{"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":"# **Import Libraries**","metadata":{}},{"cell_type":"code","source":"# For Preparing Data\nimport os\nfrom zipfile import ZipFile\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom keras.preprocessing.image import ImageDataGenerator, load_img\nimport random\n\n# For Building Model\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, BatchNormalization, MaxPooling2D, Dropout, Flatten, Dense\n\n# For Callbacks\nfrom tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n\n# For Prepare Training & Validating data\nfrom sklearn.model_selection import train_test_split\n\n# For Data Augmentation\nfrom keras.preprocessing.image import ImageDataGenerator\n\nimport numpy as np\n\n# For transfer learning\nfrom keras import applications\nfrom keras.models import Model\nfrom keras.layers import Input, GlobalAveragePooling2D, Dense, Dropout","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:42:04.204146Z","iopub.execute_input":"2022-04-09T21:42:04.204837Z","iopub.status.idle":"2022-04-09T21:42:10.633744Z","shell.execute_reply.started":"2022-04-09T21:42:04.204798Z","shell.execute_reply":"2022-04-09T21:42:10.632901Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# **Define Constants**","metadata":{}},{"cell_type":"code","source":"# For Preparing Data\nDATA_DIR = \"../data\"\nTRAIN_DIR = \"../data/train/\"\nTEST_DIR = \"../data/test1/\"\n\nZIP_TRAIN_DIR = \"../input/dogs-vs-cats/train.zip\"\nZIP_TEST_DIR = \"../input/dogs-vs-cats/test1.zip\"\n\n# For Building Model\nIMAGE_WIDTH = 128\nIMAGE_HEIGHT = 128\nIMAGE_CHANNELS = 3\n\ninput_shape = (IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNELS)\n# For Fitting Model\nFAST_RUN = True","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:42:10.635236Z","iopub.execute_input":"2022-04-09T21:42:10.635497Z","iopub.status.idle":"2022-04-09T21:42:10.643011Z","shell.execute_reply.started":"2022-04-09T21:42:10.635464Z","shell.execute_reply":"2022-04-09T21:42:10.641687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 1. Categorize Input Data","metadata":{}},{"cell_type":"markdown","source":"Create a directory to unzip the training & testing data both in different directories. But first check if that directory already exists\n\nThen unzip the data into its directory","metadata":{}},{"cell_type":"code","source":"# check if the directories already exist then dont make them\nif not os.path.exists(DATA_DIR):\n    os.mkdir(DATA_DIR)\n    \n    with ZipFile(ZIP_TRAIN_DIR) as zf:\n        zf.extractall(DATA_DIR)\n    \n    with ZipFile(ZIP_TEST_DIR) as zf:\n        zf.extractall(DATA_DIR)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:42:16.349093Z","iopub.execute_input":"2022-04-09T21:42:16.349367Z","iopub.status.idle":"2022-04-09T21:42:33.823912Z","shell.execute_reply.started":"2022-04-09T21:42:16.349338Z","shell.execute_reply":"2022-04-09T21:42:33.823092Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Have a look inside the folders we created","metadata":{}},{"cell_type":"code","source":"for dirname, _, filenames in os.walk(DATA_DIR):\n    for index, filename in enumerate(filenames):\n        if index < 3:\n            print(os.path.join(dirname, filename))\n            \n        else:\n            print(\"** **\")\n            break","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:15:18.200677Z","iopub.execute_input":"2022-03-23T17:15:18.200993Z","iopub.status.idle":"2022-03-23T17:15:18.265205Z","shell.execute_reply.started":"2022-03-23T17:15:18.200957Z","shell.execute_reply":"2022-03-23T17:15:18.264473Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now, let's prepare our training data into data frame\n\nGiving a dog value of 1 and a cat value of 0","metadata":{}},{"cell_type":"code","source":"train_files = os.listdir(TRAIN_DIR)\ncategories = []\n\nfor file in train_files:\n    category = file.split('.')[0]\n    \n    if category == \"dog\":\n        categories.append(1)\n    else:\n        categories.append(0)\n        \ndf = pd.DataFrame({\n    'filename': train_files,\n    'category': categories\n})","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:42:57.194846Z","iopub.execute_input":"2022-04-09T21:42:57.19511Z","iopub.status.idle":"2022-04-09T21:42:57.239Z","shell.execute_reply.started":"2022-04-09T21:42:57.195082Z","shell.execute_reply":"2022-04-09T21:42:57.238341Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Have a look of the data frame","metadata":{}},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:15:28.487334Z","iopub.execute_input":"2022-03-23T17:15:28.487642Z","iopub.status.idle":"2022-03-23T17:15:28.509456Z","shell.execute_reply.started":"2022-03-23T17:15:28.487611Z","shell.execute_reply":"2022-03-23T17:15:28.508414Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"see the data charts of data frames","metadata":{}},{"cell_type":"code","source":"df['category'].value_counts().plot.bar()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:15:33.252393Z","iopub.execute_input":"2022-03-23T17:15:33.25272Z","iopub.status.idle":"2022-03-23T17:15:33.496664Z","shell.execute_reply.started":"2022-03-23T17:15:33.252682Z","shell.execute_reply":"2022-03-23T17:15:33.49574Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"see a sample image","metadata":{}},{"cell_type":"code","source":"# using PIL\nimg_path = os.path.join(TRAIN_DIR, random.choice(train_files))\nimg = Image.open(os.path.join(img_path))\ndisplay(img)\n\n# using matplot\n# img = load_img(img_path)\n# plt.imshow(img)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:43:08.248791Z","iopub.execute_input":"2022-04-09T21:43:08.249053Z","iopub.status.idle":"2022-04-09T21:43:08.365362Z","shell.execute_reply.started":"2022-04-09T21:43:08.249024Z","shell.execute_reply":"2022-04-09T21:43:08.364705Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 2. Build Model","metadata":{}},{"cell_type":"markdown","source":"![model 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AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB//9k="}}},{"cell_type":"markdown","source":"Input Layer: It represent input image data. It will reshape image into single diminsion array. Example your image is 64x64 = 4096, it will convert to (4096,1) array.\n\nConv Layer: This layer will extract features from image.\n\nPooling Layer: This layerreduce the spatial volume of input image after convolution.\n\nFully Connected Layer: It connect the network from a layer to another layer.\n\nOutput Layer: It is the predicted values layer.","metadata":{}},{"cell_type":"code","source":"def add_FE_layer(model, num_filters, kernel_size, _activation, _input_shape, _pool_size, drop_rate):\n    if _input_shape is None:\n        model.add(Conv2D(num_filters, kernel_size, activation=_activation))\n    else:\n        model.add(Conv2D(num_filters, kernel_size, activation=_activation, input_shape=_input_shape))\n        \n    model.add(BatchNormalization())\n    model.add(MaxPooling2D(pool_size=_pool_size))\n    model.add(Dropout(drop_rate))\n    return model\n\nmodel = Sequential()\nmodel = add_FE_layer(model, 32, (3, 3), 'relu', input_shape, (2, 2), 0.25)\nmodel = add_FE_layer(model, 64, (3, 3), 'relu', None, (2, 2), 0.25)\nmodel = add_FE_layer(model, 128, (3, 3), 'relu', None, (2, 2), 0.25)\n\nmodel.add(Flatten())\nmodel.add(Dense(units=512, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(units=2, activation='softmax'))\n\nmodel.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:15:42.38494Z","iopub.execute_input":"2022-03-23T17:15:42.385255Z","iopub.status.idle":"2022-03-23T17:15:43.022551Z","shell.execute_reply.started":"2022-03-23T17:15:42.385221Z","shell.execute_reply":"2022-03-23T17:15:43.021596Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Callbacks \n\n**Early Stop**\n\nTo prevent over fitting we will stop the learning after 10 epochs and val_loss value not decreased\n\n**Learning Rate Reduction**\n\nWe will reduce the learning rate when then accuracy not increase for 2 steps","metadata":{}},{"cell_type":"code","source":"early_stop = EarlyStopping(patience=10)\nlearning_rate_reduction = ReduceLROnPlateau(monitor='val_acc',\n                                            factor=0.5,\n                                            patience=2,\n                                            verbose=1,\n                                            min_lr=0.00001)\n\ncallbacks = [early_stop, learning_rate_reduction]","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:43:22.409604Z","iopub.execute_input":"2022-04-09T21:43:22.409881Z","iopub.status.idle":"2022-04-09T21:43:22.415182Z","shell.execute_reply.started":"2022-04-09T21:43:22.409853Z","shell.execute_reply":"2022-04-09T21:43:22.414172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 3. Prepare Training & Validating data","metadata":{}},{"cell_type":"markdown","source":"Because we will use image genaretor `with class_mode=\"categorical\"`. \nWe need to convert column category into string. Then imagenerator will convert it one-hot encoding which is good for our classification.\n\nSo we will convert 1 to dog and 0 to cat","metadata":{}},{"cell_type":"code","source":"df['category'] = df['category'].replace({0:'cat', 1:'dog'})\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:43:34.264676Z","iopub.execute_input":"2022-04-09T21:43:34.264862Z","iopub.status.idle":"2022-04-09T21:43:34.280481Z","shell.execute_reply.started":"2022-04-09T21:43:34.26484Z","shell.execute_reply":"2022-04-09T21:43:34.279815Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Split the input data into training(80%) & validation(20%)","metadata":{}},{"cell_type":"code","source":"train_df, validate_df = train_test_split(df, test_size=0.20, random_state=42)\n\nprint(\"before reseting index\\n\\n\", train_df)\n\n# reseting index creates a new column with the old values for the index before reset\n# so activating drop=True enables us to remove that column 'index' holding the prev index values\ntrain_df = train_df.reset_index(drop=True)\nvalidate_df = validate_df.reset_index(drop=True)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:43:37.915097Z","iopub.execute_input":"2022-04-09T21:43:37.915708Z","iopub.status.idle":"2022-04-09T21:43:37.933315Z","shell.execute_reply.started":"2022-04-09T21:43:37.915669Z","shell.execute_reply":"2022-04-09T21:43:37.932612Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"After reseting index with drop=True\\n\")\ntrain_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:16:01.963615Z","iopub.execute_input":"2022-03-23T17:16:01.964293Z","iopub.status.idle":"2022-03-23T17:16:01.97546Z","shell.execute_reply.started":"2022-03-23T17:16:01.964248Z","shell.execute_reply":"2022-03-23T17:16:01.974469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Note: Here is the output if we used drop=False\n\n![image.png](attachment:eaf94cce-6478-4b1f-bd94-550e133eb589.png)","metadata":{},"attachments":{"eaf94cce-6478-4b1f-bd94-550e133eb589.png":{"image/png":"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"}}},{"cell_type":"code","source":"train_df['category'].value_counts().plot.bar()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:16:06.128737Z","iopub.execute_input":"2022-03-23T17:16:06.129714Z","iopub.status.idle":"2022-03-23T17:16:06.319113Z","shell.execute_reply.started":"2022-03-23T17:16:06.129648Z","shell.execute_reply":"2022-03-23T17:16:06.318166Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"validate_df['category'].value_counts().plot.bar()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:16:08.707812Z","iopub.execute_input":"2022-03-23T17:16:08.708129Z","iopub.status.idle":"2022-03-23T17:16:08.891301Z","shell.execute_reply.started":"2022-03-23T17:16:08.708088Z","shell.execute_reply":"2022-03-23T17:16:08.890417Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"define variables","metadata":{}},{"cell_type":"code","source":"total_train = train_df.shape[0]\ntotal_validate = validate_df.shape[0]\nbatch_size = 15","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:43:49.216245Z","iopub.execute_input":"2022-04-09T21:43:49.216719Z","iopub.status.idle":"2022-04-09T21:43:49.221707Z","shell.execute_reply.started":"2022-04-09T21:43:49.216682Z","shell.execute_reply":"2022-04-09T21:43:49.220985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 4. Data Augmentation","metadata":{}},{"cell_type":"markdown","source":"**Training Generator**","metadata":{}},{"cell_type":"code","source":"train_datagen = ImageDataGenerator(\n    rotation_range=15,\n    rescale=1./255,\n    shear_range=0.1,\n    zoom_range=0.2,\n    horizontal_flip=True,\n    width_shift_range=0.1,\n    height_shift_range=0.1)\n\ntrain_generator = train_datagen.flow_from_dataframe(\n    train_df,\n    TRAIN_DIR,\n    x_col='filename',\n    y_col='category',\n    target_size=(IMAGE_HEIGHT, IMAGE_WIDTH),\n    class_mode='categorical',\n    batch_size=batch_size\n    \n)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:43:53.805058Z","iopub.execute_input":"2022-04-09T21:43:53.805624Z","iopub.status.idle":"2022-04-09T21:43:54.008591Z","shell.execute_reply.started":"2022-04-09T21:43:53.805585Z","shell.execute_reply":"2022-04-09T21:43:54.007852Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Validation Generator**","metadata":{}},{"cell_type":"code","source":"validate_datagen = ImageDataGenerator(rescale=1./255)\n\nvalidate_generator = validate_datagen.flow_from_dataframe(\n    validate_df,\n    TRAIN_DIR,\n    x_col='filename',\n    y_col='category',\n    target_size=(IMAGE_HEIGHT, IMAGE_WIDTH),\n    class_mode='categorical',\n    batch_size=batch_size\n)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:44:04.492633Z","iopub.execute_input":"2022-04-09T21:44:04.492837Z","iopub.status.idle":"2022-04-09T21:44:04.55128Z","shell.execute_reply.started":"2022-04-09T21:44:04.492813Z","shell.execute_reply":"2022-04-09T21:44:04.550599Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Try the generator**","metadata":{}},{"cell_type":"code","source":"example_df = train_df.sample(n=1).reset_index(drop=True)\nexample_generator = train_datagen.flow_from_dataframe(\n    example_df,\n    TRAIN_DIR,\n    x_col='filename',\n    y_col='category',\n    target_size=(IMAGE_HEIGHT, IMAGE_WIDTH),\n    class_mode='categorical',\n)","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:16:21.547111Z","iopub.execute_input":"2022-03-23T17:16:21.547391Z","iopub.status.idle":"2022-03-23T17:16:21.558885Z","shell.execute_reply.started":"2022-03-23T17:16:21.54736Z","shell.execute_reply":"2022-03-23T17:16:21.558177Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(12, 12))\nfor i in range(0, 15):\n    plt.subplot(5, 3, i+1)\n    for X_batch, Y_batch in example_generator:\n        image = X_batch[0]\n        plt.imshow(image)\n        break\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:16:25.040106Z","iopub.execute_input":"2022-03-23T17:16:25.041048Z","iopub.status.idle":"2022-03-23T17:16:27.099369Z","shell.execute_reply.started":"2022-03-23T17:16:25.040974Z","shell.execute_reply":"2022-03-23T17:16:27.098372Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 5. Train model","metadata":{}},{"cell_type":"code","source":"_epochs = 15 if FAST_RUN else 50\nhistory = model.fit(\n    train_generator,\n    epochs=_epochs,\n    validation_data=validate_generator,\n    validation_steps=total_validate//batch_size,\n    steps_per_epoch=total_train//batch_size,\n    callbacks=callbacks\n)","metadata":{"execution":{"iopub.status.busy":"2022-03-23T17:17:21.364998Z","iopub.execute_input":"2022-03-23T17:17:21.365689Z","iopub.status.idle":"2022-03-23T19:21:07.114756Z","shell.execute_reply.started":"2022-03-23T17:17:21.365651Z","shell.execute_reply":"2022-03-23T19:21:07.113165Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 6. Save Weights","metadata":{}},{"cell_type":"code","source":"model.save_weights(\"weights.h5\")","metadata":{"execution":{"iopub.status.busy":"2022-03-23T19:21:16.504829Z","iopub.execute_input":"2022-03-23T19:21:16.505225Z","iopub.status.idle":"2022-03-23T19:21:16.704443Z","shell.execute_reply.started":"2022-03-23T19:21:16.505186Z","shell.execute_reply":"2022-03-23T19:21:16.702842Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 7. Virtualize Training","metadata":{}},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 12))\nax1.plot(history.history['loss'], color='b', label=\"Training loss\")\nax1.plot(history.history['val_loss'], color='r', label=\"validation loss\")\nax1.set_xticks(np.arange(1, _epochs, 1))\nax1.set_yticks(np.arange(0, 1, 0.1))\n\nax2.plot(history.history['accuracy'], color='b', label=\"Training accuracy\")\nax2.plot(history.history['val_accuracy'], color='r',label=\"Validation accuracy\")\nax2.set_xticks(np.arange(1, _epochs, 1))\n\nlegend = plt.legend(loc='best', shadow=True)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T19:28:16.826264Z","iopub.execute_input":"2022-03-23T19:28:16.826588Z","iopub.status.idle":"2022-03-23T19:28:17.95182Z","shell.execute_reply.started":"2022-03-23T19:28:16.826554Z","shell.execute_reply":"2022-03-23T19:28:17.9512Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 8. Prepare Testing Data","metadata":{}},{"cell_type":"code","source":"test_filenames = os.listdir(TEST_DIR)\ntest_df = pd.DataFrame({\n    'filename': test_filenames\n})\nnb_samples = test_df.shape[0]","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:26:39.07911Z","iopub.execute_input":"2022-04-09T22:26:39.079388Z","iopub.status.idle":"2022-04-09T22:26:39.093896Z","shell.execute_reply.started":"2022-04-09T22:26:39.079356Z","shell.execute_reply":"2022-04-09T22:26:39.092846Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Create Testing Generator**","metadata":{}},{"cell_type":"code","source":"test_datagen = ImageDataGenerator(rescale=1./255)\ntest_generator =  test_datagen.flow_from_dataframe(\n    test_df,\n    TEST_DIR,\n    x_col='filename',\n    y_col=None,\n    class_mode=None,\n    target_size=(IMAGE_HEIGHT, IMAGE_WIDTH),\n    batch_size=batch_size,\n    shuffle=False\n)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:26:43.612067Z","iopub.execute_input":"2022-04-09T22:26:43.61235Z","iopub.status.idle":"2022-04-09T22:26:43.728113Z","shell.execute_reply.started":"2022-04-09T22:26:43.612313Z","shell.execute_reply":"2022-04-09T22:26:43.727362Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 9. Predict","metadata":{}},{"cell_type":"code","source":"predict = model.predict(test_generator, steps=np.ceil(nb_samples/batch_size))","metadata":{"execution":{"iopub.status.busy":"2022-03-23T19:38:39.139963Z","iopub.execute_input":"2022-03-23T19:38:39.140273Z","iopub.status.idle":"2022-03-23T19:39:55.032257Z","shell.execute_reply.started":"2022-03-23T19:38:39.140243Z","shell.execute_reply":"2022-03-23T19:39:55.03131Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"For categoral classication the prediction will come with probability of each category. So we will pick the category that have the highest probability with numpy average max","metadata":{}},{"cell_type":"code","source":"test_df['category'] = np.argmax(predict, axis=-1)","metadata":{"execution":{"iopub.status.busy":"2022-03-23T19:47:28.942124Z","iopub.execute_input":"2022-03-23T19:47:28.942637Z","iopub.status.idle":"2022-03-23T19:47:28.951357Z","shell.execute_reply.started":"2022-03-23T19:47:28.942603Z","shell.execute_reply":"2022-03-23T19:47:28.950392Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We will convert the predict category back into our generator classes by using `train_generator.class_indices`. It is the classes that image generator map while converting data into computer vision","metadata":{}},{"cell_type":"code","source":"lable_map = dict((v,k) for k,v in train_generator.class_indices.items())\ntest_df['category'] = test_df['category'].replace(lable_map)","metadata":{"execution":{"iopub.status.busy":"2022-03-23T19:49:52.786194Z","iopub.execute_input":"2022-03-23T19:49:52.786508Z","iopub.status.idle":"2022-03-23T19:49:52.796354Z","shell.execute_reply.started":"2022-03-23T19:49:52.786478Z","shell.execute_reply":"2022-03-23T19:49:52.795243Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"From our prepare data part. We map data with {1: 'dog', 0: 'cat'}. Now we will map the result back to dog is 1 and cat is 0","metadata":{}},{"cell_type":"code","source":"test_df['category'] = test_df['category'].replace({'dog': 1, 'cat': 0})","metadata":{"execution":{"iopub.status.busy":"2022-03-23T19:51:26.571341Z","iopub.execute_input":"2022-03-23T19:51:26.571674Z","iopub.status.idle":"2022-03-23T19:51:26.587812Z","shell.execute_reply.started":"2022-03-23T19:51:26.571641Z","shell.execute_reply":"2022-03-23T19:51:26.586745Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 10. Virtualize Result","metadata":{}},{"cell_type":"code","source":"test_df['category'].value_counts().plot.bar()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T19:52:01.889281Z","iopub.execute_input":"2022-03-23T19:52:01.889772Z","iopub.status.idle":"2022-03-23T19:52:02.068456Z","shell.execute_reply.started":"2022-03-23T19:52:01.889735Z","shell.execute_reply":"2022-03-23T19:52:02.067648Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**See predicted result with images**","metadata":{}},{"cell_type":"code","source":"sample_test = test_df.head(18)\nsample_test.head()\nplt.figure(figsize=(12, 24))\nfor index, row in sample_test.iterrows():\n    filename = row['filename']\n    category = row['category']\n    img = load_img(TEST_DIR+filename, target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n    plt.subplot(6, 3, index+1)\n    plt.imshow(img)\n    plt.xlabel(filename + '(' + \"{}\".format(category) + ')' )\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-03-23T19:53:38.808785Z","iopub.execute_input":"2022-03-23T19:53:38.809129Z","iopub.status.idle":"2022-03-23T19:53:42.401553Z","shell.execute_reply.started":"2022-03-23T19:53:38.809093Z","shell.execute_reply":"2022-03-23T19:53:42.400201Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Submission**","metadata":{}},{"cell_type":"code","source":"submission_df = test_df.copy()\nsubmission_df['id'] = submission_df['filename'].str.split('.').str[0]\nsubmission_df['label'] = submission_df['category']\nsubmission_df.drop(['filename', 'category'], axis=1, inplace=True)\nsubmission_df.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2022-03-23T19:54:47.431819Z","iopub.execute_input":"2022-03-23T19:54:47.432357Z","iopub.status.idle":"2022-03-23T19:54:47.49995Z","shell.execute_reply.started":"2022-03-23T19:54:47.432291Z","shell.execute_reply":"2022-03-23T19:54:47.499063Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# 11. Transfer Learning","metadata":{}},{"cell_type":"markdown","source":"There are two different Transfer Learning approaches;\n\n* Feature Extractor\n* Fine Tunning","metadata":{}},{"cell_type":"markdown","source":"**11.1. Fearure Extractor**","metadata":{}},{"cell_type":"markdown","source":"We will use VGG16 with imagenet weights","metadata":{}},{"cell_type":"markdown","source":"<center>\n    <img 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load VGG16 model**","metadata":{}},{"cell_type":"code","source":"base_model = applications.vgg16.VGG16(weights='imagenet', include_top=False, input_shape=input_shape)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:44:23.621522Z","iopub.execute_input":"2022-04-09T21:44:23.622362Z","iopub.status.idle":"2022-04-09T21:44:26.676552Z","shell.execute_reply.started":"2022-04-09T21:44:23.622313Z","shell.execute_reply":"2022-04-09T21:44:26.675813Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"notice that the last layers in the above architecture **are not included** in the below summary as we have set the parameter include_top to false","metadata":{}},{"cell_type":"code","source":"base_model.summary()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:44:35.674416Z","iopub.execute_input":"2022-04-09T21:44:35.675026Z","iopub.status.idle":"2022-04-09T21:44:35.689916Z","shell.execute_reply.started":"2022-04-09T21:44:35.674989Z","shell.execute_reply":"2022-04-09T21:44:35.689168Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now, we need to freeze all the layers so that their parameters won't be changed during training.","metadata":{}},{"cell_type":"code","source":"for layer in base_model.layers:\n    layer.trainable = False;","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:44:44.430042Z","iopub.execute_input":"2022-04-09T21:44:44.430405Z","iopub.status.idle":"2022-04-09T21:44:44.43933Z","shell.execute_reply.started":"2022-04-09T21:44:44.430356Z","shell.execute_reply":"2022-04-09T21:44:44.438579Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Start adding the final classifying layers that we want to train on the new dataset","metadata":{}},{"cell_type":"markdown","source":"**11.1.1. Build Classifier**\n","metadata":{}},{"cell_type":"code","source":"m = base_model.layers[-1].output\nm = GlobalAveragePooling2D()(m)\nm = Dense(100, activation='relu')(m)\nm = Dropout(0.4)(m)\nm = Dense(64, activation='relu')(m)\nm = Dense(2, activation='softmax')(m)\n\ntransfer_model = Model(base_model.input, m);\ntransfer_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\ntransfer_model.summary()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:46:03.214431Z","iopub.execute_input":"2022-04-09T21:46:03.2147Z","iopub.status.idle":"2022-04-09T21:46:03.265383Z","shell.execute_reply.started":"2022-04-09T21:46:03.214672Z","shell.execute_reply":"2022-04-09T21:46:03.264701Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**11.1.3. Train Model**\n","metadata":{}},{"cell_type":"markdown","source":"Now let's train our new model","metadata":{}},{"cell_type":"code","source":"t_epochs = 15 if FAST_RUN else 50\ntransfer_history = transfer_model.fit(    \n    train_generator,\n    epochs=t_epochs,\n    validation_data=validate_generator,\n    validation_steps=total_validate//batch_size,\n    steps_per_epoch=total_train//batch_size,\n    callbacks=callbacks\n)","metadata":{"execution":{"iopub.status.busy":"2022-04-09T21:46:07.669533Z","iopub.execute_input":"2022-04-09T21:46:07.669938Z","iopub.status.idle":"2022-04-09T22:20:48.599021Z","shell.execute_reply.started":"2022-04-09T21:46:07.669899Z","shell.execute_reply":"2022-04-09T22:20:48.598317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**11.1.4. Save Weights**\n","metadata":{}},{"cell_type":"code","source":"transfer_model.save_weights(\"vgg16_transfer_FE_weights.h5\");","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:21:27.245489Z","iopub.execute_input":"2022-04-09T22:21:27.245745Z","iopub.status.idle":"2022-04-09T22:21:27.369528Z","shell.execute_reply.started":"2022-04-09T22:21:27.245719Z","shell.execute_reply":"2022-04-09T22:21:27.368761Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**11.1.5. Virtualize Training**","metadata":{}},{"cell_type":"code","source":"fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 12))\nax1.plot(transfer_history.history['loss'], color='b', label=\"Training loss\")\nax1.plot(transfer_history.history['val_loss'], color='r', label=\"validation loss\")\nax1.set_xticks(np.arange(1, t_epochs, 1))\nax1.set_yticks(np.arange(0, 1, 0.1))\n\nax2.plot(transfer_history.history['accuracy'], color='b', label=\"Training accuracy\")\nax2.plot(transfer_history.history['val_accuracy'], color='r',label=\"Validation accuracy\")\nax2.set_xticks(np.arange(1, t_epochs, 1))\n\nlegend = plt.legend(loc='best', shadow=True)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:23:34.942619Z","iopub.execute_input":"2022-04-09T22:23:34.942882Z","iopub.status.idle":"2022-04-09T22:23:35.301354Z","shell.execute_reply.started":"2022-04-09T22:23:34.942845Z","shell.execute_reply":"2022-04-09T22:23:35.300658Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**11.1.6. Predict**","metadata":{}},{"cell_type":"code","source":"transfer_predict = transfer_model.predict(test_generator, steps=np.ceil(nb_samples/batch_size))","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:26:52.512138Z","iopub.execute_input":"2022-04-09T22:26:52.512984Z","iopub.status.idle":"2022-04-09T22:27:21.954629Z","shell.execute_reply.started":"2022-04-09T22:26:52.512942Z","shell.execute_reply":"2022-04-09T22:27:21.953872Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_df['category'] = np.argmax(transfer_predict, axis=-1)\nlable_map = dict((v,k) for k,v in train_generator.class_indices.items())\ntest_df['category'] = test_df['category'].replace(lable_map)\ntest_df['category'] = test_df['category'].replace({'dog': 1, 'cat': 0})\ntest_df['category'].value_counts().plot.bar()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:28:49.40928Z","iopub.execute_input":"2022-04-09T22:28:49.409954Z","iopub.status.idle":"2022-04-09T22:28:49.60251Z","shell.execute_reply.started":"2022-04-09T22:28:49.409919Z","shell.execute_reply":"2022-04-09T22:28:49.601762Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_test = test_df.head(50)\nsample_test.head()\nplt.figure(figsize=(12, 24))\nfor index, row in sample_test.iterrows():\n    filename = row['filename']\n    category = row['category']\n    img = load_img(TEST_DIR+filename, target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n    plt.subplot(10, 5, index+1)\n    plt.imshow(img)\n    plt.xlabel(filename + '(' + \"{}\".format(category) + ')' )\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-04-09T22:32:15.923Z","iopub.execute_input":"2022-04-09T22:32:15.923262Z","iopub.status.idle":"2022-04-09T22:32:21.557528Z","shell.execute_reply.started":"2022-04-09T22:32:15.923234Z","shell.execute_reply":"2022-04-09T22:32:21.553938Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# References:\nNotebook: [link](https://www.kaggle.com/code/uysimty/keras-cnn-dog-or-cat-classification/notebook)\n\nCreating a model: [link](https://pyimagesearch.com/2019/10/28/3-ways-to-create-a-keras-model-with-tensorflow-2-0-sequential-functional-and-model-subclassing/)\n\nCross Entropy Loss Functions: [link1](https://vitalflux.com/keras-categorical-cross-entropy-loss-function/) [link2](https://peltarion.com/knowledge-center/documentation/modeling-view/build-an-ai-model/loss-functions/categorical-crossentropy)\n\nEarly Stopping: [link](https://keras.io/api/callbacks/early_stopping/)\n\nReduce Learning Rate: [link](https://keras.io/api/callbacks/reduce_lr_on_plateau/)\n\nsklearn.model_selection.train_test_split: [link](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)\n\npandas.DataFrame.reset_index: [link](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reset_index.html)\n\ntf.keras.preprocessing.image.ImageDataGenerato: [link](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator)\n\nkeras.fit() vs keras.fit_generator(): [link1](https://pyimagesearch.com/2018/12/24/how-to-use-keras-fit-and-fit_generator-a-hands-on-tutorial/) [link2](https://www.geeksforgeeks.org/keras-fit-and-keras-fit_generator/)\n\nTransfer Learning: \n[documentation](https://www.tensorflow.org/tutorials/images/transfer_learning) \n[blog](https://www.v7labs.com/blog/transfer-learning-guide) \n[notebook](https://www.kaggle.com/code/mustafacicek/dogs-cats-vgg16-implementation-transfer-learning#5\\)-Pre-Trained-VGG-16-Model-&-Transfer-Learning)","metadata":{}}]}