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All lab programs in the 7th sem course "Deep Learning Laboratory"
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{
"cells": [
{
"cell_type": "markdown",
"id": "2f523d88-f5ec-42b4-a3b0-b8fe36f86aa7",
"metadata": {},
"source": [
"# Exp1: Training XOR using multilayer perceptrons"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "297ad642-54be-4b2c-bde0-3eb9fbf1dc17",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - accuracy: 1.0000 - loss: 2.7442e-04\n",
"Accuracy: 100.00%\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
"\n",
"Predictions:\n",
"Input: [0 0]\n",
"\tPredicted Output: 0\n",
"\tTrue Output: 0\n",
"Input: [0 1]\n",
"\tPredicted Output: 1\n",
"\tTrue Output: 1\n",
"Input: [1 0]\n",
"\tPredicted Output: 1\n",
"\tTrue Output: 1\n",
"Input: [1 1]\n",
"\tPredicted Output: 0\n",
"\tTrue Output: 0\n"
]
}
],
"source": [
"import numpy as np\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Dense, Input\n",
"from tensorflow.keras.optimizers import Adam\n",
"\n",
"X = np.array([[0,0],[0,1],[1,0],[1,1]])\n",
"Y = np.array([0,1,1,0])\n",
"\n",
"model = Sequential()\n",
"model.add(Input(shape=(2,)))\n",
"model.add(Dense(4,activation='relu'))\n",
"model.add(Dense(1,activation='sigmoid'))\n",
"\n",
"model.compile(\n",
" loss='binary_crossentropy',\n",
" optimizer=Adam(learning_rate = 0.05),\n",
" metrics = ['accuracy']\n",
")\n",
"\n",
"model.fit(X,Y,epochs=1000,verbose=0)\n",
"loss,accuracy = model.evaluate(X,Y)\n",
"print(f\"Accuracy: {accuracy*100:.2f}%\")\n",
"\n",
"predictions = model.predict(X)\n",
"predictions = (predictions>0.5).astype(int)\n",
"\n",
"print(\"\\nPredictions:\")\n",
"for i, prediction in enumerate(predictions):\n",
" print(f\"Input: {X[i]}\")\n",
" print(f\"\\tPredicted Output: {prediction[0]}\")\n",
" print(f\"\\tTrue Output: {Y[i]}\")"
]
},
{
"cell_type": "markdown",
"id": "9c8f262d-add6-4a39-9df9-9f35d9eee198",
"metadata": {},
"source": [
"# Exp2: Implement regularization techniques in deep learning models using parameter norm penalties, dataset augmentation"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1e8b9559-c14d-4006-a237-636880892dfe",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/50\n",
"375/375 - 7s - 19ms/step - accuracy: 0.6784 - loss: 1.1040 - val_accuracy: 0.8834 - val_loss: 0.5266\n",
"Epoch 2/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.8407 - loss: 0.6359 - val_accuracy: 0.9213 - val_loss: 0.3911\n",
"Epoch 3/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.8718 - loss: 0.5382 - val_accuracy: 0.9286 - val_loss: 0.3611\n",
"Epoch 4/50\n",
"375/375 - 7s - 18ms/step - accuracy: 0.8854 - loss: 0.4947 - val_accuracy: 0.9423 - val_loss: 0.3192\n",
"Epoch 5/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.8937 - loss: 0.4726 - val_accuracy: 0.9445 - val_loss: 0.3162\n",
"Epoch 6/50\n",
"375/375 - 7s - 17ms/step - accuracy: 0.9020 - loss: 0.4468 - val_accuracy: 0.9516 - val_loss: 0.2947\n",
"Epoch 7/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9058 - loss: 0.4315 - val_accuracy: 0.9513 - val_loss: 0.2903\n",
"Epoch 8/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9093 - loss: 0.4233 - val_accuracy: 0.9577 - val_loss: 0.2717\n",
"Epoch 9/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9138 - loss: 0.4088 - val_accuracy: 0.9570 - val_loss: 0.2663\n",
"Epoch 10/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9144 - loss: 0.4057 - val_accuracy: 0.9579 - val_loss: 0.2664\n",
"Epoch 11/50\n",
"375/375 - 7s - 17ms/step - accuracy: 0.9159 - loss: 0.4024 - val_accuracy: 0.9587 - val_loss: 0.2627\n",
"Epoch 12/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9184 - loss: 0.3911 - val_accuracy: 0.9585 - val_loss: 0.2621\n",
"Epoch 13/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9191 - loss: 0.3907 - val_accuracy: 0.9607 - val_loss: 0.2554\n",
"Epoch 14/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9212 - loss: 0.3865 - val_accuracy: 0.9597 - val_loss: 0.2638\n",
"Epoch 15/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9235 - loss: 0.3764 - val_accuracy: 0.9597 - val_loss: 0.2531\n",
"Epoch 16/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9222 - loss: 0.3815 - val_accuracy: 0.9642 - val_loss: 0.2519\n",
"Epoch 17/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9244 - loss: 0.3726 - val_accuracy: 0.9646 - val_loss: 0.2439\n",
"Epoch 18/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9222 - loss: 0.3727 - val_accuracy: 0.9619 - val_loss: 0.2542\n",
"Epoch 19/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9250 - loss: 0.3725 - val_accuracy: 0.9624 - val_loss: 0.2510\n",
"Epoch 20/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9258 - loss: 0.3638 - val_accuracy: 0.9648 - val_loss: 0.2407\n",
"Epoch 21/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9256 - loss: 0.3631 - val_accuracy: 0.9614 - val_loss: 0.2482\n",
"Epoch 22/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9275 - loss: 0.3643 - val_accuracy: 0.9653 - val_loss: 0.2428\n",
"Epoch 23/50\n",
"375/375 - 6s - 17ms/step - accuracy: 0.9259 - loss: 0.3640 - val_accuracy: 0.9636 - val_loss: 0.2484\n",
"Epoch 23: early stopping\n",
"Restoring model weights from the end of the best epoch: 20.\n",
"<keras.src.callbacks.history.History object at 0x7eeb76e44dd0>\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers, regularizers\n",
"import numpy as np\n",
"\n",
"# Verbosity Flags\n",
"CALLBACK_VERBOSITY = 1 # 0 or 1\n",
"TRAINING_VERBOSITY = 2 # 0, 1 or 2\n",
"\n",
"# Load MNIST dataset\n",
"(x_train,y_train),_ = keras.datasets.mnist.load_data()\n",
"\n",
"# Normalize and Reshape\n",
"x_train = x_train.astype('float32')/255\n",
"x_train = np.expand_dims(x_train, -1) # (60000,28,28,1)\n",
"y_train = tf.keras.utils.to_categorical(y_train,10)\n",
"\n",
"# Add Gaussian noise\n",
"noise = 0.05 * np.random.normal(size=x_train.shape)\n",
"x_train = np.clip(x_train + noise, 0., 1.)\n",
"\n",
"# ata augmentation with validation split\n",
"datagen = keras.preprocessing.image.ImageDataGenerator(\n",
" rotation_range = 10,\n",
" width_shift_range = 0.1,\n",
" height_shift_range = 0.1,\n",
" validation_split = 0.2 # which translates to 20%\n",
")\n",
"datagen.fit(x_train)\n",
"\n",
"# Model\n",
"model = keras.Sequential([\n",
" layers.Flatten(input_shape = (28,28,1)),\n",
" layers.Dense(256,\n",
" activation = 'relu',\n",
" kernel_regularizer = regularizers.l1_l2(l1 = 1e-5, l2 = 1e-4)\n",
" ),\n",
" layers.Dropout(0.5),\n",
" layers.Dense(128,\n",
" activation = 'relu',\n",
" kernel_regularizer = regularizers.l2(1e-4)\n",
" ),\n",
" layers.Dropout(0.3),\n",
" layers.Dense(10,activation = 'softmax')\n",
"])\n",
"model.compile(optimizer = 'adam', loss = 'categorical_crossentropy',metrics = ['accuracy'])\n",
"\n",
"# Early stopping\n",
"early_stop = keras.callbacks.EarlyStopping(\n",
" monitor = 'val_loss',\n",
" patience = 3,\n",
" restore_best_weights = True,\n",
" verbose = CALLBACK_VERBOSITY\n",
")\n",
"\n",
"# Train\n",
"history = model.fit(\n",
" datagen.flow(x_train,y_train,batch_size=128,subset='training'),\n",
" validation_data = datagen.flow(x_train,y_train,batch_size=128,subset='validation'),\n",
" epochs = 50,\n",
" callbacks = [early_stop],\n",
" verbose = TRAINING_VERBOSITY\n",
")\n",
"print(history)"
]
},
{
"cell_type": "markdown",
"id": "2b078143-d5b8-41f9-bb6d-598ef707e62f",
"metadata": {},
"source": [
"# Exp3: Implement and compare different optimizer algorithms (SGD, Momentum, Adam) for training a simple neural network on a toy dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f4c212df-0038-4c3c-900d-0cf8e1623a83",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final Loss(SGD): 0.24993009865283966\n",
"Final Loss(Momentum): 0.0008142305887304246\n",
"Final Loss(Adam): 2.1369228306866717e-06\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Toy dataset: XOR problem\n",
"x = torch.tensor([[0,0],[0,1],[1,0],[1,1]], dtype=torch.float32)\n",
"y = torch.tensor([[0],[1],[1],[0]], dtype=torch.float32)\n",
"\n",
"# Define a simple neural network\n",
"class SimpleNN(nn.Module):\n",
" def __init__(self):\n",
" super(SimpleNN, self).__init__()\n",
" self.fc1 = nn.Linear(2,4)\n",
" self.fc2 = nn.Linear(4,1)\n",
"\n",
" def forward(self, x):\n",
" x = torch.sigmoid(self.fc1(x)) # Hidden layer activation\n",
" x = torch.sigmoid(self.fc2(x)) # Output layer activation\n",
" return x\n",
"\n",
"# To train the model\n",
"def train_model(optimizer_name, model, x, y, epochs=5000, learning_rate=0.1):\n",
" criterion= nn.MSELoss()\n",
" # Select Optimizer based on the input parameter\n",
" if optimizer_name == \"SGD\":\n",
" optimizer = optim.SGD(model.parameters(), lr=learning_rate)\n",
" elif optimizer_name == \"Momentum\":\n",
" optimizer = optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)\n",
" elif optimizer_name == \"Adam\":\n",
" optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
"\n",
" losses = []\n",
" for epoch in range(epochs):\n",
" optimizer.zero_grad()\n",
" output = model(x)\n",
" loss = criterion(output,y)\n",
" loss.backward()\n",
" optimizer.step()\n",
" losses.append(loss.item())\n",
" return losses\n",
"\n",
"# Initialize models for different optimizers\n",
"models = {\n",
" \"SGD\":SimpleNN(),\n",
" \"Momentum\":SimpleNN(),\n",
" \"Adam\":SimpleNN(),\n",
"}\n",
"\n",
"# Train models with different optimizers\n",
"losses_sgd = train_model(\"SGD\", models[\"SGD\"], x, y)\n",
"losses_momentum = train_model(\"Momentum\", models[\"Momentum\"], x, y)\n",
"losses_adam = train_model(\"Adam\", models[\"Adam\"], x, y)\n",
"\n",
"to_display = [\n",
" (\"SGD\", losses_sgd),\n",
" (\"Momentum\", losses_momentum),\n",
" (\"Adam\", losses_adam),\n",
"]\n",
"\n",
"# Plot the loss curves\n",
"for name, losses in to_display:\n",
" plt.plot(losses, label=name)\n",
"plt.xlabel(\"Epoch\")\n",
"plt.ylabel(\"Loss\")\n",
"plt.legend()\n",
"plt.title(\"Optimizer Algorithm Comparison\")\n",
"plt.show()\n",
"\n",
"# Final results\n",
"for name, losses in to_display:\n",
" print(f\"Final Loss({name}): {losses[-1]}\")"
]
},
{
"cell_type": "markdown",
"id": "e6767751-d4f3-442f-90a1-71e16b8adfbe",
"metadata": {},
"source": [
"# Exp3*: Same thing, but in tensorflow now"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "788f6b33-9afe-4549-8760-da6e6a8fb313",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sahyadri/anaconda3/lib/python3.11/site-packages/keras/src/layers/core/dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final Loss(SGD): 0.12202060967683792\n",
"Final Loss(Momentum): 0.0005314293666742742\n",
"Final Loss(Adam): 2.0855791262874845e-06\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers, optimizers, losses\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Toy dataset: XOR problem\n",
"x = np.array([[0,0],[0,1],[1,0],[1,1]], dtype=np.float32)\n",
"y = np.array([[0],[1],[1],[0]], dtype=np.float32)\n",
"\n",
"# Create the simple NN model\n",
"def create_model():\n",
" model = keras.Sequential([\n",
" layers.Dense(4,input_dim=2, activation='sigmoid'),\n",
" layers.Dense(1, activation='sigmoid'),\n",
" \n",
" ])\n",
" return model\n",
"\n",
"\n",
"# To train the model\n",
"def train_model(optimizer_name, x, y, epochs=5000, lr=0.1):\n",
" model = create_model()\n",
" # Select Optimizer based on the input parameter\n",
" if optimizer_name == \"SGD\":\n",
" optimizer = optimizers.SGD(learning_rate=lr)\n",
" elif optimizer_name == \"Momentum\":\n",
" optimizer = optimizers.SGD(learning_rate=lr, momentum=0.9)\n",
" elif optimizer_name == \"Adam\":\n",
" optimizer = optimizers.Adam(learning_rate=lr)\n",
"\n",
" model.compile(optimizer=optimizer, loss=losses.MeanSquaredError())\n",
"\n",
" history = model.fit(x,y,epochs=epochs, verbose=0)\n",
" return history.history['loss']\n",
"\n",
"# Train models with different optimizers\n",
"losses_sgd = train_model(\"SGD\", x, y)\n",
"losses_momentum = train_model(\"Momentum\", x, y)\n",
"losses_adam = train_model(\"Adam\", x, y)\n",
"\n",
"to_display = [\n",
" (\"SGD\", losses_sgd),\n",
" (\"Momentum\", losses_momentum),\n",
" (\"Adam\", losses_adam),\n",
"]\n",
"\n",
"# Plot the loss curves\n",
"for name, losses in to_display:\n",
" plt.plot(losses, label=name)\n",
"plt.xlabel(\"Epoch\")\n",
"plt.ylabel(\"Loss\")\n",
"plt.legend()\n",
"plt.title(\"Optimizer Algorithm Comparison\")\n",
"plt.show()\n",
"\n",
"# Final results\n",
"for name, losses in to_display:\n",
" print(f\"Final Loss({name}): {losses[-1]}\")"
]
},
{
"cell_type": "markdown",
"id": "a39a0309-f79b-4456-956e-a5db78c3bc08",
"metadata": {},
"source": [
"# Exp4: Build a CNN on MNIST dataset to demonstrate convolution, pooling and classification"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "549fc3ae-94e1-4cc9-bd69-8f7813ae1609",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-09-25 14:16:28.733225: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2025-09-25 14:16:28.742919: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"E0000 00:00:1758789988.754267 10866 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"E0000 00:00:1758789988.757608 10866 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"W0000 00:00:1758789988.766096 10866 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1758789988.766106 10866 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1758789988.766108 10866 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1758789988.766109 10866 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"2025-09-25 14:16:28.769001: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sahyadri/anaconda3/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
"2025-09-25 14:16:30.417248: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9440 - loss: 0.1884 - val_accuracy: 0.9843 - val_loss: 0.0598\n",
"Epoch 2/5\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9834 - loss: 0.0548 - val_accuracy: 0.9887 - val_loss: 0.0428\n",
"Epoch 3/5\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9881 - loss: 0.0377 - val_accuracy: 0.9898 - val_loss: 0.0377\n",
"Epoch 4/5\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 5ms/step - accuracy: 0.9908 - loss: 0.0285 - val_accuracy: 0.9885 - val_loss: 0.0459\n",
"Epoch 5/5\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 5ms/step - accuracy: 0.9928 - loss: 0.0223 - val_accuracy: 0.9897 - val_loss: 0.0348\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.9882 - loss: 0.0321\n",
"Test accuracy: 0.9882\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras import layers, models\n",
"from tensorflow.keras.datasets import mnist\n",
"from tensorflow.keras.utils import to_categorical\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# 1. Get the dataset\n",
"(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
"\n",
"# Reshape data to fit the model (add chanel dimension)\n",
"x_train = x_train.reshape((x_train.shape[0],28,28,1)).astype('float32')/255\n",
"x_test = x_test.reshape((x_test.shape[0],28,28,1)).astype('float32')/255\n",
"\n",
"# One-hot encode the labels\n",
"y_train = to_categorical(y_train)\n",
"y_test = to_categorical(y_test)\n",
"\n",
"# 2. Build the CNN model\n",
"model = models.Sequential([\n",
" layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)),\n",
" layers.MaxPooling2D(2,2),\n",
" layers.Conv2D(64,(3,3),activation='relu'),\n",
" layers.MaxPooling2D(2,2),\n",
" layers.Flatten(),\n",
" layers.Dense(64,activation='relu'),\n",
" layers.Dense(10,activation='softmax')\n",
"])\n",
"\n",
"# 3. Compile the model\n",
"model.compile(\n",
" optimizer='adam',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy']\n",
")\n",
"\n",
"# 4. Train the model\n",
"history = model.fit(x_train,y_train,epochs=5, batch_size=64, validation_split=0.1)\n",
"\n",
"# 5. Evaluate the model\n",
"test_loss, test_acc = model.evaluate(x_test,y_test)\n",
"print(f\"Test accuracy: {test_acc:.4f}\")\n",
"\n",
"# 6. Plot training history (optional)\n",
"plt.plot(history.history['accuracy'], label=\"Test Accuracy\")\n",
"plt.plot(history.history['val_accuracy'], label=\"Val Accuracy\")\n",
"plt.title(\"Training and Validation Accuracy\")\n",
"plt.xlabel(\"Epoch\")\n",
"plt.ylabel(\"Accuracy\")\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "ba6911a8-51ec-4f43-a6fa-225cd4054286",
"metadata": {},
"source": [
"# Exp5: Implement an LSTM-based RNN for text classification to demonstrate sequence modelling, unfolding computional graphs, and handling long-term dependencies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "089cebed-a642-4512-8828-ec13b8ef4951",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-10-16 14:45:31.300712: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2025-10-16 14:45:31.310032: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"E0000 00:00:1760606131.320858 8578 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"E0000 00:00:1760606131.324164 8578 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"W0000 00:00:1760606131.332132 8578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1760606131.332142 8578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1760606131.332144 8578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1760606131.332145 8578 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"2025-10-16 14:45:31.335173: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training...\n",
"Epoch 1/5\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sahyadri/anaconda3/lib/python3.11/site-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
" warnings.warn(\n",
"2025-10-16 14:45:34.768448: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"313/313 - 15s - 49ms/step - accuracy: 0.5532 - loss: 0.6800 - val_accuracy: 0.5920 - val_loss: 0.6351\n",
"Epoch 2/5\n",
"313/313 - 14s - 43ms/step - accuracy: 0.6952 - loss: 0.5584 - val_accuracy: 0.7784 - val_loss: 0.5064\n",
"Epoch 3/5\n",
"313/313 - 14s - 44ms/step - accuracy: 0.6305 - loss: 0.6187 - val_accuracy: 0.5074 - val_loss: 0.7539\n",
"Epoch 4/5\n",
"313/313 - 13s - 42ms/step - accuracy: 0.6573 - loss: 0.5933 - val_accuracy: 0.7916 - val_loss: 0.4867\n",
"Epoch 5/5\n",
"313/313 - 13s - 42ms/step - accuracy: 0.7998 - loss: 0.4624 - val_accuracy: 0.8090 - val_loss: 0.4595\n",
"\n",
"Evaluating on test set...\n",
"\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 22ms/step - accuracy: 0.8053 - loss: 0.4633\n",
"\n",
"Test accuracy: 0.8053\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 1500x500 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.datasets import imdb\n",
"from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Embedding, LSTM, Dense, Dropout\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# 1. Load and Preprocess Data\n",
"vocab_size = 10000\n",
"max_sequence_length = 200\n",
"\n",
"(x_train,y_train),(x_test,y_test) = imdb.load_data(num_words=vocab_size)\n",
"x_train = pad_sequences(x_train, maxlen=max_sequence_length, padding='post')\n",
"x_test = pad_sequences(x_test, maxlen=max_sequence_length, padding='post')\n",
"\n",
"# 2. Build the LSTM RNN Model\n",
"model = Sequential()\n",
"model.add(Embedding(input_dim=vocab_size, output_dim=64,input_length=max_sequence_length))\n",
"model.add(LSTM(units=64,return_sequences=False))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(1,activation='sigmoid'))\n",
"\n",
"# 3. Compile the model\n",
"model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
"\n",
"# 4. Train the model\n",
"print(\"\\nTraining...\")\n",
"history = model.fit(x_train,y_train,epochs=5,batch_size=64,validation_split=0.2,verbose=2)\n",
"\n",
"# 5. Evaluate the model\n",
"print(\"\\nEvaluating on test set...\")\n",
"loss,accuracy = model.evaluate(x_test,y_test)\n",
"print(f\"\\nTest accuracy: {accuracy:.4f}\")\n",
"\n",
"# 6. Plot accuracy and Loss graphs\n",
"plt.figure(figsize=(15,5))\n",
"def plot_graphs(history, string, noOfCols=1, index=0):\n",
" plt.subplot(1,noOfCols,index+1)\n",
" plt.plot(history.history[string])\n",
" plt.plot(history.history['val_'+string])\n",
" plt.xlabel(\"Epochs\")\n",
" plt.ylabel(string.capitalize())\n",
" plt.title(f\"{string.capitalize()} Over Epochs\")\n",
" plt.legend([string,'val_'+string])\n",
" plt.grid()\n",
"\n",
"to_display = [\n",
" \"accuracy\",\n",
" \"loss\",\n",
"]\n",
"\n",
"for index, title in enumerate(to_display):\n",
" plot_graphs(history,title, len(to_display), index)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "bdc08cef",
"metadata": {},
"source": [
"# Exp6: Construct a Bidirectional RNN and compare it's performancce with a standard RNN on sequence prediction tasks"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4ffac51b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final Loss (RNN):2415.9666\n",
"Final Loss (BiRNN):1446.1538\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"import numpy as np\n",
"\n",
"# Synthetic dataset: predict next number in a sequence\n",
"seq_length = 10\n",
"num_samples = 1000\n",
"x= []\n",
"y =[]\n",
"for _ in range(num_samples):\n",
" start = np.random.randint(0,100)\n",
" seq = np.arange(start,start+seq_length)\n",
" x.append(seq[:-1])\n",
" y.append(seq[1:])\n",
"x = torch.tensor(x,dtype=torch.float32).unsqueeze(-1) # (batch,seq_len,input_size)\n",
"y = torch.tensor(y,dtype=torch.float32).unsqueeze(-1)\n",
"\n",
"class SimpleRNN(nn.Module):\n",
" def __init__(self, input_size=1, hidden_size=32, output_size=1):\n",
" super(SimpleRNN, self).__init__()\n",
" self.rnn = nn.RNN(input_size,hidden_size,batch_first=True)\n",
" self.fc = nn.Linear(hidden_size,output_size)\n",
" def forward(self,x):\n",
" out,_ = self.rnn(x)\n",
" return self.fc(out)\n",
"\n",
"class BiRNN(nn.Module):\n",
" def __init__(self,input_size=1,hidden_size=32,output_size=1):\n",
" super(BiRNN,self).__init__()\n",
" self.rnn = nn.RNN(input_size,hidden_size,batch_first=True,bidirectional=True)\n",
" self.fc = nn.Linear(hidden_size*2,output_size)\n",
" def forward(self,x):\n",
" out,_ = self.rnn(x)\n",
" return self.fc(out)\n",
"\n",
"def train_model(model,X,Y,epochs=50):\n",
" criterion = nn.MSELoss()\n",
" optimizer = optim.Adam(model.parameters(),lr=0.01)\n",
" for _ in range(epochs):\n",
" optimizer.zero_grad()\n",
" output = model(X)\n",
" loss = criterion(output,Y)\n",
" loss.backward()\n",
" optimizer.step()\n",
" return loss.item()\n",
"\n",
"rnn_model = SimpleRNN()\n",
"BiRNN_model = BiRNN()\n",
"rnn_loss = train_model(rnn_model,x,y)\n",
"BiRNN_loss = train_model(BiRNN_model,x,y)\n",
"print(f\"Final Loss (RNN):{rnn_loss:.4f}\")\n",
"print(f\"Final Loss (BiRNN):{BiRNN_loss:.4f}\")"
]
},
{
"cell_type": "markdown",
"id": "844e3298-ecf7-470a-9d90-616213db37eb",
"metadata": {},
"source": [
"# Exp7: Implement an encoder-decoder architecture with LSTMs for sequence-to-sequence learning"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "09ff9fd8-b926-4d20-992c-4cd2889740b0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.1199\n",
"Epoch 2/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0626\n",
"Epoch 3/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0566\n",
"Epoch 4/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0504\n",
"Epoch 5/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0428\n",
"Epoch 6/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0318\n",
"Epoch 7/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0127\n",
"Epoch 8/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0020\n",
"Epoch 9/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 7.1048e-04\n",
"Epoch 10/10\n",
"\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 4.8850e-04\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step\n",
"Input:\n",
" [0.518819 0.15346329 0.15712637 0.99405924 0.54429002]\n",
"Predicted reverse:\n",
" [0.58295447 0.9721017 0.15140787 0.17658892 0.51995903]\n",
"True reverse:\n",
" [0.54429002 0.99405924 0.15712637 0.15346329 0.518819 ]\n"
]
}
],
"source": [
"import numpy as np\n",
"from tensorflow.keras.models import Model\n",
"from tensorflow.keras.layers import Input, LSTM, Dense\n",
"\n",
"# Sample data: input->output\n",
"num_samples = 1000\n",
"timesteps = 5\n",
"input_dim = 1\n",
"latent_dim = 32\n",
"\n",
"# Create toy data\n",
"x = np.random.rand(num_samples, timesteps, input_dim)\n",
"y = np.flip(x, axis=1) # reversed sequences\n",
"\n",
"# Encoder\n",
"encoder_inputs = Input(shape=(timesteps,input_dim))\n",
"encoder_outputs, state_h, state_c = LSTM(latent_dim, return_state=True)(encoder_inputs)\n",
"encoder_states = [state_h,state_c]\n",
"\n",
"# Decoder\n",
"decoder_inputs = Input(shape=(timesteps,input_dim))\n",
"decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)\n",
"decoder_outputs,_,_ = decoder_lstm(decoder_inputs,initial_state=encoder_states)\n",
"decoder_dense = Dense(input_dim)\n",
"decoder_outputs = decoder_dense(decoder_outputs)\n",
"\n",
"# Full Model\n",
"model = Model([encoder_inputs,decoder_inputs], decoder_outputs)\n",
"model.compile(optimizer='adam',loss='mse')\n",
"\n",
"# Train (teacher forcing - decoder gerts previous true sequence)\n",
"model.fit((x,y), y, epochs=10, batch_size=32)\n",
"\n",
"# Test\n",
"pred = model.predict([x[:1],y[:1]])\n",
"print(\"Input:\\n\", x[0].squeeze())\n",
"print(\"Predicted reverse:\\n\", pred[0].squeeze())\n",
"print(\"True reverse:\\n\", y[0].squeeze())"
]
},
{
"cell_type": "markdown",
"id": "2d41b810-7514-48d6-804a-fc162660b27c",
"metadata": {},
"source": [
"# Exp8: Implement a Restricted Boltzman Machine (RBM) for learning binary data representations"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e2cbacd8-7ed7-4899-b881-b7c9addf49d8",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-11-06 14:26:51.626145: I tensorflow/core/framework/local_rendezvous.cc:407] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5, Reconstruction Loss: 0.1193\n",
"Epoch 2/5, Reconstruction Loss: 0.0841\n",
"Epoch 3/5, Reconstruction Loss: 0.0746\n",
"Epoch 4/5, Reconstruction Loss: 0.0693\n",
"Epoch 5/5, Reconstruction Loss: 0.0657\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 700x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Load and preprocesss MNIST\n",
"(x_train,_),_ = tf.keras.datasets.mnist.load_data()\n",
"x_train = x_train.astype('float32')/255.0\n",
"x_train = (x_train > 0.5).astype('float32') # binarize\n",
"x_train = x_train.reshape(-1,784)\n",
"\n",
"batch_size = 64\n",
"train_dataset = tf.data.Dataset.from_tensor_slices(x_train).shuffle(10000).batch(batch_size)\n",
"\n",
"# RBM Class\n",
"class RBM(tf.keras.Model):\n",
" def __init__(self,n_visible,n_hidden):\n",
" super(RBM,self).__init__()\n",
" self.n_visible = n_visible\n",
" self.n_hidden = n_hidden\n",
" # Parameters\n",
" initializer = tf.initializers.RandomNormal(mean=0.0, stddev=0.01)\n",
" self.W = tf.Variable(initializer([n_visible,n_hidden]),name='weights')\n",
" self.h_bias = tf.Variable(tf.zeros([n_hidden]),name='hidden_bias')\n",
" self.v_bias = tf.Variable(tf.zeros([n_visible]),name='visible_bias')\n",
"\n",
" def sample_prob(self,probs):\n",
" return tf.nn.relu(tf.sign(probs-tf.random.uniform(tf.shape(probs))))\n",
" \n",
" def sample_h(self,v):\n",
" prob_h = tf.nn.sigmoid(tf.matmul(v,self.W)+self.h_bias)\n",
" return prob_h, self.sample_prob(prob_h)\n",
"\n",
" def sample_v(self,h):\n",
" prob_v = tf.nn.sigmoid(tf.matmul(h,tf.transpose(self.W))+self.v_bias)\n",
" return prob_v, self.sample_prob(prob_v)\n",
"\n",
" def contrastive_divergence(self, v, lr=0.01):\n",
" # Positive Phase\n",
" prob_h,h0 = self.sample_h(v)\n",
" # Negative phase (reconstruction)\n",
" prob_v, v1 = self.sample_v(h0)\n",
" prob_h1,_ = self.sample_h(v1)\n",
" # Compute gradient\n",
" positive_grad = tf.matmul(tf.transpose(v),prob_h)\n",
" negative_grad = tf.matmul(tf.transpose(v1),prob_h1)\n",
" # Update weights and biases\n",
" batch_size = tf.cast(tf.shape(v)[0],tf.float32)\n",
" self.W.assign_add(lr*(positive_grad-negative_grad)/batch_size)\n",
" self.v_bias.assign_add(lr*tf.reduce_mean(v-v1,axis=0))\n",
" self.h_bias.assign_add(lr*tf.reduce_mean(prob_h-prob_h1,axis=0))\n",
" # Compute reconstruction loss (MSE)\n",
" loss = tf.reduce_mean(tf.square(v-v1))\n",
" return loss\n",
"\n",
"# Initialize and train RBM\n",
"n_visible = 784\n",
"n_hidden = 128\n",
"rbm = RBM(n_visible,n_hidden)\n",
"\n",
"n_epochs = 5\n",
"lr = 0.05\n",
"losses = []\n",
"\n",
"for epoch in range(n_epochs):\n",
" epoch_loss = 0\n",
" for batch in train_dataset:\n",
" loss = rbm.contrastive_divergence(batch,lr)\n",
" epoch_loss += loss.numpy()\n",
" avg_loss = epoch_loss/len(list(train_dataset))\n",
" losses.append(avg_loss)\n",
" print(f\"Epoch {epoch+1}/{n_epochs}, Reconstruction Loss: {avg_loss:.4f}\")\n",
"\n",
"# Plot training loss\n",
"plt.figure(figsize=(7,5))\n",
"plt.plot(range(1,n_epochs+1), losses, marker='o')\n",
"plt.title(\"RBM Reconstruction Loss over Epochs\")\n",
"plt.xlabel(\"Epoch\")\n",
"plt.ylabel(\"MSE Loss\")\n",
"plt.grid(True)\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"id": "d83e0d16-a4a3-450a-bdda-bbc593775338",
"metadata": {},
"source": [
"# Exp9: Develop a Denoising Autoencoder to reconstruct clean images from noisy input data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "40f6d322-2a92-4b84-9a7d-bf0d34a80a3e",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x400 with 20 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 34ms/step - loss: 0.1521 - val_loss: 0.0947\n",
"Epoch 2/5\n",
"\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 32ms/step - loss: 0.0915 - val_loss: 0.0872\n",
"Epoch 3/5\n",
"\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 32ms/step - loss: 0.0867 - val_loss: 0.0857\n",
"Epoch 4/5\n",
"\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 32ms/step - loss: 0.0845 - val_loss: 0.0832\n",
"Epoch 5/5\n",
"\u001b[1m235/235\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 32ms/step - loss: 0.0831 - val_loss: 0.0818\n",
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1000x400 with 30 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras import layers, models\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Load and normalize MNIST dataset\n",
"(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()\n",
"x_train, x_test = x_train / 255.0, x_test / 255.0\n",
"x_train, x_test = np.expand_dims(x_train, -1), np.expand_dims(x_test, -1)\n",
"\n",
"# Add random noise\n",
"x_train_noisy = np.clip(x_train + 0.3* np.random.normal(0, 1, x_train.shape), 0, 1)\n",
"x_test_noisy = np.clip(x_test + 0.3*np.random.normal(0, 1, x_test.shape), 0, 1)\n",
"\n",
"# Visualize clean vs noisy images\n",
"plt.figure(figsize=(10, 4))\n",
"for i in range(10):\n",
" plt.subplot(2, 10, i + 1)\n",
" plt.imshow(x_test[i].squeeze(), cmap='gray')\n",
" plt.axis('off')\n",
" plt.subplot(2, 10, i + 11)\n",
" plt.imshow(x_test_noisy[i].squeeze(), cmap='gray')\n",
" plt.axis('off')\n",
"plt.show()\n",
"\n",
"# Build Autoencoder\n",
"autoencoder = models.Sequential([\n",
" layers.Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(28, 28, 1)),\n",
" layers.MaxPooling2D((2, 2), padding='same'),\n",
" layers.Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
" layers.MaxPooling2D((2, 2), padding='same'),\n",
" layers.Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
" layers.UpSampling2D((2, 2)),\n",
" layers.Conv2D(32, (3, 3), activation='relu', padding='same'),\n",
" layers.UpSampling2D((2, 2)),\n",
" layers.Conv2D(1, (3, 3), activation='sigmoid', padding='same')\n",
"])\n",
"\n",
"autoencoder.compile(optimizer='adam', loss='binary_crossentropy')\n",
"autoencoder.fit(x_train_noisy, x_train, epochs=5, batch_size=256, validation_data=(x_test_noisy, x_test))\n",
"\n",
"# Predict and visualize denoised image\n",
"decoded_imgs = autoencoder.predict(x_test_noisy)\n",
"\n",
"plt.figure(figsize=(10, 4))\n",
"for i in range(10):\n",
" plt.subplot(3, 10, i + 1)\n",
" plt.imshow(x_test[i].squeeze(),cmap='gray')\n",
" plt.axis('off')\n",
" plt.subplot(3, 10, i + 11)\n",
" plt.imshow(x_test_noisy[i].squeeze(),cmap='gray')\n",
" plt.axis('off')\n",
" plt.subplot(3, 10, i + 21)\n",
" plt.imshow(decoded_imgs[i].squeeze(),cmap='gray')\n",
" plt.axis('off')\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "f60b8a76-d789-48c1-af66-cd42e89707e9",
"metadata": {},
"source": [
"# Exp10: Implement a simple Generative Adversarial Network (GAN) on the MNIST dataset to generate new handwritten digits"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "ea665a49-db80-4724-b2b1-168906ce7ec5",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sahyadri/TensorFlow/lib/python3.11/site-packages/keras/src/layers/core/dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
"/home/sahyadri/TensorFlow/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
"/home/sahyadri/TensorFlow/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:83: UserWarning: The model does not have any trainable weights.\n",
" warnings.warn(\"The model does not have any trainable weights.\")\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 0 | D loss: 0.683 | G loss: 0.696\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"E0000 00:00:1763029229.696004 8566 meta_optimizer.cc:967] remapper failed: INVALID_ARGUMENT: Mutation::Apply error: fanout 'gradient_tape/functional_30_1/sequential_13_1/leaky_re_lu_26_1/LeakyRelu/LeakyReluGrad' exist for missing node 'functional_30_1/sequential_13_1/conv2d_12_1/BiasAdd'.\n"
]
},
{
"data": {
"image/png": 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wh+U3P+WUU8pNX19fuYmI2LJlS7np6ekpN9OnTy83TzzxRLnZuXNnuYmImDBhQrmZOnVquZkzZ065aTkmGBHxqU99qtxs3bq13CxcuLDctHy/thzei4h49dVXy03L3+n5558vN7Nnzy43Hf7q+d9ccskl5eaRRx4pNy0HM8eOHVtuIiJOOumkcvPCCy+Um9/85jeDvsaTAgDJKACQjAIAySgAkIwCAMkoAJCMAgDJKACQjAIAySgAkIwCAMkoAJCGdfrC4447rvzmR44cKTdPPfVUuYmImD9/frl58skny82tt95abu6+++5y8+lPf7rcRET84Q9/KDeLFi0qN//4xz/KzfLly8tNRMTrr79ebs4999xys2PHjnJz3nnnlZs77rij3ES0HTvcv39/ubnuuuvKzfe///1y89WvfrXcRET86Ec/KjdXX311uVm/fn25aTkuGRExbFjHv4rTkiVLmj5rMJ4UAEhGAYBkFABIRgGAZBQASEYBgGQUAEhGAYBkFABIRgGAZBQASEYBgNTxFaaWg009PT3lpvWgVH9/f7k5++yzy80TTzxRbkaPHl1ufv/735ebiIilS5eWm4svvrjcrF27tty0HEiMiDh48GC5afneGz58eLl55plnyk3L1zsi4pprrik3//Vf/1Vunn766XLzxS9+sdy0HC2MiBg6dGi5aTkUOXPmzHLTcrQwImLjxo3l5vjjj2/6rMF4UgAgGQUAklEAIBkFAJJRACAZBQCSUQAgGQUAklEAIBkFAJJRACAZBQBSx1fuRowYUX7zBx54oNy0HpRq0XKEqre3t9w8+OCD5eaXv/xluYmIeOyxx8rNDTfcUG6GDKn/98SxY8fKTUTEkiVLys0VV1xRbrq7u8vNJZdcUm5effXVchMRcfjw4XJz7733lpvrr7++3Lz88svl5tlnny03ERGrVq36P/JZ3/3ud8tNq/Hjx5ebk0466X/gT+JJAYD/hVEAIBkFAJJRACAZBQCSUQAgGQUAklEAIBkFAJJRACAZBQCSUQAgGQUAUtfAwMBAJy+87bbbym++ffv2cnP06NFy0/pZLVcGx40bV27mzp1bbn7729+Wm4iIBQsWlJuXXnqp3Nx0003l5uGHHy43EW3XQceOHdv0WVUd/vj8h9bv8bPPPrvcbN68udy8//775Wb+/PnlZs+ePeUmImLWrFnlpuVa7FVXXVVu1q5dW24iIs4888xy88wzz5SbO++8c9DXeFIAIBkFAJJRACAZBQCSUQAgGQUAklEAIBkFAJJRACAZBQCSUQAgGQUA0rBOXzhkSH0/Wo6SDR8+vNxEREybNq3crFmzptwcOXKk3Jx88snl5nOf+1y5iYhYvXp1uVm5cmW56e3tLTct/0YREX/84x/LzY033lhutmzZUm62bdtWbjZs2FBuIiLmzJlTbrq6uspNy6HIRx99tNxcd9115SYi4tlnny03ixYtKjcthyJbjhZGRHz44YflpuUwYCc8KQCQjAIAySgAkIwCAMkoAJCMAgDJKACQjAIAySgAkIwCAMkoAJCMAgCp44N4LYe1pk+fXm5ajpJFROzevbvctPz5rr322nLz0EMPlZt//etf5SYi4rLLLis3EyZMKDdvvvlmuXnqqafKTUTE+eefX27mzp1bbl588cVyM3/+/HLT8veJaDuI98EHH5SbjRs3lpvx48eXm0svvbTcRESsXbu23LQcIZwxY0a5aT362HJ879ChQ02fNRhPCgAkowBAMgoAJKMAQDIKACSjAEAyCgAkowBAMgoAJKMAQDIKACSjAEDqGhgYGOjkhbfcckv5zd94441y03LAK6LtEFVvb2+5OeGEE8rNww8/XG4WL15cbiIiFixYUG5aDoy1fB1OP/30chMR8corr5SbkSNHlpuWA2N9fX3lZujQoeUmImLz5s3lZtiwjm9eplNPPbXcdHd3l5sHHnig3ES0/ay3HC586623yk3LEb2Itr/TGWecUW5WrFgx6Gs8KQCQjAIAySgAkIwCAMkoAJCMAgDJKACQjAIAySgAkIwCAMkoAJCMAgDJKACQOr6Seuedd5bf/MiRI+Wm5TJhRMSGDRvKzUUXXVRuWi5cDh8+vNzcd9995SYiYuHCheWmq6ur3LRcsz148GC5iYiYPHlyudm3b1+5Oemkk8rN/v37y80777xTbiLavl/Hjx9fbv7yl7+Um1mzZpWb1u+HCRMmlJtVq1aVmxtuuKHctPzZIiK2b99ebrZs2VJufvrTnw76Gk8KACSjAEAyCgAkowBAMgoAJKMAQDIKACSjAEAyCgAkowBAMgoAJKMAQOr4IN6vfvWr8pvv3bu33LQc0YuIGDKkvm8tn9Xd3V1uWrz++utNXcvX/Mwzzyw3H/vYx8rNa6+9Vm4iItatW1duli1bVm5ajvyNHj263Bw7dqzcRETs3Lmz3PT09JSblq/Djh07yk3LociIiClTppSbE088sdw8/fTT5eacc84pNxFthzb7+vrKzc033zzoazwpAJCMAgDJKACQjAIAySgAkIwCAMkoAJCMAgDJKACQjAIAySgAkIwCAKnjg3gA/P/PkwIAySgAkIwCAMkoAJCMAgDJKACQjAIAySgAkIwCAOm/AevvlBXNPp8sAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 500 | D loss: 1.114 | G loss: 0.255\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGFCAYAAAASI+9IAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjUsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvWftoOwAAAAlwSFlzAAAPYQAAD2EBqD+naQAADKVJREFUeJzt3MuKXOUax+Gv69CmAx6C4gkkMwWVSCSZiGNHXoB4BQ4y9SacieDUgRMvw4F4wCNGMtCRghhBjETojumu6j3a/w37wF7fu3d9WTTPM6631qpVq/LrNci7d3p6etoAoLW2uNcnAMB8iAIAIQoAhCgAEKIAQIgCACEKAIQoABCrqS+8efNm95s/+uij3TPPPvts90xrrX3//ffdMx9++GH3zCuvvNI9s7e31z1zdHTUPVM9VsXI//P4008/dc9cvHixe2a73XbPVK735cuXu2daa+2bb77pnhn1PT333HPdMzdu3NjBmfx7leuwXC67ZzabTfdMa7V7b71ed89MOT9PCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgCxdzpxU1Rl8Vdl5q233uqeaa21N998s3umsiTr7t273TP7+/vdMycnJ90zrdWWZC0W/X8bjFoe19rY5Xu9Rp5b5XuqnF9lZtS5tdbaiy++2D3z5Zdflo41Z5Xf05Rr7kkBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIFZTX3hwcND95oeHh90zb7/9dvdMa7XlWufOneue+euvv7pnnn/++e6Z69evd89UVZbbVTz00EOluVu3bv1/T+Q/eO+997pnqkv+Ku6///7umT///LN7pvKZPvjgg+6Z1157rXumtda++uqr7pmXX365e+ajjz7qnqkaeR/9N54UAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAIi904nrRZ955pnuN//ss8+6Zy5cuNA901ptS2pFZZthZQvppUuXumdaa+27777rnhl17apGbZCc8z3UWu38Fov+v/vO4nUYdZzK9a4ea7PZdM9MOT9PCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAxeSHeqKVkVZXzWy6X3TPHx8fdMxUjr3dlGdfBwUH3zK1bt7pnqseqmPsiuFFGXYfK76+12oLJymea+29wVzwpABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAMTkhXijvP7666W5999/v3tmvV53z7zxxhvdM++++273THUZ15wXf4281SqfqTIzs5/Pvxj1meb+3Y76XZyF6+BJAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACAmL8TbbDbdb75arbpnRjo4OOieOTo66p6Z85K6qpGfablcds+cnJx0z7jm9eNUfuuVf1Nam/d1qH6myj2+q+vgSQGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgJm+xWq/XuzyPe+LOnTvdM6+++mr3zMhFa6OWhS0W4/6eqCwZm/tyu4pff/31Xp/CfzRyAeGo73bUEr2qXV0HTwoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAxN7pyLV+E5zF7ZZzN2qz6sxutXumumF2u912z/ie6ub+b9GTTz7ZPfPzzz//19d4UgAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFACI1b0+gX9WXcY1anlV5TijFpm1Zrnd3436THNfmsZYc/9dTOFJAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACAmL8Qbtfhrsah16pdffumeefzxx7tnRl2H/f39IcdprfaZzp8/3z1zeHjYPdPavJfOjVwmeO3atSHHmvuCxFH3w2rVvy/0LCyy9KQAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEHunEzcxLZfL7jffbDb9JzRwodRvv/3WPfPII490z4xc6DZqsVZlud3BwUH3TGut3b59u3vmwQcfLB2r16jrXTXn86su0Ru1sK8yU13oWbHdbrtnplw7TwoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAxGrqCytbMUdtM6watfF05GeqqJxfZXPp+fPnu2eqRl3zyv1Q2R7cWu03WDHnzaojncXf+hSeFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBi8kK8yjKzuS+HGrXwauRirfV63T1zcnLSPVM5v+122z3TWmuLxXz/dhm5PG7UsS5dutQ9M3eVe2+5XHbPVH+3c1q+N99fGwDDiQIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQe6cTtyrNedFaa/UlYyPMfWlaxR9//NE9c+HChdKxKgvxNptN98z+/n73zPHxcfdM1cj7iLNpyj3kSQGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgJi/E++GHH7rf/Omnn+6eqRq1CG7UgjGLAf9h7te819yXHVbOb7vd7uBM/r3KgsQ5X7vWxi07tBAPgC6iAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAMRq6gvntLDp/3WsilGLta5evVqaq5zfZrPpnlmtJt86Uf2Orly50j3z+eefl441Z3O+x0f+1kf9BkdaLpfdM7u6Dp4UAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAIjJqy5v3LjR/eajti22Vtv0uViMaeIDDzzQPXP79u3SsUZuq+xV/W4//vjjIceq3EMjt1uO2pI66h6q/v622233zKjPNHKD664+kycFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgNg73eEGp6eeeqp75vfffy8dqzJ37ty50rF6jVq01lptyVjl/EYtZzuLKte7tdp3O2oR3Mj7YdSiusr1vnjxYvdMa639+OOPpbleFuIB0EUUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgFhNfuFq8kujuvir4r777uueWa/X3TPHx8fdM5XFWvv7+90zrbV29+7d0lyvuS9NG+UsXofKMsZRi/dGH6vX0dHRkOO0trvP5EkBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIPZOJ25Vqix1G7WEaqRRn6lyvVtrbbvdds/MfUFbxahFdWdxId6cr13VqGte+f1V7eozeVIAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQAiNXUF8594VXFnJeZVRfijTLna9daa59++mn3zNzv14o5L7er3g+jvqe53w+7+p7m/S8PAEOJAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAEBM3pJ6fHzc/ebL5bJ7purOnTvdM3PeBrndbrtnWmvt8uXL3TNz33haceXKle6ZTz75pHvmpZde6p75+uuvu2daq323c77HRzo8POyeOYuboafwpABAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQe6cTNzjNfeHVKHNfFjbn86suC5vzvTfyeq9Wk/dXRmWR5dzvh4r1et09U7l2X3zxRfdMa61dvXq1e2ZX94MnBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYCYvFGpsiSrslCqsuSJ/83IxWSjWAxYn5nztWutdn4nJyfdM6Oud1XlM03hSQGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAg9k4nbpcaueiJsUvq5r4IrrIkcbPZdM9Uzm/UuVWNWm43cole5ViVmcVi3n8z7+rfiHl/agCGEgUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAmLzi8bHHHut+85s3b3bPjNzG+s4773TPXLt2rXum8pleeOGF7pnWWvv222+7Zyrnt91uhxxnpMr5Pfzww90zTzzxRPdMa61dv369e2buG09Hmfv5Vezqu/WkAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABB7pxO3X53FhVKjFn9VjlN1dHTUPXNwcLCDM/lXy+WyNFdZvlexWk3eDxnHx8fdM5vNpnumtdYWi/6/4Sozlft11HGqx6pc87n/1nfFkwIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBATF6IB8DZ50kBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGA+Bu9emxEZ/5meAAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 1000 | D loss: 1.179 | G loss: 0.217\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 1500 | D loss: 1.204 | G loss: 0.204\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 2000 | D loss: 1.218 | G loss: 0.196\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 2500 | D loss: 1.227 | G loss: 0.192\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 3000 | D loss: 1.233 | G loss: 0.188\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGFCAYAAAASI+9IAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjUsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvWftoOwAAAAlwSFlzAAAPYQAAD2EBqD+naQAADLtJREFUeJzt3L+LXdUax+E1JzMZAjEEwUIRFEyRFCEWCoKVgqBBUgf0DwgKBhQiIhZiI0HFH6D2ItiKYmxUsFCLFBEFfxAURbEQRDEkGSczc4vL/RYx4HnX9axshuep5z17n3XOzCe7yLu0tbW11QCgtTa72jcAwHSIAgAhCgCEKAAQogBAiAIAIQoAhCgAEMtX+wYut7S01DX34YcflmfuvvvurmuN0Pt/CnvPr6rn/nrvbdS1Rv0/ztms799iTz/9dHnmqaee6rpW1Z49e8ozf/755wLu5Mqm/h367rvvyjO33HJLeWae+/OkAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABBLW3NucBq1aG3k0rSema+++qo8c+DAgfLMqPNubdyysPvvv78801pr7777btfcCKOW6LW2PZcdjjLycxplUQv7PCkAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAxEIX4o1crDVq4dVDDz1Unnn11VfLM5988kl5prXW7rzzzvLM6upqeWZtba08s7m5WZ5prbVvv/22PLN///7yzHXXXVee+fXXX8sz29HUl+gdO3asPPPaa68t4E6ubErLDj0pABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABBzb0m98cYbyy/+008/1W9o4ltSR22LvXTpUnmmtdZWVlbKM6POrteUNkhe7uzZs+WZCxculGdaa+3gwYPlmZGbSLebqW9+XdTvrScFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgFie9wd//vnn8osfOXKkPNPr2WefLc888cQT5ZlRy+N6FtuN9PLLL5dnHnnkkQXcyb+nZwnhvn37yjMjl6bdfPPN5Znvv//+37+RK5j68rip39+ieFIAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQAiKWtOTcxbW5ull98Nqs3p3cJ1fr6ennm+eefL888/vjj5Zkevecw5cVfI5d+jXpPPd/xnt+lqZv6Zzvq92I7nIMnBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYCYeyFe14sPWko2dSOX1E15Id5IUz6HqS9Ne+WVV8ozDz/8cHmmZ5lga9tzud2UeFIAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQAiLkX4lma1j/Tu/irx6hlYaurq+WZixcvlmda257fvR7bbRHc1Jc+vvjii+WZ48ePl2daa+2FF14ozzz66KNd1/onnhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQAiLm3pI4yciNmz/bSzc3NBdzJ1XX99deXZ3755ZfyzMS+alfN1LeD+pz+a+rbeXft2lWeOX/+/D/+jCcFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgFi+2jdwud9//71rbu/eveWZZ555pjzz5JNPlmdOnjxZnjlx4kR5pjVL0/5n1Hua+tI0+r3xxhvlmQcffHABdzKWJwUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAWNqacwvYzp07yy++vr5enuk1m9X7trGxUZ6Z+qK1Ude67777yjPvvfdeeaa1cUvnpr4Qb9QSwqkvSOy5v9tuu608c/r06fJMryktsvSkAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABBzL8TbjovgRpn60rSpL0Db3Nwsz+zYsWMBd/J323GJ3pQXELbW2uHDh8szvcsYq3rP7o477ijPfPrpp13X+ieeFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFACIubekdr34xLdv9tiO76nHuXPnyjPXXHPNAu7kykad+cjvw6FDh8ozn3/+ede1qkaeg9/BxfKkAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABDL8/7gdlxCNeo9TX1ZWI+e++t9T7t27eqaG2HU92Hktab+e9uj5z3NZvV/M2+HJX+eFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBi7oV4Paa+CG7KC8ZGLk0bpWfBWGvj3lPPmb/55psLuJOra9RSxVHXaa3/u1c18j0t6m+RJwUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAWOhCvB7bcRHcDTfcUJ55/fXXu67Vc3633npreebMmTPlmZHLDkctLrx06VJ55oEHHijP9Op5TzfddFN55ocffijPbGxslGdaa215uf5na8pLFVsbuzTzn3hSACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAIi5N0uNWih14sSJrrlFLYe6XM85zGb19h47dqw809q0F3/1fkaPPfZYeea5557rulZVz3K2zz77rOtaU/6Oj1pA+P/MTdnJkyfLM4s6B08KAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAMTS1pyr9o4cOVJ+8Xfeeac806tnY+DRo0fLM2+99VZ5ZqRR2ypHbexsbdz9ra+vl2dWVlbKM2tra+WZ1lpbXV3tmhth5Hdo6ltcR1nUe/KkAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABBzL8Trce7cufLM7t27F3AnVzZqqVvPEX/55Zdd1zp48GB5xkK8sXp/5Ua9p99++608c+211y7gTq5s1KK6kUv0pvS3yJMCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQMy9EG/UwqbZrK9TPQu5Dh06VJ754IMPyjObm5vlmd5z6NGzxOvixYvlmd5lhxsbG11zI/Sc3Y4dO7qu1fM96rHdFhC2Nu493XXXXeWZ1lr76KOPyjOLWgzoSQGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgJrcQb319vWtuZWXlX76TK1vUEqrL9Z73dlxm1mPUOYw87wsXLpRndu3aVZ6Z8tm11vc3YufOnV3Xqhr192GRPCkAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAxPIiX3zqy9n++uuv8sztt99enjl9+nR5ZuRirT/++KM8s2fPnvJM72e7e/furrmq8+fPD7lOr+223G7k0scpX6fXoj4nTwoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAxNLWAlcBjtx4um/fvvJMz1s/e/ZseabnHI4cOVKeaa21t99+u2tuhJHfh57Pdjar/xup5zrHjx8vz7TW2ksvvdQ1V7W2tlaeWV1dXcCdXFnPmV+6dKk8s7KyUp4ZuVnVllQAFk4UAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgFie9wdHLTPbvXt311zPorop612I12PUZ9u7LKzn/qb8nnrv7cyZM+WZQ4cOlWemfHa9Ri23O3z4cHmmtdZOnTrVNbcInhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAYmlrzq1PX3/9dfnF9+/fX7+hQcu4Whu7kKuq9xym/J569ZzFqEV1I897yovqRp7dyL8Ro8xm9X+fb25ulmfmOXNPCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgCxPO8PHjhwoPziU1/ONuXFWttx0doXX3wx5DqtjVvQtrw8969QbGxslGd6jVpu9/HHH5dnet1zzz3lmVOnTpVnej7bXj3L7Xpm5uFJAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAYtwZwwe69997yTM/mxB6rq6vlmSlvcG1t3PbNXlO+v94NuFO+v6l/ttvRbFb/N/08Z+dJAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACAWuhBv5JKs999/f8h1jh49Wp5ZW1srz/z444/lmdZa27t3b3lmz549Xdeasikvjxt5rZ5zmPrCuZ739M033yzgTv5u6mc3D08KAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCALG0tR02OAHwr/CkAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQPwHTHxacQ6Z9m4AAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 3500 | D loss: 1.238 | G loss: 0.186\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAGFCAYAAAASI+9IAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjUsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvWftoOwAAAAlwSFlzAAAPYQAAD2EBqD+naQAADOBJREFUeJzt3DuLneW7x/F7xslkNARMEERJ1LwBO8GzlYUBC1OooKJiiliKWGgnCBLxBSgSjaRTSMAihSDiCcVaLNUko4hIIgTMcWZ28Wf/im32dl03WXeePXw+da71HNYz852nyLWwsbGx0QCgtbZ4rU8AgOkQBQBCFAAIUQAgRAGAEAUAQhQACFEAIJau9Qn8TwsLC11zX375ZXnmwQcf7DpWVc81ra+vDztWj57/89h7bqOONer/cS4u9v0t9sYbb5RnXnvtta5jVV1//fXlmfPnz8/hTK5s6s/Q6upqeWb37t3lmVnOz5sCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQCxszLjBadSitZFL03pmvvrqq/LMAw88UJ4Zdb9bG7cs7NZbby3PtNbab7/91jU3wqgleq1tzmWHo4z8nkaZ18I+bwoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAsTTPDx+5WGvUwqsTJ06UZ3oW4h0/frw801pre/fuLc/cdNNNXceqWl1d7Zo7efJkeeaOO+4ozzzyyCPlmakvgusxr0VrV+M4vR566KHyzBdffDGHM7myKT1H3hQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQAiIWNGdcb3n777eUP79koOvUtqaM2SP7+++/lmdZau+WWW8ozo+5dr1EbJHvuw08//VSe+fXXX8szrfVt253S9s3/b6a++XVeP7feFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBi5oV4PYueHn/88fLMRx99VJ5prbUXXnihPHPo0KHyzJQX7/XquaYjR46UZ5555pnyTGvj7sWlS5fKM0tLS+WZ3utZXl4uzzz11FPlmffff78802Pqz/jUz29evCkAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAxMwL8UYtlOpdQvXXX3+VZ1566aXyzNSXhU158dfIpV+jrmllZaU8c/78+TmcybU19e921M/FZrgP3hQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAYmnWfzhyuV2PG2+8cchxPvjgg/LMyCVZUzbyeRi1AK1nud3Ul6a9/vrrcziTf5r60sep/9zO6/y8KQAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgDEwsaMW5VGLjMbZdTCq824CG7Xrl3lmVOnTpVnWhu3jHEzLkCb8n2Y+kK8t956qzzzyiuvlGdaa+3AgQPlmXfeeafrWP/GmwIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAMfOW1FE24zbWkXru3/79+8sz7733Xnlm5KP2888/l2f27NkzhzP5p6lvB53Yr4RrZuq/i5aXl8szFy5c+Nd/400BgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAICa3EG+WhU1XsrKyUp557rnnyjOHDx8uzxw/frw8s3fv3vJMa5am/bdR1zRyaZrvdqyjR4+WZ/bt2zeHMxnLmwIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAzLwQb2lpqfzha2tr5ZlePYu/1tfXhxxn5KK1ixcvlmeWl5fLM/fdd1955uuvvy7PtDZu6dxmXIjXY+pL9BYX63/Lvvzyy+WZt99+uzzTa0rLDr0pABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAMTMC/Gmvghu5EKuqqkvTes5v3PnzpVnVlZWyjO9LNH7j56ljz0L53r0/szec8895Zlvv/2261hVvd/to48+Wp755JNPuo71b7wpABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABAzb0nt+vBBm1VH2ozX1OOXX34pz+zZs+fqn8j/YtQ9H/k8rK6ulmd27drVdayqkffBz+B8eVMAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQAiKVZ/+FmXEI16pqmviysR8/59V7TzTff3DU3wqjnYeSxpv5z2+OPP/4oz/Q8d5thyZ83BQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYCYeSFejx07dpRnzpw503WszbbcbuTStFGWlvoet7W1tat8JlfWc89PnDgxhzO5tkYtVRx1nNbGLVUceU3z+l3kTQGAEAUAQhQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgFjZm3KA2ctFTj1GL4Hruw5YtW8oz586dK8+01rd0buvWreWZCxculGd6v6NRz17P+V26dKk8s7y8XJ5pbdwzvn379vLM2bNn53AmV9bzPPQsVVxcrP/NPHKRpYV4AMydKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAx8/a0Ucu47r///q65KS9N6zm3nsV2rY37nkYuC9uxY0d55vTp013HqupZdnjkyJGuY222Z7z3WR31jI/04osvlmfmdR+8KQAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQCxszrtp7/vnnyx9++PDh8kyv9fX18sy+ffvKM8eOHSvPjDRqW+WojZ2tjTu/s2fPlme2b99enrl48WJ5prXWlpeXu+ZGGPkMTX2L6yjzuiZvCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAx80K8Hj2Lv0Yu/Rq11K1nWd8PP/zQdaw777yzPGMh3li9P3Kjrum7774rz9x9991zOJMrG7WobuQSvVHfrYV4AJSIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABAzL8QbtbCp9zjbtm0rz6ysrJRn/vzzz/LM1Be69Zzf2tpaeabnfrfW2uXLl7vmRpj6d9vDNf1HzzVt3769PNNaa2fPni3PzGsxoDcFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgJjcQrzeJU9TP7+q3uvZjMvMeoy6DyPv96lTp8ozu3fvLs9M+d71zi0ujvn7d9Tvh3nypgBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQS/P88KkvZ+s5v507d5Znzpw5U54ZuVjr008/Lc88/PDD5ZmpL9777LPPrvUp/J8223K7kUsfp3ycXvP6nrwpABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABALG3NcBThyK+YNN9xQnnn66afLM++++255puc+3HvvveWZ1lr75ptvuuaqeh6bxcVxf4NMeUPvs88+2zX34YcfXuUzubKTJ0+WZ2677bY5nMmV9Xy3f//9d3lm27Zt5ZneX6ejfp5sSQWgRBQACFEAIEQBgBAFAEIUAAhRACBEAYAQBQBCFAAIUQAgRAGAmHkh3qhlYTt37uyaO3369FU+kysbtWjt+++/L8+01tpdd91Vnhn13fYuCxu5WLFq5OK9tbW18kzP0rSpPw89eq6p5/z2799fnmmttUOHDnXNVVmIB0CJKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgCxNOs/vHTpUv3Dl2b++Bi5/GzUQq7Lly+XZ7Zu3TrsWOvr6+WZKS+pa63vPvQ8rz2mvhhw5JK/HisrK0OOM/KarrvuuvJMz4LEWXhTACBEAYAQBQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAEIUAIiZN4Bt2bKl/OGjFs71mvJSt5H3bnFxzN8GTzzxxJDjtNa33K7nnvf8XPQs6+s1arndxx9/XJ7pdeDAgfLMwYMHyzM9i/d6f6f0LLfrWWQ5C28KAIQoABCiAECIAgAhCgCEKAAQogBAiAIAIQoAhCgAEKIAQIgCACEKAER9leREPfbYY+WZo0ePzuFM/unVV18tz0x5g2tr47Zv9pry+fVuwJ3y+U39ux1l6tuNZzk/bwoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgCEKAAQogBAiAIAMdeFeCOXZB07dmzIcQ4ePFieefPNN8szq6ur5ZnWWvv888/LM08++WTXsaZsysvjRh6r5z5MeeFca33X9OOPP87hTP5pfX29a25KCzC9KQAQogBAiAIAIQoAhCgAEKIAQIgCACEKAIQoABCiAECIAgAhCgDEwsbUt18BMIw3BQBCFAAIUQAgRAGAEAUAQhQACFEAIEQBgBAFAOK/AOhMZmsqARJoAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 4000 | D loss: 1.241 | G loss: 0.184\n"
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step 4500 | D loss: 1.244 | G loss: 0.183\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Execution done\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras import layers\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Load and normalize MNIST\n",
"(x_train,_),_ = tf.keras.datasets.mnist.load_data()\n",
"x_train = (x_train.astype('float32')-127.5)/127.5\n",
"x_train = np.expand_dims(x_train, axis = -1)\n",
"\n",
"batch_size = 128\n",
"buffer_size = 60000\n",
"train_dataset = tf.data.Dataset.from_tensor_slices(x_train).shuffle(buffer_size).batch(batch_size)\n",
"\n",
"# Generator\n",
"def build_generator():\n",
" model = tf.keras.Sequential([\n",
" layers.Dense(7*7*128, input_dim=100),\n",
" layers.LeakyReLU(0.2),\n",
" layers.Reshape((7,7,128)),\n",
" layers.Conv2DTranspose(64, (5,5), strides=2, padding='same'),\n",
" layers.LeakyReLU(0.2),\n",
" layers.Conv2DTranspose(1, (5,5), strides=2, padding='same', activation='tanh'),\n",
" ])\n",
" return model\n",
"def build_discriminator():\n",
" model = tf.keras.Sequential([\n",
" layers.Conv2D(64, (5, 5), strides=2, padding=\"same\", input_shape=[28, 28, 1]),\n",
" layers.LeakyReLU(0.2),\n",
" layers.Dropout(0.3),\n",
" layers.Conv2D(128, (5, 5), strides=2, padding=\"same\"),\n",
" layers.LeakyReLU(0.2),\n",
" layers.Dropout(0.3),\n",
" layers.Flatten(),\n",
" layers.Dense(1, activation=\"sigmoid\")\n",
" ])\n",
" return model\n",
"\n",
"# Models\n",
"generator = build_generator()\n",
"discriminator = build_discriminator()\n",
"discriminator.compile(loss='binary_crossentropy',\n",
" optimizer='adam',\n",
" metrics=['accuracy'])\n",
"# GAN combined model\n",
"discriminator.trainable = False\n",
"gan_input = tf.keras.Input(shape=(100,))\n",
"gan_output = discriminator(generator(gan_input))\n",
"gan = tf.keras.Model(gan_input,gan_output)\n",
"gan.compile(loss='binary_crossentropy',\n",
" optimizer='adam')\n",
"\n",
"# Training\n",
"epochs = 5000 # Small but enough to get clear digits\n",
"for step in range(epochs):\n",
" # Train Discriminator\n",
" noise = np.random.normal(0, 1, (batch_size, 100))\n",
" fake = generator.predict(noise, verbose=0)\n",
" real = x_train[np.random.randint(0, x_train.shape[0], batch_size)]\n",
" x = np.concatenate([real,fake])\n",
" y = np.concatenate([np.ones((batch_size,1)), np.zeros((batch_size,1))])\n",
" d_loss,_ = discriminator.train_on_batch(x,y)\n",
"\n",
" # Train Generator\n",
" noise = np.random.normal(0,1,(batch_size,100))\n",
" y_gen = np.ones((batch_size,1))\n",
" g_loss = gan.train_on_batch(noise, y_gen)\n",
"\n",
" # Print progress and save sample every 500 steps\n",
" if step%500 == 0:\n",
" print(f\"Step {step} | D loss: {d_loss:.3f} | G loss: {g_loss:.3f}\")\n",
" z = np.random.normal(0,1,(1,100))\n",
" gen_img = generator.predict(z, verbose=0)[0,:,:,0]\n",
" plt.imshow(gen_img*0.5+0.5, cmap=\"gray\")\n",
" plt.axis(\"off\")\n",
" plt.show()\n",
"\n",
"print('Execution done')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "tensorflow",
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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