Misspelt tittle on plot | Update C3_W1_Lab_2_Transfer_Learning_CIFAR

Course 3 - Advance Computer Vision/W1/ungraded_labs/C3_W1_Lab_2_Transfer_Learning_CIFAR_10.ipynb

@@ -1 +1 @@
-{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"private_outputs":true,"provenance":[],"machine_shape":"hm"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.4"}},"cells":[{"cell_type":"markdown","metadata":{"id":"FStp_vbUkRz5"},"source":["\n","\n","\n","# Transfer Learning\n","In this notebook, you will perform transfer learning to train CIFAR-10 dataset on ResNet50 model available in Keras.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"qpiJj8ym0v0-"},"source":["## Imports"]},{"cell_type":"code","metadata":{"id":"AoilhmYe1b5t"},"source":["import os, re, time, json\n","import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n","import numpy as np\n","try:\n","  # %tensorflow_version only exists in Colab.\n","  %tensorflow_version 2.x\n","except Exception:\n","  pass\n","import tensorflow as tf\n","from tensorflow.keras.applications.resnet50 import ResNet50\n","from matplotlib import pyplot as plt\n","import tensorflow_datasets as tfds\n","\n","print(\"Tensorflow version \" + tf.__version__)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HuG_q_1jkaZ6"},"source":["## Parameters"]},{"cell_type":"markdown","metadata":{"id":"v4ocPhg6J_xw"},"source":["- Define the batch size\n","- Define the class (category) names"]},{"cell_type":"code","metadata":{"id":"cCpkS9C_H7Tl"},"source":["BATCH_SIZE = 32 \n","classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"O-o96NnyJ_xx"},"source":["Define some functions that will help you to create some visualizations. (These will be used later)"]},{"cell_type":"code","metadata":{"id":"CfFqJxrzoj5Q"},"source":["#@title Visualization Utilities[RUN ME]\n","#Matplotlib config\n","plt.rc('image', cmap='gray')\n","plt.rc('grid', linewidth=0)\n","plt.rc('xtick', top=False, bottom=False, labelsize='large')\n","plt.rc('ytick', left=False, right=False, labelsize='large')\n","plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n","plt.rc('text', color='a8151a')\n","plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n","MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n","# utility to display a row of digits with their predictions\n","def display_images(digits, predictions, labels, title):\n","\n","  n = 10\n","\n","  indexes = np.random.choice(len(predictions), size=n)\n","  n_digits = digits[indexes]\n","  n_predictions = predictions[indexes]\n","  n_predictions = n_predictions.reshape((n,))\n","  n_labels = labels[indexes]\n"," \n","  fig = plt.figure(figsize=(20, 4))\n","  plt.title(title)\n","  plt.yticks([])\n","  plt.xticks([])\n","\n","  for i in range(10):\n","    ax = fig.add_subplot(1, 10, i+1)\n","    class_index = n_predictions[i]\n","    \n","    plt.xlabel(classes[class_index])\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.imshow(n_digits[i])\n","\n","# utility to display training and validation curves\n","def plot_metrics(metric_name, title, ylim=5):\n","  plt.title(title)\n","  plt.ylim(0,ylim)\n","  plt.plot(history.history[metric_name],color='blue',label=metric_name)\n","  plt.plot(history.history['val_' + metric_name],color='green',label='val_' + metric_name)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wPq4Sw5akosT"},"source":["## Loading and Preprocessing Data\n","[CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset has 32 x 32 RGB images belonging to 10 classes. You will load the dataset from Keras."]},{"cell_type":"code","metadata":{"id":"E103YDdQ8NNq"},"source":["(training_images, training_labels) , (validation_images, validation_labels) = tf.keras.datasets.cifar10.load_data()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"prd944ThNavt"},"source":["### Visualize Dataset\n","\n","Use the `display_image` to view some of the images and their class labels."]},{"cell_type":"code","metadata":{"id":"UiokWTuKo88c"},"source":["display_images(training_images, training_labels, training_labels, \"Training Data\" )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-q35q41KNfxH"},"source":["display_images(validation_images, validation_labels, validation_labels, \"Training Data\" )"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["validation_images[0].astype('float32').shape"],"metadata":{"id":"6iIw-0ousl1C"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ltKfwrCVNuIu"},"source":["### Preprocess Dataset\n","Here, you'll perform normalization on images in training and validation set. \n","- You'll use the function [preprocess_input](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet50.py) from the ResNet50 model in Keras."]},{"cell_type":"code","metadata":{"id":"JIxdiJVKArC6"},"source":["def preprocess_image_input(input_images):\n","  input_images = input_images.astype('float32')\n","  output_ims = tf.keras.applications.resnet50.preprocess_input(input_images)\n","  return output_ims\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QOqjKzgAEU-Z"},"source":["train_X = preprocess_image_input(training_images)\n","valid_X = preprocess_image_input(validation_images)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2fooPL9Gkuox"},"source":["## Define the Network\n","You will be performing transfer learning on **ResNet50** available in Keras.\n","- You'll load pre-trained **imagenet weights** to the model.\n","- You'll choose to retain all layers of **ResNet50** along with the final classification layers."]},{"cell_type":"code","metadata":{"id":"56y8UNFQIVwj"},"source":["'''\n","Feature Extraction is performed by ResNet50 pretrained on imagenet weights. \n","Input size is 224 x 224.\n","'''\n","def feature_extractor(inputs):\n","\n","  feature_extractor = tf.keras.applications.resnet.ResNet50(input_shape=(224, 224, 3),\n","                                               include_top=False,\n","                                               weights='imagenet')(inputs)\n","  return feature_extractor\n","\n","\n","'''\n","Defines final dense layers and subsequent softmax layer for classification.\n","'''\n","def classifier(inputs):\n","    x = tf.keras.layers.GlobalAveragePooling2D()(inputs)\n","    x = tf.keras.layers.Flatten()(x)\n","    x = tf.keras.layers.Dense(1024, activation=\"relu\")(x)\n","    x = tf.keras.layers.Dense(512, activation=\"relu\")(x)\n","    x = tf.keras.layers.Dense(10, activation=\"softmax\", name=\"classification\")(x)\n","    return x\n","\n","'''\n","Since input image size is (32 x 32), first upsample the image by factor of (7x7) to transform it to (224 x 224)\n","Connect the feature extraction and \"classifier\" layers to build the model.\n","'''\n","def final_model(inputs):\n","\n","    resize = tf.keras.layers.UpSampling2D(size=(7,7))(inputs)\n","\n","    resnet_feature_extractor = feature_extractor(resize)\n","    classification_output = classifier(resnet_feature_extractor)\n","\n","    return classification_output\n","\n","'''\n","Define the model and compile it. \n","Use Stochastic Gradient Descent as the optimizer.\n","Use Sparse Categorical CrossEntropy as the loss function.\n","'''\n","def define_compile_model():\n","  inputs = tf.keras.layers.Input(shape=(32,32,3))\n","  \n","  classification_output = final_model(inputs) \n","  model = tf.keras.Model(inputs=inputs, outputs = classification_output)\n"," \n","  model.compile(optimizer='SGD', \n","                loss='sparse_categorical_crossentropy',\n","                metrics = ['accuracy'])\n","  \n","  return model\n","\n","\n","model = define_compile_model()\n","\n","model.summary()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CuhDh8ao8VyB"},"source":["## Train the model"]},{"cell_type":"code","metadata":{"id":"2K6RNDqtJ_xx"},"source":["# this will take around 20 minutes to complete\n","EPOCHS = 4\n","history = model.fit(train_X, training_labels, epochs=EPOCHS, validation_data = (valid_X, validation_labels), batch_size=64)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CYb5sAEmk4ut"},"source":["## Evaluate the Model\n","\n","Calculate the loss and accuracy metrics using the model's `.evaluate` function."]},{"cell_type":"code","metadata":{"id":"io7Fuu-w3PZi"},"source":["loss, accuracy = model.evaluate(valid_X, validation_labels, batch_size=64)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yml-phRfPeOj"},"source":["### Plot Loss and Accuracy Curves\n","\n","Plot the loss (in blue) and validation loss (in green)."]},{"cell_type":"code","metadata":{"id":"b1ZMMJ6T921A"},"source":["plot_metrics(\"loss\", \"Loss\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QbnWIbeJJ_xx"},"source":["Plot the training accuracy (blue) as well as the validation accuracy (green)."]},{"cell_type":"code","metadata":{"id":"P0YpFs3J99eO"},"source":["plot_metrics(\"accuracy\", \"Accuracy\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9jFVovcUUVs1"},"source":["### Visualize predictions\n","You can take a look at the predictions on the validation set."]},{"cell_type":"code","metadata":{"id":"NIQAqkMV9adq"},"source":["probabilities = model.predict(valid_X, batch_size=64)\n","probabilities = np.argmax(probabilities, axis = 1)\n","\n","display_images(validation_images, probabilities, validation_labels, \"Bad predictions indicated in red.\")"],"execution_count":null,"outputs":[]}]}
+{"nbformat":4,"nbformat_minor":0,"metadata":{"accelerator":"GPU","colab":{"private_outputs":true,"provenance":[],"machine_shape":"hm"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.4"}},"cells":[{"cell_type":"markdown","metadata":{"id":"FStp_vbUkRz5"},"source":["\n","\n","\n","# Transfer Learning\n","In this notebook, you will perform transfer learning to train CIFAR-10 dataset on ResNet50 model available in Keras.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"qpiJj8ym0v0-"},"source":["## Imports"]},{"cell_type":"code","metadata":{"id":"AoilhmYe1b5t"},"source":["import os, re, time, json\n","import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n","import numpy as np\n","try:\n","  # %tensorflow_version only exists in Colab.\n","  %tensorflow_version 2.x\n","except Exception:\n","  pass\n","import tensorflow as tf\n","from tensorflow.keras.applications.resnet50 import ResNet50\n","from matplotlib import pyplot as plt\n","import tensorflow_datasets as tfds\n","\n","print(\"Tensorflow version \" + tf.__version__)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"HuG_q_1jkaZ6"},"source":["## Parameters"]},{"cell_type":"markdown","metadata":{"id":"v4ocPhg6J_xw"},"source":["- Define the batch size\n","- Define the class (category) names"]},{"cell_type":"code","metadata":{"id":"cCpkS9C_H7Tl"},"source":["BATCH_SIZE = 32 \n","classes = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"O-o96NnyJ_xx"},"source":["Define some functions that will help you to create some visualizations. (These will be used later)"]},{"cell_type":"code","metadata":{"id":"CfFqJxrzoj5Q"},"source":["#@title Visualization Utilities[RUN ME]\n","#Matplotlib config\n","plt.rc('image', cmap='gray')\n","plt.rc('grid', linewidth=0)\n","plt.rc('xtick', top=False, bottom=False, labelsize='large')\n","plt.rc('ytick', left=False, right=False, labelsize='large')\n","plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n","plt.rc('text', color='a8151a')\n","plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n","MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n","# utility to display a row of digits with their predictions\n","def display_images(digits, predictions, labels, title):\n","\n","  n = 10\n","\n","  indexes = np.random.choice(len(predictions), size=n)\n","  n_digits = digits[indexes]\n","  n_predictions = predictions[indexes]\n","  n_predictions = n_predictions.reshape((n,))\n","  n_labels = labels[indexes]\n"," \n","  fig = plt.figure(figsize=(20, 4))\n","  plt.title(title)\n","  plt.yticks([])\n","  plt.xticks([])\n","\n","  for i in range(10):\n","    ax = fig.add_subplot(1, 10, i+1)\n","    class_index = n_predictions[i]\n","    \n","    plt.xlabel(classes[class_index])\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.imshow(n_digits[i])\n","\n","# utility to display training and validation curves\n","def plot_metrics(metric_name, title, ylim=5):\n","  plt.title(title)\n","  plt.ylim(0,ylim)\n","  plt.plot(history.history[metric_name],color='blue',label=metric_name)\n","  plt.plot(history.history['val_' + metric_name],color='green',label='val_' + metric_name)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"wPq4Sw5akosT"},"source":["## Loading and Preprocessing Data\n","[CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset has 32 x 32 RGB images belonging to 10 classes. You will load the dataset from Keras."]},{"cell_type":"code","metadata":{"id":"E103YDdQ8NNq"},"source":["(training_images, training_labels) , (validation_images, validation_labels) = tf.keras.datasets.cifar10.load_data()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"prd944ThNavt"},"source":["### Visualize Dataset\n","\n","Use the `display_image` to view some of the images and their class labels."]},{"cell_type":"code","metadata":{"id":"UiokWTuKo88c"},"source":["display_images(training_images, training_labels, training_labels, \"Training Data\" )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-q35q41KNfxH"},"source":["display_images(validation_images, validation_labels, validation_labels, \"Validation Data\" )"],"execution_count":null,"outputs":[]},{"cell_type":"code","source":["validation_images[0].astype('float32').shape"],"metadata":{"id":"6iIw-0ousl1C"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ltKfwrCVNuIu"},"source":["### Preprocess Dataset\n","Here, you'll perform normalization on images in training and validation set. \n","- You'll use the function [preprocess_input](https://github.com/keras-team/keras-applications/blob/master/keras_applications/resnet50.py) from the ResNet50 model in Keras."]},{"cell_type":"code","metadata":{"id":"JIxdiJVKArC6"},"source":["def preprocess_image_input(input_images):\n","  input_images = input_images.astype('float32')\n","  output_ims = tf.keras.applications.resnet50.preprocess_input(input_images)\n","  return output_ims\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QOqjKzgAEU-Z"},"source":["train_X = preprocess_image_input(training_images)\n","valid_X = preprocess_image_input(validation_images)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2fooPL9Gkuox"},"source":["## Define the Network\n","You will be performing transfer learning on **ResNet50** available in Keras.\n","- You'll load pre-trained **imagenet weights** to the model.\n","- You'll choose to retain all layers of **ResNet50** along with the final classification layers."]},{"cell_type":"code","metadata":{"id":"56y8UNFQIVwj"},"source":["'''\n","Feature Extraction is performed by ResNet50 pretrained on imagenet weights. \n","Input size is 224 x 224.\n","'''\n","def feature_extractor(inputs):\n","\n","  feature_extractor = tf.keras.applications.resnet.ResNet50(input_shape=(224, 224, 3),\n","                                               include_top=False,\n","                                               weights='imagenet')(inputs)\n","  return feature_extractor\n","\n","\n","'''\n","Defines final dense layers and subsequent softmax layer for classification.\n","'''\n","def classifier(inputs):\n","    x = tf.keras.layers.GlobalAveragePooling2D()(inputs)\n","    x = tf.keras.layers.Flatten()(x)\n","    x = tf.keras.layers.Dense(1024, activation=\"relu\")(x)\n","    x = tf.keras.layers.Dense(512, activation=\"relu\")(x)\n","    x = tf.keras.layers.Dense(10, activation=\"softmax\", name=\"classification\")(x)\n","    return x\n","\n","'''\n","Since input image size is (32 x 32), first upsample the image by factor of (7x7) to transform it to (224 x 224)\n","Connect the feature extraction and \"classifier\" layers to build the model.\n","'''\n","def final_model(inputs):\n","\n","    resize = tf.keras.layers.UpSampling2D(size=(7,7))(inputs)\n","\n","    resnet_feature_extractor = feature_extractor(resize)\n","    classification_output = classifier(resnet_feature_extractor)\n","\n","    return classification_output\n","\n","'''\n","Define the model and compile it. \n","Use Stochastic Gradient Descent as the optimizer.\n","Use Sparse Categorical CrossEntropy as the loss function.\n","'''\n","def define_compile_model():\n","  inputs = tf.keras.layers.Input(shape=(32,32,3))\n","  \n","  classification_output = final_model(inputs) \n","  model = tf.keras.Model(inputs=inputs, outputs = classification_output)\n"," \n","  model.compile(optimizer='SGD', \n","                loss='sparse_categorical_crossentropy',\n","                metrics = ['accuracy'])\n","  \n","  return model\n","\n","\n","model = define_compile_model()\n","\n","model.summary()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CuhDh8ao8VyB"},"source":["## Train the model"]},{"cell_type":"code","metadata":{"id":"2K6RNDqtJ_xx"},"source":["# this will take around 20 minutes to complete\n","EPOCHS = 4\n","history = model.fit(train_X, training_labels, epochs=EPOCHS, validation_data = (valid_X, validation_labels), batch_size=64)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CYb5sAEmk4ut"},"source":["## Evaluate the Model\n","\n","Calculate the loss and accuracy metrics using the model's `.evaluate` function."]},{"cell_type":"code","metadata":{"id":"io7Fuu-w3PZi"},"source":["loss, accuracy = model.evaluate(valid_X, validation_labels, batch_size=64)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yml-phRfPeOj"},"source":["### Plot Loss and Accuracy Curves\n","\n","Plot the loss (in blue) and validation loss (in green)."]},{"cell_type":"code","metadata":{"id":"b1ZMMJ6T921A"},"source":["plot_metrics(\"loss\", \"Loss\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"QbnWIbeJJ_xx"},"source":["Plot the training accuracy (blue) as well as the validation accuracy (green)."]},{"cell_type":"code","metadata":{"id":"P0YpFs3J99eO"},"source":["plot_metrics(\"accuracy\", \"Accuracy\")"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"9jFVovcUUVs1"},"source":["### Visualize predictions\n","You can take a look at the predictions on the validation set."]},{"cell_type":"code","metadata":{"id":"NIQAqkMV9adq"},"source":["probabilities = model.predict(valid_X, batch_size=64)\n","probabilities = np.argmax(probabilities, axis = 1)\n","\n","display_images(validation_images, probabilities, validation_labels, \"Bad predictions indicated in red.\")"],"execution_count":null,"outputs":[]}]}