03_M02L02_Lab_Practical Application of Transpose Convolution__M.ipynb

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    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/BQm_EV6i0_j80CQZ8vcLvw/SN-web-lightmode.png\" width=300 height=300 />\n"
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    "## Lab: Practical Application of Transpose Convolution  \n",
    "\n",
    "**Estimated time needed: 30 minutes**\n",
    "\n",
    "This lab will guide you through creating, training, and evaluating models that use transpose convolution layers for tasks such as image reconstruction. \n",
    "\n",
    "##### Learning objectives: \n",
    "\n",
    "By the end of this lab, you will: \n",
    "\n",
    "- Apply transpose convolution in practical scenarios using Keras.  \n",
    "\n",
    "- Create, compile, train, and evaluate the model \n",
    "\n",
    "- Visualize the results  \n",
    "\n",
    "##### Prerequisites: \n",
    "\n",
    "- Basic understanding of Python programming \n",
    "\n",
    "- Familiarity with convolutional neural network (CNN) concepts \n",
    "\n",
    "- Keras and TensorFlow installed \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "#### Steps: \n",
    "**Step 1: Import Necessary Libraries**\n",
    "\n",
    "Before you start, make sure to import the required libraries: TensorFlow and Keras. Keras is included within TensorFlow as `tensorflow.keras`. \n"
   ]
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-11-28 13:06:31.860895: I tensorflow/core/util/port.cc:113] 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",
      "2024-11-28 13:06:31.863297: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-11-28 13:06:31.867313: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-11-28 13:06:31.878737: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:479] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-11-28 13:06:31.901659: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:10575] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-11-28 13:06:31.901704: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1442] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-11-28 13:06:31.918707: 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 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-11-28 13:06:32.877233: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter('ignore')\n",
    "\n",
    "!pip install tensorflow==2.16.2 \n",
    "\n",
    "!pip install matplotlib \n",
    "import tensorflow as tf \n",
    "\n",
    "from tensorflow.keras.models import Model \n",
    "\n",
    "from tensorflow.keras.layers import Input, Conv2D, Conv2DTranspose, UpSampling2D \n",
    "\n",
    "import numpy as np \n",
    "\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "\n",
    " `tensorflow` is the main library for machine learning in Python. \n",
    "\n",
    "  `Model, Input, Conv2D, and Conv2DTranspose` are used to create and manipulate the layers in your model. \n",
    "\n",
    "  `numpy` and `matplotlib` are used for data manipulation and visualization. \n",
    "\n",
    " \n",
    "**Step 2: Define the Input Layer**\n",
    "\n",
    "You need to define the input shape for your model. For simplicity, let's assume you are working with an input image of size 28x28 with 1 channel (grayscale). \n",
    "\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_layer = Input(shape=(28, 28, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "\n",
    "`Input(shape=(28, 28, 1))` creates an input layer that expects 28x28 grayscale images.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 3: Add convolutional and transpose convolutional layers**\n",
    "\n",
    "You will add a convolutional layer to extract features, followed by a transpose convolution layer to reconstruct the image. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv_layer = Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same')(input_layer) \n",
    "\n",
    "transpose_conv_layer = Conv2DTranspose(filters=1, kernel_size=(3, 3), activation='sigmoid', padding='same')(conv_layer) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "\n",
    "`Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same')` creates a convolution layer with 32 filters. \n",
    "\n",
    "`Conv2DTranspose(filters=1, kernel_size=(3, 3), activation='sigmoid', padding='same')` creates a transpose convolution layer for reconstructing the image. \n",
    "\n",
    "**Step 4: Create the Model**\n",
    "\n",
    "Now, you will create the model by specifying the input and output layers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=input_layer, outputs=transpose_conv_layer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "\n",
    "`Model(inputs=input_layer, outputs=transpose_conv_layer)` creates a Keras model that connects the input layer to the output layer through the convolutional and transpose convolutional layers.  \n",
    "\n",
    "**Step 5: Compile the Model**\n",
    "\n",
    "Before training the model, you need to compile it. You will specify the loss function, optimizer, and evaluation metrics. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "\n",
    "- optimizer=`adam` specifies the Adam optimizer. \n",
    "\n",
    "- loss=`binary_crossentropy` specifies the binary cross-entropy loss function, suitable for binary pixel classification. \n",
    "\n",
    "- metrics=[`accuracy`] directs Keras to evaluate the model using accuracy during training. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 6: Train the Model**\n",
    "\n",
    "You can now train the model on some training data. For this example, let's assume X_train is our training input data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 0.0000e+00 - loss: 0.0789 - val_accuracy: 0.0000e+00 - val_loss: 0.0606\n",
      "Epoch 2/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - accuracy: 0.0000e+00 - loss: 0.0545 - val_accuracy: 0.0000e+00 - val_loss: 0.0374\n",
      "Epoch 3/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - accuracy: 0.0000e+00 - loss: 0.0327 - val_accuracy: 0.0000e+00 - val_loss: 0.0207\n",
      "Epoch 4/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - accuracy: 0.0000e+00 - loss: 0.0177 - val_accuracy: 0.0000e+00 - val_loss: 0.0109\n",
      "Epoch 5/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - accuracy: 0.0000e+00 - loss: 0.0095 - val_accuracy: 0.0000e+00 - val_loss: 0.0063\n",
      "Epoch 6/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 28ms/step - accuracy: 0.0000e+00 - loss: 0.0057 - val_accuracy: 0.0000e+00 - val_loss: 0.0043\n",
      "Epoch 7/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 27ms/step - accuracy: 0.0000e+00 - loss: 0.0039 - val_accuracy: 0.0000e+00 - val_loss: 0.0033\n",
      "Epoch 8/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - accuracy: 0.0000e+00 - loss: 0.0030 - val_accuracy: 0.0000e+00 - val_loss: 0.0025\n",
      "Epoch 9/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - accuracy: 0.0000e+00 - loss: 0.0024 - val_accuracy: 0.0000e+00 - val_loss: 0.0021\n",
      "Epoch 10/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.0000e+00 - loss: 0.0020 - val_accuracy: 0.0000e+00 - val_loss: 0.0018\n"
     ]
    }
   ],
   "source": [
    "# Generate synthetic training data \n",
    "\n",
    "X_train = np.random.rand(1000, 28, 28, 1) \n",
    "\n",
    "y_train = X_train # For reconstruction, the target is the input \n",
    "\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "\n",
    "- X_train is a placeholder for your actual training data. \n",
    "\n",
    "- y_train is the same as X_train for image reconstruction. \n",
    "\n",
    "- model.fit trains the model for a specified number of epochs and batch size.\n",
    "\n",
    "  \n",
    "**Step 7: Evaluate the Model**\n",
    "\n",
    "After training, you can evaluate the model on test data to see how well it performs. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step - accuracy: 0.0000e+00 - loss: 0.0018\n",
      "Test loss: 0.0018302910029888153\n",
      "Test accuracy: 0.0\n"
     ]
    }
   ],
   "source": [
    " # Generate synthetic test data \n",
    "\n",
    "X_test = np.random.rand(200, 28, 28, 1) \n",
    "\n",
    "y_test = X_test \n",
    "\n",
    "\n",
    "loss, accuracy = model.evaluate(X_test, y_test) \n",
    "\n",
    "print(f'Test loss: {loss}') \n",
    "\n",
    "print(f'Test accuracy: {accuracy}') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "\n",
    "`X_test` and `y_test` are placeholders for your actual test data. \n",
    "\n",
    "`model.evaluate` computes the loss and accuracy of the model on test data. . \n",
    "\n",
    "**Step 8: Visualize the Results**\n",
    "\n",
    "To better understand the model's performance, let's visualize some reconstructed images.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict on test data \n",
    "y_pred = model.predict(X_test) \n",
    "\n",
    "# Plot some sample images \n",
    "\n",
    "n = 10 # Number of samples to display \n",
    "\n",
    "plt.figure(figsize=(20, 4))\n",
    "\n",
    "for i in range(n): \n",
    "\n",
    "    # Display original \n",
    "    ax = plt.subplot(2, n, i + 1) \n",
    "    plt.imshow(X_test[i].reshape(28, 28), cmap='gray')\n",
    "    plt.title(\"Original\") \n",
    "    plt.axis('off') \n",
    "    # Display reconstruction \n",
    "    ax = plt.subplot(2, n, i + 1 + n) \n",
    "    plt.imshow(y_pred[i].reshape(28, 28), cmap='gray')\n",
    "    plt.title(\"Reconstructed\")\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "\n",
    "`model.predict` generates predictions for the test data. \n",
    "\n",
    "`plt.imshow` visualizes the original and reconstructed images side by side for comparison. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Practice exercises\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 1: Experiment with Different Kernel Sizes \n",
    "\n",
    "**Objective:** Understand the impact of different kernel sizes on the model's performance. \n",
    "\n",
    "**Instructions:**\n",
    "1. Modify the kernel size of the `Conv2D` and `Conv2DTranspose` layers. \n",
    "2. Recompile, train, and evaluate the model. \n",
    "3. Observe and record the differences in performance.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 44ms/step - accuracy: 0.0000e+00 - loss: 0.0750 - val_accuracy: 0.0000e+00 - val_loss: 0.0438\n",
      "Epoch 2/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.0000e+00 - loss: 0.0351 - val_accuracy: 0.0000e+00 - val_loss: 0.0155\n",
      "Epoch 3/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - accuracy: 0.0000e+00 - loss: 0.0124 - val_accuracy: 0.0000e+00 - val_loss: 0.0063\n",
      "Epoch 4/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.0000e+00 - loss: 0.0055 - val_accuracy: 0.0000e+00 - val_loss: 0.0041\n",
      "Epoch 5/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - accuracy: 0.0000e+00 - loss: 0.0039 - val_accuracy: 0.0000e+00 - val_loss: 0.0035\n",
      "Epoch 6/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - accuracy: 0.0000e+00 - loss: 0.0034 - val_accuracy: 0.0000e+00 - val_loss: 0.0031\n",
      "Epoch 7/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - accuracy: 0.0000e+00 - loss: 0.0031 - val_accuracy: 0.0000e+00 - val_loss: 0.0029\n",
      "Epoch 8/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - accuracy: 0.0000e+00 - loss: 0.0028 - val_accuracy: 0.0000e+00 - val_loss: 0.0027\n",
      "Epoch 9/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - accuracy: 0.0000e+00 - loss: 0.0026 - val_accuracy: 0.0000e+00 - val_loss: 0.0025\n",
      "Epoch 10/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - accuracy: 0.0000e+00 - loss: 0.0025 - val_accuracy: 0.0000e+00 - val_loss: 0.0024\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step - accuracy: 0.0000e+00 - loss: 0.0023\n",
      "Test loss: 0.0023367858957499266\n",
      "Test accuracy: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "\n",
    "from tensorflow.keras.layers import Dropout, Conv2D, Conv2DTranspose, Input\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Define the input layer\n",
    "input_layer = Input(shape=(28, 28, 1))\n",
    "\n",
    "# Add convolutional and transpose convolutional layers with different kernel sizes\n",
    "conv_layer = Conv2D(filters=32, kernel_size=(5, 5), activation='relu', padding='same')(input_layer)\n",
    "transpose_conv_layer = Conv2DTranspose(filters=1, kernel_size=(5, 5), activation='sigmoid', padding='same')(conv_layer)\n",
    "\n",
    "# Create the model\n",
    "model = Model(inputs=input_layer, outputs=transpose_conv_layer)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f\"Test loss: {loss}\")\n",
    "print(f\"Test accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click here for solution</summary> </br>\n",
    "\n",
    "```python\n",
    "from tensorflow.keras.layers import Dropout, Conv2D, Conv2DTranspose, Input\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Define the input layer\n",
    "input_layer = Input(shape=(28, 28, 1))\n",
    "\n",
    "# Add convolutional and transpose convolutional layers with different kernel sizes\n",
    "conv_layer = Conv2D(filters=32, kernel_size=(5, 5), activation='relu', padding='same')(input_layer)\n",
    "transpose_conv_layer = Conv2DTranspose(filters=1, kernel_size=(5, 5), activation='sigmoid', padding='same')(conv_layer)\n",
    "\n",
    "# Create the model\n",
    "model = Model(inputs=input_layer, outputs=transpose_conv_layer)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f'Test loss: {loss}')\n",
    "print(f'Test accuracy: {accuracy}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 2: Add Dropout Layers\n",
    "\n",
    "**Objective:** Add dropout layers to prevent overfitting.\n",
    "\n",
    "**Instructions:**\n",
    "1. Add dropout layers after the convolutional layer. \n",
    "2. Set the dropout rate to 0.5.\n",
    "3. Recompile, train, and evaluate the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 50ms/step - accuracy: 0.0000e+00 - loss: 0.0804 - val_accuracy: 0.0000e+00 - val_loss: 0.0628\n",
      "Epoch 2/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - accuracy: 0.0000e+00 - loss: 0.0583 - val_accuracy: 0.0000e+00 - val_loss: 0.0379\n",
      "Epoch 3/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 44ms/step - accuracy: 0.0000e+00 - loss: 0.0365 - val_accuracy: 0.0000e+00 - val_loss: 0.0202\n",
      "Epoch 4/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - accuracy: 0.0000e+00 - loss: 0.0227 - val_accuracy: 0.0000e+00 - val_loss: 0.0115\n",
      "Epoch 5/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - accuracy: 0.0000e+00 - loss: 0.0161 - val_accuracy: 0.0000e+00 - val_loss: 0.0076\n",
      "Epoch 6/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 44ms/step - accuracy: 0.0000e+00 - loss: 0.0128 - val_accuracy: 0.0000e+00 - val_loss: 0.0057\n",
      "Epoch 7/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - accuracy: 0.0000e+00 - loss: 0.0111 - val_accuracy: 0.0000e+00 - val_loss: 0.0046\n",
      "Epoch 8/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - accuracy: 0.0000e+00 - loss: 0.0100 - val_accuracy: 0.0000e+00 - val_loss: 0.0039\n",
      "Epoch 9/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 47ms/step - accuracy: 0.0000e+00 - loss: 0.0091 - val_accuracy: 0.0000e+00 - val_loss: 0.0034\n",
      "Epoch 10/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - accuracy: 0.0000e+00 - loss: 0.0085 - val_accuracy: 0.0000e+00 - val_loss: 0.0030\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - accuracy: 0.0000e+00 - loss: 0.0030\n",
      "Test loss: 0.002963006729260087\n",
      "Test accuracy: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "\n",
    "from tensorflow.keras.layers import Dropout, Conv2D, Conv2DTranspose, Input\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Define the input layer\n",
    "input_layer = Input(shape=(28, 28, 1))\n",
    "\n",
    "# Add convolutional, dropout, and transpose convolutional layers\n",
    "conv_layer = Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same')(input_layer)\n",
    "dropout_layer = Dropout(0.5)(conv_layer)\n",
    "transpose_conv_layer = Conv2DTranspose(filters=1, kernel_size=(3, 3), activation='sigmoid', padding='same')(dropout_layer)\n",
    "\n",
    "# Create the model\n",
    "model = Model(inputs=input_layer, outputs=transpose_conv_layer)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f\"Test loss: {loss}\")\n",
    "print(f\"Test accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click here for solution</summary> </br>\n",
    "\n",
    "```python\n",
    "from tensorflow.keras.layers import Dropout, Conv2D, Conv2DTranspose, Input\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Define the input layer\n",
    "input_layer = Input(shape=(28, 28, 1))\n",
    "\n",
    "# Add convolutional, dropout, and transpose convolutional layers\n",
    "conv_layer = Conv2D(filters=32, kernel_size=(3, 3), activation='relu', padding='same')(input_layer)\n",
    "dropout_layer = Dropout(0.5)(conv_layer)\n",
    "transpose_conv_layer = Conv2DTranspose(filters=1, kernel_size=(3, 3), activation='sigmoid', padding='same')(dropout_layer)\n",
    "\n",
    "# Create the model\n",
    "model = Model(inputs=input_layer, outputs=transpose_conv_layer)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f'Test loss: {loss}')\n",
    "print(f'Test accuracy: {accuracy}')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercise 3: Use Different Activation Functions \n",
    "\n",
    "**Objective:** Experiment with different activation functions and observe their impact on model performance. \n",
    "\n",
    "**Instructions:**\n",
    "1. Change the activation function of the convolutional and transpose convolutional layers to `tanh`. \n",
    "2. Recompile, train, and evaluate the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - accuracy: 0.0000e+00 - loss: 0.1973 - val_accuracy: 0.0000e+00 - val_loss: 0.0583\n",
      "Epoch 2/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 0.0469 - val_accuracy: 0.0000e+00 - val_loss: 0.0252\n",
      "Epoch 3/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 0.0211 - val_accuracy: 0.0000e+00 - val_loss: 0.0123\n",
      "Epoch 4/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 0.0108 - val_accuracy: 0.0000e+00 - val_loss: 0.0076\n",
      "Epoch 5/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - accuracy: 0.0000e+00 - loss: 0.0071 - val_accuracy: 0.0000e+00 - val_loss: 0.0060\n",
      "Epoch 6/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 0.0057 - val_accuracy: 0.0000e+00 - val_loss: 0.0052\n",
      "Epoch 7/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 29ms/step - accuracy: 0.0000e+00 - loss: 0.0051 - val_accuracy: 0.0000e+00 - val_loss: 0.0048\n",
      "Epoch 8/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.0000e+00 - loss: 0.0048 - val_accuracy: 0.0000e+00 - val_loss: 0.0046\n",
      "Epoch 9/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 31ms/step - accuracy: 0.0000e+00 - loss: 0.0045 - val_accuracy: 0.0000e+00 - val_loss: 0.0044\n",
      "Epoch 10/10\n",
      "\u001b[1m25/25\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step - accuracy: 0.0000e+00 - loss: 0.0044 - val_accuracy: 0.0000e+00 - val_loss: 0.0043\n",
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.0000e+00 - loss: 0.0042\n",
      "Test loss: 0.0042707109823822975\n",
      "Test accuracy: 0.0\n"
     ]
    }
   ],
   "source": [
    "# Write your code here\n",
    "\n",
    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Input\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Define the input layer\n",
    "input_layer = Input(shape=(28, 28, 1))\n",
    "\n",
    "# Add convolutional and transpose convolutional layers with different activation functions\n",
    "conv_layer = Conv2D(filters=32, kernel_size=(3, 3), activation='tanh', padding='same')(input_layer)\n",
    "transpose_conv_layer = Conv2DTranspose(filters=1, kernel_size=(3, 3), activation='tanh', padding='same')(conv_layer)\n",
    "\n",
    "# Create the model\n",
    "model = Model(inputs=input_layer, outputs=transpose_conv_layer)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f\"Test loss: {loss}\")\n",
    "print(f\"Test accuracy: {accuracy}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>\n",
    "<summary>Click here for solution</summary> </br>\n",
    "\n",
    "```python\n",
    "\n",
    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Input\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Define the input layer\n",
    "input_layer = Input(shape=(28, 28, 1))\n",
    "\n",
    "# Add convolutional and transpose convolutional layers with different activation functions\n",
    "conv_layer = Conv2D(filters=32, kernel_size=(3, 3), activation='tanh', padding='same')(input_layer)\n",
    "transpose_conv_layer = Conv2DTranspose(filters=1, kernel_size=(3, 3), activation='tanh', padding='same')(conv_layer)\n",
    "\n",
    "# Create the model\n",
    "model = Model(inputs=input_layer, outputs=transpose_conv_layer)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
    "\n",
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f'Test loss: {loss}')\n",
    "print(f'Test accuracy: {accuracy}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion:\n",
    "\n",
    "By completing this lab, you have successfully created, trained, and evaluated a simple neural network model using transpose convolution for image reconstruction. This exercise provided hands-on experience with Keras and practical applications of transpose convolution layers. Continue experimenting with different architectures and datasets to deepen your understanding and skills in deep learning with Keras. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © IBM Corporation. All rights reserved.\n"
   ]
  }
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