{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Context\n\nThere are three essential computer vision tasks you can do with deep learning:\n- Image classification\n- Image segmentation\n- Object detection.\n\nHere is an example of Binary Image classification (Cat or Dog)\n\n<img src=\"https://camo.githubusercontent.com/f0fe57a3540c293e21dcadff0cf5dedf3aaaea16509613822ee713fd40228a91/68747470733a2f2f6d69726f2e6d656469756d2e636f6d2f6d61782f313833382f312a6f4233533579484868766f75674a6b50587563386f672e676966\" width=800>\n\n###### Credits: [ReiCHU31](https://github.com/ReiCHU31/Cat-Dog-Classification-Flask-App)\n\n**[Keras](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjgzp3J5eT8AhUQUcAKHZpcCXUQFnoECBAQAQ&url=https%3A%2F%2Fkeras.io%2F&usg=AOvVaw202JaS4UanG9bHmxJAu6V_)** is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.","metadata":{}},{"cell_type":"code","source":"# Reproducability\nseed_value = 10\n\nimport os\nos.environ['PYTHONHASHSEED'] = str(seed_value)\n\nimport random\nrandom.seed(seed_value)\n\nimport numpy as np\nnp.random.seed(seed_value)\n\nimport tensorflow as tf\ntf.random.set_seed(seed_value)","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:18:49.551930Z","iopub.execute_input":"2023-01-29T07:18:49.552340Z","iopub.status.idle":"2023-01-29T07:18:53.842890Z","shell.execute_reply.started":"2023-01-29T07:18:49.552252Z","shell.execute_reply":"2023-01-29T07:18:53.841946Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Necessary imports\nimport pandas as pd\nimport matplotlib.pyplot as plt \nplt.style.use(\"ggplot\")\nimport seaborn as sns\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-01-29T07:18:53.844804Z","iopub.execute_input":"2023-01-29T07:18:53.845422Z","iopub.status.idle":"2023-01-29T07:18:54.212857Z","shell.execute_reply.started":"2023-01-29T07:18:53.845367Z","shell.execute_reply":"2023-01-29T07:18:54.211814Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# unzip folders\n!unzip /kaggle/input/dogs-vs-cats/train.zip\n!unzip /kaggle/input/dogs-vs-cats/test1.zip","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-01-29T07:18:54.217288Z","iopub.execute_input":"2023-01-29T07:18:54.219847Z","iopub.status.idle":"2023-01-29T07:19:13.607128Z","shell.execute_reply.started":"2023-01-29T07:18:54.219811Z","shell.execute_reply":"2023-01-29T07:19:13.605983Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Reading the Images\n\n### Read Train Images","metadata":{}},{"cell_type":"code","source":"from glob import glob\n\nfile_names = glob('./train/*.jpg')\ncategories = [1 if 'dog' in pic else 0 for pic in os.listdir(\"./train\")]\n\ndf = pd.DataFrame({'filename': file_names, 'category':categories})\ndf[\"category\"] = df[\"category\"].replace({0: 'cat', 1: 'dog'})\n\nprint(\"shape:\", df.shape)\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:19:13.609604Z","iopub.execute_input":"2023-01-29T07:19:13.610590Z","iopub.status.idle":"2023-01-29T07:19:13.723805Z","shell.execute_reply.started":"2023-01-29T07:19:13.610545Z","shell.execute_reply":"2023-01-29T07:19:13.722813Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Split train/valid dataframes\n\nto train the model on the `train_df` & evaluate it on the `validate_df`.","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\ntrain_df, validate_df = train_test_split(df, test_size=0.2, random_state=10)\ntrain_df = train_df.reset_index(drop=True)\nvalidate_df = validate_df.reset_index(drop=True)\n\n# print shape\ntrain_df.shape, validate_df.shape","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:19:26.309026Z","iopub.execute_input":"2023-01-29T07:19:26.309567Z","iopub.status.idle":"2023-01-29T07:19:26.658095Z","shell.execute_reply.started":"2023-01-29T07:19:26.309517Z","shell.execute_reply":"2023-01-29T07:19:26.657234Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot label counts\nfig, ax = plt.subplots(nrows=1, ncols=2, figsize=(10,5))\n\nsns.countplot(train_df.category, ax=ax[0])\nax[0].set_title('Training dataset', fontsize=14)\n\nsns.countplot(validate_df.category, ax=ax[1])\nax[1].set_title('Valiadtion dataset', fontsize=14);","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:19:26.662363Z","iopub.execute_input":"2023-01-29T07:19:26.664517Z","iopub.status.idle":"2023-01-29T07:19:27.023419Z","shell.execute_reply.started":"2023-01-29T07:19:26.664482Z","shell.execute_reply":"2023-01-29T07:19:27.022373Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Great, We have a balanced dataset ✅","metadata":{}},{"cell_type":"markdown","source":"### Read Test Images","metadata":{}},{"cell_type":"code","source":"file_names = os.listdir(\"./test1\")\n\ntest_df = pd.DataFrame({'filename': file_names})\n\nprint(\"shape:\", test_df.shape)\ntest_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:19:29.889620Z","iopub.execute_input":"2023-01-29T07:19:29.889984Z","iopub.status.idle":"2023-01-29T07:19:29.913730Z","shell.execute_reply.started":"2023-01-29T07:19:29.889954Z","shell.execute_reply":"2023-01-29T07:19:29.912767Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Images Preprocessing","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nIMAGE_SIZE = (190,190)\nBATCH_SIZE = 32\n\ndatagen = ImageDataGenerator(rescale = 1/255)\n\ntrain_generator = datagen.flow_from_dataframe(\n    dataframe=train_df,\n#     directory='./Images/train', \n    x_col='filename',\n    y_col='category',\n    class_mode='binary',\n    target_size=IMAGE_SIZE,\n    batch_size=BATCH_SIZE\n)\n\nvalid_generator = datagen.flow_from_dataframe(\n    dataframe=validate_df,\n#     directory='./Images/train',\n    x_col='filename',\n    y_col='category',\n    class_mode='binary',\n    target_size=IMAGE_SIZE,\n    batch_size=BATCH_SIZE\n)\n\ntest_generator = datagen.flow_from_dataframe(\n    dataframe=test_df,\n    directory=\"./test1\",\n    x_col='filename',\n    y_col=None,\n    class_mode=None,\n    target_size=IMAGE_SIZE,\n    batch_size=BATCH_SIZE\n)","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:19:31.214699Z","iopub.execute_input":"2023-01-29T07:19:31.215069Z","iopub.status.idle":"2023-01-29T07:19:32.267216Z","shell.execute_reply.started":"2023-01-29T07:19:31.215038Z","shell.execute_reply":"2023-01-29T07:19:32.266280Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# get a batch of 32 training images \nimages = train_generator.next()[:9]\n\n# plot 9 original training images\nplt.figure(figsize=(10, 10))\nfor i in range(9):\n    plt.subplot(3, 3, i+1)\n    plt.imshow(images[0][i])\n    plt.axis('off')\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:19:33.064455Z","iopub.execute_input":"2023-01-29T07:19:33.065147Z","iopub.status.idle":"2023-01-29T07:19:33.818836Z","shell.execute_reply.started":"2023-01-29T07:19:33.065112Z","shell.execute_reply":"2023-01-29T07:19:33.817981Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Building the model\n\nDifferent ways to build **Keras** models:\n- `Sequential model`: it’s basically a Python list. the simplest one. [Beginners]\n- `Functional API`: a graph-like model architectures. a nice mid-point between usability and flexibility. the most commonly used model-building API.\n- `Model subclassing`: a low-level option where you write everything yourself from scratch. the most flexible. [Experts]\n\n\n![index.png](attachment:ab6c9405-5602-49fc-9b07-c5ad1b45e447.png)","metadata":{},"attachments":{"ab6c9405-5602-49fc-9b07-c5ad1b45e447.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"## Our model contains:\n\n- Convolutional Layer `Conv2D`:\n    - processes the input image using a set of convolutional filters.\n    - Each filter extracts a specific kind of feature, ex. a high-pass filter is often used to detect the edge of an object.\n     - he output of a given convolutional layer is a set of feature maps\n- Pooling layers `MaxPool2D`: used to reduce the dimensions of the feature maps.\n- Flatten:\n    - used to convert all the resultant 2D arrays from pooled feature maps into 1D a single long continuous linear vector (Dense).\n    - converting a matrix of image features into a feature vector whose dimensions are 1xC, where C is the number of classes (Fully-Connected Layer).\n- Dense: simple layer of neurons in which each neuron receives input from all the neurons of previous layer and here outputs (0, 1) because of the sigmoid activation function.\n- [Activation Function](https://keras.io/api/layers/activations/): `relu` & `sigmoid`\n    - is a node that is put at the end of or in between Neural Networks.\n    - is the non linear transformation that we do over the input signal.\n    - helps to calculate the output of the neuron.\n    \nWe will start with the `Sequential model`","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Input, Rescaling, Conv2D, MaxPool2D, Flatten, Dense\n\n# Design the model architecture\nmodel_1 = Sequential([\n    Input(shape=(190,190,3)),\n    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'),\n    MaxPool2D(pool_size=2),\n    Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'),\n    MaxPool2D(pool_size=2),\n    Conv2D(filters=128, kernel_size=3, padding='same', activation='relu'),\n    MaxPool2D(pool_size=2),\n    Conv2D(filters=265, kernel_size=3, padding='same', activation='relu'),\n    Flatten(),\n    Dense(1, activation='sigmoid')  # Cat or dog  \n])\n\nmodel_1.summary()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-01-29T07:19:37.869289Z","iopub.execute_input":"2023-01-29T07:19:37.869669Z","iopub.status.idle":"2023-01-29T07:19:40.432943Z","shell.execute_reply.started":"2023-01-29T07:19:37.869639Z","shell.execute_reply":"2023-01-29T07:19:40.431974Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Configuring the model for training\n\nTwo arguments required for compiling a model:\n- **Loss**:\n    - measure how far the predicted image by the model from the original image.\n    - There are many different [loss functions](https://keras.io/losses/) in Keras. \n    - here we use `binary_crossentropy` because we have (binary classififcation problem) 2 categories.\n- **Optimizer**:\n    - algorithm or function used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses.\n    - Keras has different [optimizers](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwikn66liuX8AhWiU6QEHTedAGkQFnoECA8QAQ&url=https%3A%2F%2Fkeras.io%2Fapi%2Foptimizers%2F&usg=AOvVaw0FAGroezYYk-tZ5VFEZDDa). but `Adam` is the most used one, so we will start with Adam.","metadata":{}},{"cell_type":"markdown","source":"<!-- ![optimizers.gif](attachment:046ff90c-ca8d-48bb-81b4-f892792bdc6c.gif) -->\n<img src='https://camo.githubusercontent.com/cccdc200cc2a3052ad9562f6f58ebd429d634fb1190a84b20db931e8ad4ffb8f/68747470733a2f2f692e737461636b2e696d6775722e636f6d2f316f6274562e676966'>\n\nsource: [Jaewan-Yun](https://github.com/Jaewan-Yun/optimizer-visualization)","metadata":{}},{"cell_type":"code","source":"# Compile model\nmodel_1.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:19:47.773707Z","iopub.execute_input":"2023-01-29T07:19:47.774258Z","iopub.status.idle":"2023-01-29T07:19:47.829323Z","shell.execute_reply.started":"2023-01-29T07:19:47.774214Z","shell.execute_reply":"2023-01-29T07:19:47.828407Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Fitting the model","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n\n# callbacks is a reguralization technique to brevent over fitting\ncallbacks = [\n    # to stop training when you measure that the validation loss is no longer improving\n    EarlyStopping(patience=4, monitor='val_loss'),\n    # reduce learning_rate if the model is not imporving\n    ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.5, min_lr=0.00001),\n    # save best model\n    ModelCheckpoint(filepath='./Models/model_1.keras', save_best_only=True, monitor='val_loss')\n]\n\n# train the model\nhistory_1 = model_1.fit(train_generator, validation_data=valid_generator, epochs=10, callbacks=[callbacks])","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:19:49.134851Z","iopub.execute_input":"2023-01-29T07:19:49.136027Z","iopub.status.idle":"2023-01-29T07:19:57.511603Z","shell.execute_reply.started":"2023-01-29T07:19:49.135967Z","shell.execute_reply":"2023-01-29T07:19:57.509972Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot model performance\npd.DataFrame(history_1.history).plot();","metadata":{"execution":{"iopub.status.busy":"2023-01-26T11:57:25.308268Z","iopub.execute_input":"2023-01-26T11:57:25.309013Z","iopub.status.idle":"2023-01-26T11:57:25.589092Z","shell.execute_reply.started":"2023-01-26T11:57:25.308976Z","shell.execute_reply":"2023-01-26T11:57:25.588073Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Wow, with a simple CNN on the original images we reached ~ 85% Accuracy after only 10 epochs. but the plot shows that the model crealy overfit the data (the `val_loss` starts to increase after a while).\n\n![overfitting.jpg](attachment:3a776269-bca7-4d75-ab87-320ffe86ba14.jpg)\n\nBut we can use a reguralization techniques -to prevent overfitting- such as:\n\n- Data Augmentation\n- [Layer weight regularizers](https://keras.io/api/layers/regularizers/)\n- [Layer weight constraints](https://keras.io/api/layers/constraints/)\n- [Dropout](https://keras.io/api/layers/regularization_layers/dropout/)\n- [Batch normalization](https://keras.io/api/layers/normalization_layers/batch_normalization/)\n- [EarlyStopping](https://keras.io/api/callbacks/early_stopping/)","metadata":{},"attachments":{"3a776269-bca7-4d75-ab87-320ffe86ba14.jpg":{"image/jpeg":"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"}}},{"cell_type":"markdown","source":"## Using data augmentation\n\nthere is two ways to Augment the images:\n- With Inputs: using `tensorflow.keras.preprocessing.image.ImageDataGenerator`. \n- Within model: using **Keras** layers:\n\n```python\nDataAugmentation = tensorflow.keras.layers.Sequential([\n    tensorflow.keras.layers.RandomFlip(\"horizontal\"),\n    tensorflow.keras.layers.RandomRotation(0.2),\n    tensorflow.keras.layers.RandomZoom(0.2),\n    tensorflow.keras.layers.RandomContrast(0.1)\n])\n\n# use it within the model\nx = DataAugmentation( Input(shape=(190,190,3)) )\n```\n\nHere we will use the `ImageDataGenerator`: ","metadata":{}},{"cell_type":"code","source":"# we will change only the train images\ntrain_datagen = ImageDataGenerator(rescale = 1/255,\n                                   zoom_range=0.2,\n                                   rotation_range=0.2,\n                                   shear_range=0.2,\n                                   brightness_range=(0.7, 1),\n                                   horizontal_flip=True,\n                                   fill_mode='nearest',\n                                   width_shift_range=0.1, \n                                   height_shift_range=0.1)\n\ntrain_generator = train_datagen.flow_from_dataframe(\n    dataframe=train_df,\n    x_col='filename',\n    y_col='category',\n    class_mode='binary',\n    target_size=IMAGE_SIZE,\n    batch_size=BATCH_SIZE\n)","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:20:02.636542Z","iopub.execute_input":"2023-01-29T07:20:02.637142Z","iopub.status.idle":"2023-01-29T07:20:02.929932Z","shell.execute_reply.started":"2023-01-29T07:20:02.637093Z","shell.execute_reply":"2023-01-29T07:20:02.928954Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# get a batch of 32 Augmented training images \nimages = train_generator.next()[:9]\n\n# plot 9 Augmented training images\nplt.figure(figsize=(10, 10))\nfor i in range(9):\n    plt.subplot(3, 3, i+1)\n    plt.imshow(images[0][i])\n    plt.axis('off')\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:20:03.280988Z","iopub.execute_input":"2023-01-29T07:20:03.281437Z","iopub.status.idle":"2023-01-29T07:20:04.456104Z","shell.execute_reply.started":"2023-01-29T07:20:03.281383Z","shell.execute_reply":"2023-01-29T07:20:04.455291Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### This time let's use the `Functional API`:","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import Dropout, GlobalAveragePooling2D\n\n# Create model\ninputs = Input(shape=(190,190,3))\nx = Conv2D(filters=32, kernel_size=3, padding='same', activation='relu')(inputs)\nx = MaxPool2D(pool_size=2)(x)\nx = Conv2D(filters=64, kernel_size=3, padding='same', activation='relu')(x)\nx = MaxPool2D(pool_size=2)(x)\nx = Conv2D(filters=128, kernel_size=3, padding='same', activation='relu')(x)\nx = MaxPool2D(pool_size=2)(x)\nx = Conv2D(filters=265, kernel_size=3, padding='same', activation='relu')(x)\nx = GlobalAveragePooling2D()(x)  # Flatten\nx = Dropout(0.1)(x)\noutputs = Dense(1, activation='sigmoid')(x)\nmodel_2 = Model(inputs=inputs, outputs=outputs)\n\n# Compile model\nmodel_2.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n\n# fit model\nmodel_2.summary()","metadata":{"_kg_hide-output":false,"execution":{"iopub.status.busy":"2023-01-29T07:20:05.348722Z","iopub.execute_input":"2023-01-29T07:20:05.349912Z","iopub.status.idle":"2023-01-29T07:20:05.413622Z","shell.execute_reply.started":"2023-01-29T07:20:05.349862Z","shell.execute_reply":"2023-01-29T07:20:05.412618Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"After using `GlobalAveragePooling2D` the model have a fewer parameters.","metadata":{}},{"cell_type":"code","source":"# callbacks is a reguralization technique to brevent over fitting\ncallbacks = [\n    EarlyStopping(patience=4),\n    ModelCheckpoint(filepath='./Models/model_2.keras', save_best_only=True, monitor='val_loss')\n]\n\n# train the model\nhistory_2 = model_2.fit(train_generator, validation_data=valid_generator, epochs=10, callbacks=[callbacks])","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-01-29T07:20:19.011080Z","iopub.execute_input":"2023-01-29T07:20:19.011447Z","iopub.status.idle":"2023-01-29T07:58:19.830752Z","shell.execute_reply.started":"2023-01-29T07:20:19.011418Z","shell.execute_reply":"2023-01-29T07:58:19.829783Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot model performance\npd.DataFrame(history_2.history).plot();","metadata":{"execution":{"iopub.status.busy":"2023-01-29T07:58:19.832763Z","iopub.execute_input":"2023-01-29T07:58:19.833108Z","iopub.status.idle":"2023-01-29T07:58:20.071325Z","shell.execute_reply.started":"2023-01-29T07:58:19.833073Z","shell.execute_reply":"2023-01-29T07:58:20.070486Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The model is doing very well 👏, if we fitted if with more epochs we will get a very descent accuracy. but instead of creating a model from scratch we can use a pre-trained model for a state-of-the-art accuracy.\n\n# Using a [pretrained model](https://keras.io/api/applications/)\n\nThere is two ways to use a pretrained model:\n1. Feature extraction\n2. Fine-tuning","metadata":{}},{"cell_type":"markdown","source":"Here we will use `VGG19` Model, but feel free to use any other Model, the process will be the same.\n\n![vgg.png](attachment:304464ea-fc61-4cc1-be72-d750f39c56ed.png)\n\n<!-- ![vgg 2.jpg](attachment:9f215fcc-7778-461d-821c-e9761999ccf4.jpg) 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"}}},{"cell_type":"markdown","source":"## 1. Feature Extraction:\n> We use the hidden states as features and just train a classifier on them, without modifying\nthe pretrained model.  \n> we can use image with/without data augmentation to feed it to the model as usal.\n\n**Note that**: Every pretrained model requires inputs to be preprocessed via Keras, Here: `tensorflow.keras.applications.vgg19.preprocess_input`","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.applications.vgg19 import VGG19\n\n# read the pretrained VGG19\nvgg = VGG19(include_top=False, weights='imagenet', input_shape=(190, 190, 3))\n\nvgg.summary()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-01-28T12:21:19.563570Z","iopub.execute_input":"2023-01-28T12:21:19.563923Z","iopub.status.idle":"2023-01-28T12:21:19.895444Z","shell.execute_reply.started":"2023-01-28T12:21:19.563892Z","shell.execute_reply":"2023-01-28T12:21:19.894504Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Freezing all layers\nvgg.trainable = False\n\nprint(\"This is the number of trainable weights of the VGG:\", len(vgg.trainable_weights))","metadata":{"execution":{"iopub.status.busy":"2023-01-28T11:15:30.453627Z","iopub.execute_input":"2023-01-28T11:15:30.455512Z","iopub.status.idle":"2023-01-28T11:15:30.463138Z","shell.execute_reply.started":"2023-01-28T11:15:30.455462Z","shell.execute_reply":"2023-01-28T11:15:30.462253Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create model using VGG\nx = vgg.output\nx = GlobalAveragePooling2D()(x)\nx = Dropout(0.3)(x)\noutputs = Dense(1, activation='sigmoid')(x)\nmodel_3 = Model(vgg.input, outputs)\n\n# Compile model\nmodel_3.compile(loss=\"binary_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n\n# Fit model\nmodel_3.summary()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-01-28T11:15:31.441673Z","iopub.execute_input":"2023-01-28T11:15:31.442034Z","iopub.status.idle":"2023-01-28T11:15:31.474387Z","shell.execute_reply.started":"2023-01-28T11:15:31.442003Z","shell.execute_reply":"2023-01-28T11:15:31.473369Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# callbacks is a reguralization technique to brevent over fitting\ncallbacks = [\n    EarlyStopping(patience=4),\n    ModelCheckpoint(filepath='./Models/model_3.keras', save_best_only=True, monitor='val_loss')\n]\n\n# train the model\nhistory_3 = model_3.fit(train_generator, validation_data=valid_generator, epochs=10, callbacks=[callbacks])","metadata":{"execution":{"iopub.status.busy":"2023-01-28T11:15:32.432499Z","iopub.execute_input":"2023-01-28T11:15:32.433602Z","iopub.status.idle":"2023-01-28T11:55:33.864072Z","shell.execute_reply.started":"2023-01-28T11:15:32.433560Z","shell.execute_reply":"2023-01-28T11:55:33.863023Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot model performance\npd.DataFrame(history_3.history).plot();","metadata":{"execution":{"iopub.status.busy":"2023-01-28T12:06:13.546510Z","iopub.execute_input":"2023-01-28T12:06:13.546873Z","iopub.status.idle":"2023-01-28T12:06:13.792500Z","shell.execute_reply.started":"2023-01-28T12:06:13.546840Z","shell.execute_reply":"2023-01-28T12:06:13.791527Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We can see that the model is better than the previous 2 from scratch models with just the hidden states of the VGG (pretrained model). \n\n**Note That:**\n- ImageNet -that VGG was trained on: `weights='imagenet'`- contains pictures of cats & dogs. you many not get the similar results with another data.\n- As usual, you can use the Original or the Augmented images as model inputs.","metadata":{}},{"cell_type":"markdown","source":"## 2. Fine-tuning a pretrained model\n> We train the whole model (or it's top layers) end-to-end, which also updates the parameters of the pretrained\nmodel.","metadata":{}},{"cell_type":"code","source":"# Freezing all layers until the last 5 layers (block_5 layers)\nvgg.trainable = True\nfor layer in vgg.layers[:-5]:\n    layer.trainable = False\n    \nprint(\"This is the number of trainable weights of the VGG:\", len(vgg.trainable_weights))","metadata":{"execution":{"iopub.status.busy":"2023-01-28T12:21:22.675869Z","iopub.execute_input":"2023-01-28T12:21:22.676919Z","iopub.status.idle":"2023-01-28T12:21:22.683980Z","shell.execute_reply.started":"2023-01-28T12:21:22.676876Z","shell.execute_reply":"2023-01-28T12:21:22.682975Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.optimizers import SGD, Adadelta   # let's try different optimizer\n\n# Create model using VGG\nx = vgg.output\nx = Flatten()(x)\nx = Dropout(0.3)(x)\noutputs = Dense(1, activation='sigmoid')(x)\nmodel_4 = Model(vgg.input, outputs)\n\n# Compile model\nmodel_4.compile(loss=\"binary_crossentropy\", metrics=[\"accuracy\"],\n                optimizer=SGD(lr=0.001))\n\n# Fit model\nmodel_4.summary()","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-01-28T12:21:24.264378Z","iopub.execute_input":"2023-01-28T12:21:24.264734Z","iopub.status.idle":"2023-01-28T12:21:24.295506Z","shell.execute_reply.started":"2023-01-28T12:21:24.264703Z","shell.execute_reply":"2023-01-28T12:21:24.294399Z"},"collapsed":true,"jupyter":{"outputs_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now we have around 9,000,000 (9 millions) more trainable parameters than the Feature Extraction model","metadata":{}},{"cell_type":"code","source":"# callbacks is a reguralization technique to brevent over fitting\ncallbacks = [\n    EarlyStopping(patience=4),\n    ReduceLROnPlateau(monitor='val_acc', patience=2, verbose=1, factor=0.5, min_lr=0.00001),\n    ModelCheckpoint(filepath='./Models/model_4.keras', save_best_only=True, monitor='val_loss')\n]\n\n# train the model\nhistory_4 = model_4.fit(train_generator, validation_data=valid_generator, epochs=10, callbacks=[callbacks])","metadata":{"execution":{"iopub.status.busy":"2023-01-28T12:21:26.771072Z","iopub.execute_input":"2023-01-28T12:21:26.771471Z","iopub.status.idle":"2023-01-28T13:01:27.422036Z","shell.execute_reply.started":"2023-01-28T12:21:26.771438Z","shell.execute_reply":"2023-01-28T13:01:27.420898Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plot model performance\npd.DataFrame(history_4.history).plot();","metadata":{"execution":{"iopub.status.busy":"2023-01-28T13:30:39.393640Z","iopub.execute_input":"2023-01-28T13:30:39.394003Z","iopub.status.idle":"2023-01-28T13:30:39.641379Z","shell.execute_reply.started":"2023-01-28T13:30:39.393973Z","shell.execute_reply":"2023-01-28T13:30:39.640488Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Wow, we got around 96% accuracy 👏\n\nTo get a better results, try to:\n- Increase epochs numbers.\n- Try different optimizer.\n- Use a different pretrained model.","metadata":{}},{"cell_type":"markdown","source":"## Predict Test Images","metadata":{}},{"cell_type":"code","source":"# The model return class probabilites\ny_pred = model_4.predict(test_generator)\ny_pred","metadata":{"execution":{"iopub.status.busy":"2023-01-26T13:50:23.869073Z","iopub.execute_input":"2023-01-26T13:50:23.869512Z","iopub.status.idle":"2023-01-26T13:51:00.458279Z","shell.execute_reply.started":"2023-01-26T13:50:23.869483Z","shell.execute_reply":"2023-01-26T13:51:00.457213Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"remebmer that we used the `sigmid` function for the output `Dense` layer.\n\nso we differentiate between the 2 classes by checking wether the class probability is greater or smaller than 0.5 (dash line in the figure below).","metadata":{}},{"cell_type":"markdown","source":"\n![segmoid22.png](attachment:766aadf4-756b-4886-9789-690aaf11b095.png)\n\nRemember that the cat class is 0 & dog is 1","metadata":{},"attachments":{"766aadf4-756b-4886-9789-690aaf11b095.png":{"image/png":"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"}}},{"cell_type":"code","source":"# assign the predictions for the test_df \ntest_df['predictions'] = [\"Cat\" if y < 0.5 else \"Dog\" for y in y_pred]\ntest_df['predictions'].value_counts().plot(kind='bar', title='Model Predictions');","metadata":{"execution":{"iopub.status.busy":"2023-01-26T13:51:00.459777Z","iopub.execute_input":"2023-01-26T13:51:00.460127Z","iopub.status.idle":"2023-01-26T13:51:00.662032Z","shell.execute_reply.started":"2023-01-26T13:51:00.460092Z","shell.execute_reply":"2023-01-26T13:51:00.661033Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**For any suggestions, please let me know in the comments! Thanks.**\n\n**Happy Learning 🤗**","metadata":{}}]}