{"cells":[{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tf.__version__","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_labels = pd.read_csv(\"/kaggle/input/dog-breed-identification/labels.csv\")\nprint(df_labels)\ndf_labels['id'] = df_labels['id'] + '.jpg'\nfrom sklearn.model_selection import train_test_split\ndf_train, df_validation = train_test_split(df_labels, test_size=0.2, shuffle=True)\ndf_train.shape, df_validation.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator\n\ntrain_datagen = ImageDataGenerator(\n        rotation_range=40,\n        width_shift_range=0.2,\n        height_shift_range=0.2,\n        rescale=1./255,\n        shear_range=0.2,\n        zoom_range=0.2,\n        horizontal_flip=True,\n        fill_mode='nearest'\n)\n\nvalidation_datagen = ImageDataGenerator(\n        rescale=1./255)\n\ntraining_set = train_datagen.flow_from_dataframe(\n    dataframe=df_train,\n    directory='/kaggle/input/dog-breed-identification/train',\n    x_col=\"id\",\n    y_col=\"breed\",\n    target_size=(150, 150),\n    class_mode=\"categorical\",\n    batch_size=128\n)\n\nvalidation_set = validation_datagen.flow_from_dataframe(\n    dataframe=df_validation,\n    directory='/kaggle/input/dog-breed-identification/train',\n    x_col=\"id\",\n    y_col=\"breed\",\n    target_size=(150, 150),\n    class_mode=\"categorical\",\n    batch_size=128\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"classes = df_labels['breed'].unique().shape[0]\nclasses","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Initialising the CNN\ncnn = tf.keras.models.Sequential()\n\n# Step 1 - Convolution\ncnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[150, 150, 3]))\n\n# Step 2 - Pooling\ncnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n\n# Adding a second convolutional layer\ncnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))\ncnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n\n# Step 3 - Flattening\ncnn.add(tf.keras.layers.Flatten())\n\n# Step 4 - Full Connection\ncnn.add(tf.keras.layers.Dense(units=128, activation='relu'))\n\n# Step 5 - Output Layer\ncnn.add(tf.keras.layers.Dense(units=classes, activation='softmax'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cnn.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cnn.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# cnn.fit(x = training_set, validation_data = validation_set, epochs = 50)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test_datagen = ImageDataGenerator ( \n    rescale=1/255. \n)\n\ntest_set = test_datagen.flow_from_directory (\n    '/kaggle/input/dog-breed-identification',\n    target_size = (150,150),\n    batch_size = 128,\n    classes=['test']\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# y_pred = cnn.predict(test_set)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_submission = pd.read_csv('/kaggle/input/dog-breed-identification/sample_submission.csv')\ndf_submission","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import re\nfile_list = test_set.filenames\nid_list = []\nfor name in file_list:\n    m = re.sub('test/', '', name)\n    m = re.sub('.jpg', '', m)\n    id_list.append(m)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"id_list","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_submission['id'] = id_list\ndf_submission.iloc[:,1:] = y_pred\ndf_submission","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_df = df_submission.set_index('id')\nfinal_df.to_csv('Submission_12.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}