"fig, axes = plt.subplots(2, 5)\n",
"\n",
"for ax, img, lbl in zip(axes.ravel(), images, labels):\n",
" ax.imshow(img.squeeze().numpy(), cmap='gray')\n",
" ax.set_title(f'Label: {lbl}')\n",
" ax.axis('off')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:28.606350630Z",
"start_time": "2023-10-03T12:40:28.277928820Z"
}
},
"id": "87c6eae807f51118"
},
{
"cell_type": "markdown",
"source": [
"Define the MLP and Convolutional Autoencoder"
],
"metadata": {
"collapsed": false
},
"id": "e4e25962ef8e5b0d"
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [],
"source": [
"class Autoencoder(nn.Module):\n",
" def __init__(self, input_size, hidden_size, type='mlp'):\n",
" super(Autoencoder, self).__init__()\n",
" # type of autoencoder\n",
" self.type = type\n",
" if self.type == 'mlp':\n",
" self.encoder = nn.Sequential(\n",
" nn.Linear(input_size, hidden_size),\n",
" nn.ReLU(True))\n",
" self.decoder = nn.Sequential(\n",
" nn.Linear(hidden_size, input_size),\n",
" nn.ReLU(True),\n",
" nn.Sigmoid()\n",
" )\n",
" elif self.type == 'cnn':\n",
" # Encoder module\n",
" self.encoder = nn.Sequential(\n",
" nn.Conv2d(in_channels=input_size, out_channels=hidden_size//2, kernel_size=3, stride=2, padding=1),\n",
" nn.ReLU(),\n",
" nn.Conv2d(in_channels=hidden_size//2, out_channels=hidden_size, kernel_size=3, stride=2, padding=1),\n",
" nn.ReLU()\n",
" )\n",
" # Decoder module\n",
" self.decoder = nn.Sequential(\n",
" nn.ConvTranspose2d(in_channels=hidden_size, out_channels=hidden_size//2, kernel_size=3, stride=2, padding=1, output_padding=1),\n",
" nn.ReLU(),\n",
" nn.ConvTranspose2d(in_channels=hidden_size//2, out_channels=1, kernel_size=3, stride=2, padding=1, output_padding=1),\n",
" nn.Sigmoid() # Sigmoid to ensure the output is between 0 and 1\n",
" )\n",
"\n",
" def forward(self, x):\n",
" x = self.encoder(x)\n",
" x = self.decoder(x)\n",
" return x"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:29.561602021Z",
"start_time": "2023-10-03T12:40:29.559204154Z"
}
},
"id": "26f2513d92b78e1e"
},
{
"cell_type": "markdown",
"source": [
"Check if GPU support is available"
],
"metadata": {
"collapsed": false
},
"id": "91a01313b4d95274"
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cuda\n"
]
}
],
"source": [
"# device\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"print(device)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:30.834696308Z",
"start_time": "2023-10-03T12:40:30.827970016Z"
}
},
"id": "67006b35b75d8dff"
},
{
"cell_type": "markdown",
"source": [
"Define the training function"
],
"metadata": {
"collapsed": false
},
"id": "8eebf70cb27640d5"
},
{
"cell_type": "code",
"execution_count": 7,
"outputs": [],
"source": [
"# Define the training function\n",
"def train(model, train_loader, optimizer, criterion, epoch, verbose=True):\n",
" model.train()\n",
" train_loss = 0\n",
" for i, (data, _) in enumerate(train_loader):\n",
" # check the type of autoencoder and modify the input data accordingly\n",
" if model.type == 'mlp':\n",
" data = data.view(data.size(0), -1)\n",
" data = data.to(device)\n",
" optimizer.zero_grad()\n",
" output = model(data)\n",
" loss = criterion(output, data)\n",
" loss.backward()\n",
" train_loss += loss.item()\n",
" optimizer.step()\n",
" train_loss /= len(train_loader.dataset)\n",
" if verbose:\n",
" print(f'{model.type}====> Epoch: {epoch} Average loss: {train_loss:.4f}') \n",
" return train_loss"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:31.675127463Z",
"start_time": "2023-10-03T12:40:31.655370005Z"
}
},
"id": "5f96f7be13984747"
},
{
"cell_type": "markdown",
"source": [
"The evaluation functions for the linear classification and clustering tasks"
],
"metadata": {
"collapsed": false
},
"id": "5f6386edcab6b1e4"
},
{
"cell_type": "code",
"execution_count": 8,
"outputs": [],
"source": [
"# Extract encoded representations for a given loader\n",
"def extract_features(loader, model):\n",
" features = []\n",
" labels = []\n",
" model.eval()\n",
" with torch.no_grad():\n",
" for data in loader:\n",
" img, label = data\n",
" if model.type == 'mlp':\n",
" img = img.view(img.size(0), -1)\n",
" img = img.to(device)\n",
" feature = model.encoder(img)\n",
" if model.type == 'cnn':\n",
" feature = feature.view(feature.size(0), -1) # Flatten the CNN encoded features\n",
" features.append(feature)\n",
" labels.append(label)\n",
" return torch.cat(features), torch.cat(labels)\n",
"\n",
"# Define the loss test function\n",
"def test_loss(model, test_loader, criterion):\n",
" model.eval()\n",
" eval_loss = 0\n",
" with torch.no_grad():\n",
" for i, (data, _) in enumerate(test_loader):\n",
" # check the type of autoencoder and modify the input data accordingly\n",
" if model.type == 'mlp':\n",
" data = data.view(data.size(0), -1)\n",
" data = data.to(device)\n",
" output = model(data)\n",
" eval_loss += criterion(output, data).item()\n",
" eval_loss /= len(test_loader.dataset)\n",
" print('====> Test set loss: {:.4f}'.format(eval_loss))\n",
" return eval_loss\n",
"\n",
"# Define the linear classification test function\n",
"def test_linear(encoded_train, train_labels, encoded_test, test_labels):\n",
" train_features_np = encoded_train.cpu().numpy()\n",
" train_labels_np = train_labels.cpu().numpy()\n",
" test_features_np = encoded_test.cpu().numpy()\n",
" test_labels_np = test_labels.cpu().numpy()\n",
" \n",
" # Apply logistic regression on train features and labels\n",
" logistic_regression = LogisticRegression(random_state=0, max_iter=100).fit(train_features_np, train_labels_np)\n",
" print(f\"Train accuracy: {logistic_regression.score(train_features_np, train_labels_np)}\")\n",
" # Apply logistic regression on test features and labels\n",
" test_accuracy = logistic_regression.score(test_features_np, test_labels_np)\n",
" print(f\"Test accuracy: {test_accuracy}\")\n",
" return test_accuracy\n",
"\n",
"\n",
"def test_clustering(encoded_features, true_labels):\n",
" encoded_features_np = encoded_features.cpu().numpy()\n",
" true_labels_np = true_labels.cpu().numpy()\n",
" \n",
" # Apply k-means clustering\n",
" kmeans = KMeans(n_clusters=10, n_init=10, random_state=0).fit(encoded_features_np)\n",
" cluster_labels = kmeans.labels_\n",
" \n",
" # Evaluate clustering results using Adjusted Rand Index\n",
" ari_score = adjusted_rand_score(true_labels_np, cluster_labels)\n",
" print(f\"Clustering ARI score: {ari_score}\")\n",
" return ari_score\n",
"\n",
"def knn_classifier(encoded_train, train_labels, encoded_test, test_labels, k=5):\n",
" encoded_train_np = encoded_train.cpu().numpy()\n",
" encoded_test_np = encoded_test.cpu().numpy()\n",
" train_labels_np = train_labels.cpu().numpy()\n",
" test_labels_np = test_labels.cpu().numpy()\n",
" \n",
" # Apply k-nearest neighbors classification\n",
" knn = KNeighborsClassifier(n_neighbors=k).fit(encoded_train_np, train_labels_np)\n",
" accuracy_score = knn.score(encoded_test_np, test_labels_np)\n",
" print(f\"KNN accuracy: {accuracy_score}\")\n",
" return accuracy_score\n",
"\n",
"def test(model, train_loader, test_loader, criterion):\n",
" # Extract features once for all tests\n",
" encoded_train, train_labels = extract_features(train_loader, model)\n",
" encoded_test, test_labels = extract_features(test_loader, model)\n",
" print(f\"{model.type} Autoencoder\")\n",
" results = {\n",
" 'reconstruction_loss': test_loss(model, test_loader, criterion),\n",
" 'linear_classification_accuracy': test_linear(encoded_train, train_labels, encoded_test, test_labels),\n",
" 'knn_classification_accuracy': knn_classifier(encoded_train, train_labels, encoded_test, test_labels),\n",
" 'clustering_ari_score': test_clustering(encoded_test, test_labels)\n",
" }\n",
" \n",
" # Save results to a log file\n",
" with open(\"evaluation_results.log\", \"w\") as log_file:\n",
" for key, value in results.items():\n",
" log_file.write(f\"{key}: {value}\")\n",
" \n",
" return results\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:32.662180572Z",
"start_time": "2023-10-03T12:40:32.657785583Z"
}
},
"id": "b2c4483492fdd427"
},
{
"cell_type": "code",
"execution_count": 9,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MLP AE parameters: 201616\n",
"CNN AE parameters: 148865\n"
]
}
],
"source": [
"# Define the training parameters for the fully connected MLP Autoencoder\n",
"batch_size = 32\n",
"epochs = 5\n",
"hidden_size = 128\n",
"train_frequency = epochs\n",
"test_frequency = epochs\n",
"\n",
"# Create the fully connected MLP Autoencoder\n",
"input_size = trainset.data.shape[1] * trainset.data.shape[2]\n",
"ae = Autoencoder(input_size, hidden_size, type='mlp').to(device)\n",
"input_size=1\n",
"cnn_ae = Autoencoder(input_size, hidden_size, type='cnn').to(device)\n",
"# print the models' number of parameters\n",
"print(f\"MLP AE parameters: {sum(p.numel() for p in ae.parameters())}\")\n",
"print(f\"CNN AE parameters: {sum(p.numel() for p in cnn_ae.parameters())}\")\n",
"\n",
"# Define the loss function and optimizer\n",
"criterion = nn.MSELoss()\n",
"optimizer = optim.Adam(ae.parameters(), lr=1e-3)\n",
"optimizer_cnn = optim.Adam(cnn_ae.parameters(), lr=1e-3)\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:33.593858793Z",
"start_time": "2023-10-03T12:40:33.153759806Z"
}
},
"id": "bcb22bc5af9fb014"
},
{
"cell_type": "code",
"execution_count": 10,
"outputs": [],
"source": [
"# Create the train and test dataloaders\n",
"train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:40:34.157176359Z",
"start_time": "2023-10-03T12:40:34.153720678Z"
}
},
"id": "f1626ce45bb25883"
},
{
"cell_type": "code",
"execution_count": 11,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 80%|████████ | 4/5 [01:02<00:15, 15.79s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mlp====> Epoch: 5 Average loss: 0.0598\n",
"cnn====> Epoch: 5 Average loss: 0.0260\n",
"mlp Autoencoder\n",
"====> Test set loss: 0.0598\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/fotis/PycharmProjects/representation_learning_tutorial/venv/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train accuracy: 0.25626666666666664\n",
"Test accuracy: 0.2649\n",
"KNN accuracy: 0.2295\n",
"Clustering ARI score: 0.0614873771495409\n",
"cnn Autoencoder\n",
"====> Test set loss: 0.0260\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/fotis/PycharmProjects/representation_learning_tutorial/venv/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
"\n",
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
"Please also refer to the documentation for alternative solver options:\n",
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
" n_iter_i = _check_optimize_result(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train accuracy: 0.9316166666666666\n",
"Test accuracy: 0.9278\n",
"KNN accuracy: 0.9639\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5/5 [06:56<00:00, 83.22s/it] "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Clustering ARI score: 0.3909294873624941\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"test_mlp = []\n",
"test_cnn = []\n",
"# Train the model\n",
"for epoch in tqdm(range(1, epochs + 1)):\n",
" verbose = True if epoch % train_frequency == 0 else False\n",
" train(ae, train_loader, optimizer, criterion, epoch, verbose)\n",
" train(cnn_ae, train_loader, optimizer_cnn, criterion, epoch, verbose)\n",
"\n",
" # test every n epochs\n",
" if epoch % test_frequency == 0:\n",
" restults_dic = test(ae, train_loader, test_loader, criterion)\n",
" test_mlp.append([restults_dic['reconstruction_loss'], restults_dic['linear_classification_accuracy'], restults_dic['knn_classification_accuracy'], restults_dic['clustering_ari_score']])\n",
" restults_dic = test(cnn_ae, train_loader, test_loader, criterion)\n",
" test_cnn.append([restults_dic['reconstruction_loss'], restults_dic['linear_classification_accuracy'], restults_dic['knn_classification_accuracy'], restults_dic['clustering_ari_score']])"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:47:30.814501313Z",
"start_time": "2023-10-03T12:40:34.720164326Z"
}
},
"id": "7472159fdc5f2532"
},
{
"cell_type": "markdown",
"source": [
"Compare the evaluation results of the MLP and CNN Autoencoders"
],
"metadata": {
"collapsed": false
},
"id": "10639256e342a159"
},
{
"cell_type": "code",
"execution_count": 12,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model Reconstruction Loss Linear Accuracy KNN Accuracy Clustering ARI \n",
"MLP AE 0.0598 0.2649 0.2295 0.0615 \n",
"CNN AE 0.0260 0.9278 0.9639 0.3909 \n"
]
}
],
"source": [
"print(f\"{'Model':<10} {'Reconstruction Loss':<20} {'Linear Accuracy':<20} {'KNN Accuracy':<20} {'Clustering ARI':<20}\")\n",
"print(f\"{'MLP AE':<10} {test_mlp[-1][0]:<20.4f} {test_mlp[-1][1]:<20.4f} {test_mlp[-1][2]:<20.4f} {test_mlp[-1][3]:<20.4f}\")\n",
"print(f\"{'CNN AE':<10} {test_cnn[-1][0]:<20.4f} {test_cnn[-1][1]:<20.4f} {test_cnn[-1][2]:<20.4f} {test_cnn[-1][3]:<20.4f}\")"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:47:30.828062767Z",
"start_time": "2023-10-03T12:47:30.812448850Z"
}
},
"id": "50bb4c3c58af09ee"
},
{
"cell_type": "markdown",
"source": [
"Develop a linear classifier with fully connected layers"
],
"metadata": {
"collapsed": false
},
"id": "b9201d1403781706"
},
{
"cell_type": "code",
"execution_count": 13,
"outputs": [],
"source": [
"# Define the fully connected classifier for MNIST\n",
"class DenseClassifier(nn.Module):\n",
" def __init__(self, input_size=784, hidden_size=500, num_classes=10):\n",
" super(DenseClassifier, self).__init__()\n",
" self.type = 'mlp'\n",
" self.encoder = nn.Sequential(\n",
" nn.Linear(input_size, hidden_size),\n",
" nn.ReLU(True))\n",
" self.fc1 = nn.Linear(hidden_size, num_classes)\n",
"\n",
" def forward(self, x):\n",
" x = x.view(x.size(0), -1) # Flatten the input tensor\n",
" x = self.encoder(x)\n",
" x = self.fc1(x)\n",
" return x\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:47:30.833296890Z",
"start_time": "2023-10-03T12:47:30.819270525Z"
}
},
"id": "1612800950703181"
},
{
"cell_type": "markdown",
"source": [
"Develop a non-linear classifier with convolutional layers"
],
"metadata": {
"collapsed": false
},
"id": "35db4190e9c7f716"
},
{
"cell_type": "code",
"execution_count": 14,
"outputs": [],
"source": [
"# cnn classifier\n",
"class CNNClassifier(nn.Module):\n",
" def __init__(self, input_size=3, hidden_size=128, num_classes=10):\n",
" super(CNNClassifier, self).__init__()\n",
" self.type = 'cnn'\n",
" # Encoder (Feature extractor)\n",
" self.encoder = nn.Sequential(\n",
" nn.Conv2d(in_channels=input_size, out_channels=hidden_size//2, kernel_size=3, stride=2, padding=1),\n",
" nn.ReLU(),\n",
" nn.Conv2d(in_channels=hidden_size//2, out_channels=hidden_size, kernel_size=3, stride=2, padding=1),\n",
" nn.ReLU()\n",
" )\n",
" \n",
" # Classifier\n",
" # Here, for the sake of example, I'm assuming the spatial size of the encoder output \n",
" # is 7x7 for an input size of 28x28. You might want to adjust this if the spatial dimensions change.\n",
" self.classifier = nn.Sequential(\n",
" nn.Flatten(),\n",
" nn.Linear(hidden_size*7*7, hidden_size),\n",
" nn.ReLU(),\n",
" nn.Linear(hidden_size, num_classes),\n",
" nn.LogSoftmax(dim=1) # LogSoftmax is typically used with NLLLoss\n",
" )\n",
"\n",
" def forward(self, x):\n",
" x = self.encoder(x)\n",
" x = self.classifier(x)\n",
" return x\n",
" return x"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:47:30.916320521Z",
"start_time": "2023-10-03T12:47:30.828281294Z"
}
},
"id": "cb2dfcf75113fd0b"
},
{
"cell_type": "markdown",
"source": [
"Train and test functions for the non-linear classifier"
],
"metadata": {
"collapsed": false
},
"id": "7c3cf2371479da0c"
},
{
"cell_type": "code",
"execution_count": 15,
"outputs": [],
"source": [
"# Train for the classifier\n",
"def train_classifier(model, train_loader, optimizer, criterion, epoch, verbose=True):\n",
" model.train()\n",
" train_loss = 0\n",
" correct = 0 \n",
" for i, (data, target) in enumerate(train_loader):\n",
" if model.type == 'cnn':\n",
" data = data.to(device)\n",
" else:\n",
" data = data.view(data.size(0), -1)\n",
" data = data.to(device)\n",
" target = target.to(device)\n",
" optimizer.zero_grad()\n",
" output = model(data)\n",
" loss = criterion(output, target)\n",
" loss.backward()\n",
" train_loss += loss.item()\n",
" optimizer.step()\n",
" # Calculate correct predictions for training accuracy\n",
" pred = output.argmax(dim=1, keepdim=True)\n",
" correct += pred.eq(target.view_as(pred)).sum().item()\n",
"\n",
" train_loss /= len(train_loader.dataset)\n",
" train_accuracy = 100. * correct / len(train_loader.dataset)\n",
" if verbose:\n",
" print(f'{model.type}====> Epoch: {epoch} Average loss: {train_loss:.4f}')\n",
" print(f'{model.type}====> Epoch: {epoch} Training accuracy: {train_accuracy:.2f}%')\n",
" return train_loss\n",
"\n",
"\n",
"def test_classifier(model, test_loader, criterion):\n",
" model.eval()\n",
" eval_loss = 0\n",
" correct = 0\n",
" with torch.no_grad():\n",
" for i, (data, target) in enumerate(test_loader):\n",
" if model.type == 'cnn':\n",
" data = data.to(device)\n",
" else:\n",
" data = data.view(data.size(0), -1)\n",
" data = data.to(device)\n",
" target = target.to(device)\n",
" output = model(data)\n",
" eval_loss += criterion(output, target).item()\n",
" pred = output.argmax(dim=1, keepdim=True)\n",
" correct += pred.eq(target.view_as(pred)).sum().item()\n",
" eval_loss /= len(test_loader.dataset)\n",
" print('====> Test set loss: {:.4f}'.format(eval_loss))\n",
" accuracy = correct / len(test_loader.dataset)\n",
" print('====> Test set accuracy: {:.4f}'.format(accuracy))\n",
" return accuracy\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:47:30.924063759Z",
"start_time": "2023-10-03T12:47:30.875486950Z"
}
},
"id": "ac980d25bd8a3dd3"
},
{
"cell_type": "code",
"execution_count": 16,
"outputs": [],
"source": [
"# Define the training parameters for the fully connected classifier\n",
"batch_size = 32\n",
"epochs = 5\n",
"learning_rate = 1e-3\n",
"hidden_size = 128\n",
"num_classes = 10\n",
"train_frequency = epochs\n",
"test_frequency = epochs\n",
"# Create the fully connected classifier\n",
"input_size = trainset.data.shape[1] * trainset.data.shape[2]\n",
"classifier = DenseClassifier(input_size, hidden_size, num_classes).to(device)\n",
"input_size = 1\n",
"cnn_classifier = CNNClassifier(input_size, hidden_size, num_classes).to(device)"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:47:30.924544974Z",
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}
},
"id": "dff05e622dcfd774"
},
{
"cell_type": "code",
"execution_count": 17,
"outputs": [],
"source": [
"# Define the loss function and optimizer\n",
"criterion = nn.CrossEntropyLoss()\n",
"optimizer = optim.Adam(classifier.parameters(), lr=learning_rate)\n",
"optimizer_cnn = optim.Adam(cnn_classifier.parameters(), lr=learning_rate)\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:47:30.924773429Z",
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},
"id": "3104345cdee0eb00"
},
{
"cell_type": "markdown",
"source": [
"Train the non-linear classifiers"
],
"metadata": {
"collapsed": false
},
"id": "e1fed39be2f04745"
},
{
"cell_type": "code",
"execution_count": 18,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 80%|████████ | 4/5 [02:10<00:32, 32.47s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mlp====> Epoch: 5 Average loss: 0.0030\n",
"mlp====> Epoch: 5 Training accuracy: 97.08%\n",
"cnn====> Epoch: 5 Average loss: 0.0005\n",
"cnn====> Epoch: 5 Training accuracy: 99.49%\n",
"====> Test set loss: 0.0035\n",
"====> Test set accuracy: 0.9686\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5/5 [02:48<00:00, 33.62s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"====> Test set loss: 0.0013\n",
"====> Test set accuracy: 0.9880\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Train the classifier\n",
"for epoch in tqdm(range(1, epochs + 1)):\n",
" verbose = True if epoch % train_frequency == 0 else False\n",
" train_classifier(classifier, train_loader, optimizer, criterion, epoch, verbose)\n",
" train_classifier(cnn_classifier, train_loader, optimizer_cnn, criterion, epoch, verbose)\n",
"\n",
" # test every n epochs\n",
" if epoch % test_frequency == 0:\n",
" test_acc = test_classifier(classifier, test_loader, criterion)\n",
" test_acc_cnn = test_classifier(cnn_classifier, test_loader, criterion)\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:50:19.005163249Z",
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},
"id": "abc0c6ce338d40d9"
},
{
"cell_type": "markdown",
"source": [
"Load the encoder weights into the classifier"
],
"metadata": {
"collapsed": false
},
"id": "a06038f113d8434f"
},
{
"cell_type": "code",
"execution_count": 19,
"outputs": [
{
"data": {
"text/plain": "<All keys matched successfully>"
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# initialize the classifier with the encoder weights\n",
"classifier.encoder.load_state_dict(ae.encoder.state_dict())\n",
"cnn_classifier.encoder.load_state_dict(cnn_ae.encoder.state_dict())"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:50:19.005816667Z",
"start_time": "2023-10-03T12:50:18.994175691Z"
}
},
"id": "6a91d8894b70ef7c"
},
{
"cell_type": "markdown",
"source": [
"Transfer learning"
],
"metadata": {
"collapsed": false
},
"id": "aafa4a9ba7208647"
},
{
"cell_type": "code",
"execution_count": 20,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 80%|████████ | 4/5 [01:56<00:27, 27.00s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"mlp====> Epoch: 5 Average loss: 0.0547\n",
"mlp====> Epoch: 5 Training accuracy: 38.00%\n",
"cnn====> Epoch: 5 Average loss: 0.0004\n",
"cnn====> Epoch: 5 Training accuracy: 99.57%\n",
"====> Test set loss: 0.0526\n",
"====> Test set accuracy: 0.4150\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 5/5 [02:25<00:00, 29.01s/it]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"====> Test set loss: 0.0017\n",
"====> Test set accuracy: 0.9868\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# fine-tune the classifier\n",
"learning_rate = 1e-5\n",
"epochs = 5\n",
"train_frequency = epochs\n",
"test_frequency = epochs\n",
"optimizer_pretrained = optim.Adam(classifier.parameters(), lr=learning_rate)\n",
"optimizer_pretrained_cnn = optim.Adam(cnn_classifier.parameters(), lr=learning_rate)\n",
"for epoch in tqdm(range(1, epochs + 1)):\n",
" verbose = True if epoch % train_frequency == 0 else False\n",
" train_loss = train_classifier(classifier, train_loader, optimizer_pretrained, criterion, epoch, verbose)\n",
" train_loss_cnn = train_classifier(cnn_classifier, train_loader, optimizer_cnn, criterion, epoch, verbose)\n",
"\n",
" # test every n epochs\n",
" if epoch % test_frequency == 0:\n",
" test_acc_pretrained = test_classifier(classifier, test_loader, criterion)\n",
" test_acc_pretrained_cnn = test_classifier(cnn_classifier, test_loader, criterion)\n"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:52:44.085979647Z",
"start_time": "2023-10-03T12:50:19.003728990Z"
}
},
"id": "a60dd68f988a8249"
},
{
"cell_type": "markdown",
"source": [
"Compare the results of the linear probing with the results of the linear classifier"
],
"metadata": {
"collapsed": false
},
"id": "31577275b833707a"
},
{
"cell_type": "code",
"execution_count": 21,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model Linear Accuracy Non-linear accuracy Pretrained accuracy \n",
"MLP AE 0.2649 0.9686 0.4150 \n",
"CNN AE 0.9278 0.9880 0.9868 \n"
]
}
],
"source": [
"# print a table of the accuracies. compare the results with the results of the linear probing\n",
"print(f\"{'Model':<10} {'Linear Accuracy':<20} {'Non-linear accuracy':<20} {'Pretrained accuracy':<20}\")\n",
"print(f\"{'MLP AE':<10} {test_mlp[-1][1]:<20.4f} {test_acc:<20.4f} {test_acc_pretrained:<20.4f}\")\n",
"print(f\"{'CNN AE':<10} {test_cnn[-1][1]:<20.4f} {test_acc_cnn:<20.4f} {test_acc_pretrained_cnn:<20.4f}\")"
],
"metadata": {
"collapsed": false,
"ExecuteTime": {
"end_time": "2023-10-03T12:52:44.091206610Z",
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}
},
"id": "40d0e7f3f13404c9"
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{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
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},
"id": "f38a1ab6951a694e"
}
],
"metadata": {
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Reference in New Issue Copy Permalink