{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\n'''for dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))'''\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import keras\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nimport tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"(x_train, y_train), (x_test, y_test) = cifar10.load_data()\nprint('x_train shape:', x_train.shape)\nprint('y_train shape:', y_train.shape)\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\n\n# One hot encoding.\ny_train = keras.utils.to_categorical(y_train, 10)\ny_test = keras.utils.to_categorical(y_test, 10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train = x_train.astype('float32') / 256\nx_test = x_test.astype('float32') / 256","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def generate_model():\n    return tf.keras.models.Sequential([\n    tf.keras.layers.Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:]),\n    tf.keras.layers.Activation('relu'),\n    tf.keras.layers.Conv2D(32, (3, 3)),\n    tf.keras.layers.Activation('relu'),\n    tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n    tf.keras.layers.Dropout(0.25),\n\n    tf.keras.layers.Conv2D(64, (3, 3), padding='same'),\n    tf.keras.layers.Activation('relu'),\n    tf.keras.layers.Conv2D(64, (3, 3)),\n    tf.keras.layers.Activation('relu'),\n    tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n    tf.keras.layers.Dropout(0.25),\n\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(512),\n    tf.keras.layers.Activation('relu'),\n    tf.keras.layers.Dropout(0.5),\n    tf.keras.layers.Dense(10),\n    tf.keras.layers.Activation('softmax')\n  ])\n\nmodel = generate_model()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"opt = keras.optimizers.RMSprop(learning_rate=0.0001, decay=1e-6)\n\nmodel.compile(loss='categorical_crossentropy',\n              optimizer=opt,\n              metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class myCallback(tf.keras.callbacks.Callback):\n    def on_epoch_end(self, epoch, logs={}):\n        if(logs.get('accuracy')>0.97):\n            print(\"\\nReached 97% accuracy so cancelling training!\")\n            self.model.stop_training = True ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"datagen = ImageDataGenerator(\n            zca_epsilon=1e-06,\n            rotation_range=10,\n            width_shift_range=0.1,\n            height_shift_range=0.1,\n            zoom_range=0.1,\n            fill_mode='nearest',\n            cval=0.,\n            horizontal_flip=True\n            )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"datagen.fit(x_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"callbacks = myCallback()\n\nhistory = model.fit_generator(datagen.flow(x_train, y_train,\n                                    batch_size=32),\n                                    epochs=40,\n                                    validation_data=(x_test, y_test),\n                                    callbacks=[callbacks],\n                                    workers=4)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\n\ndef plotmodelhistory(history): \n    fig, axs = plt.subplots(1,2,figsize=(15,5)) \n    # summarize history for accuracy\n    axs[0].plot(history.history['accuracy']) \n    axs[0].plot(history.history['val_accuracy']) \n    axs[0].set_title('Model Accuracy')\n    axs[0].set_ylabel('Accuracy') \n    axs[0].set_xlabel('Epoch')\n    axs[0].legend(['train', 'validate'], loc='upper left')\n    # summarize history for loss\n    axs[1].plot(history.history['loss']) \n    axs[1].plot(history.history['val_loss']) \n    axs[1].set_title('Model Loss')\n    axs[1].set_ylabel('Loss') \n    axs[1].set_xlabel('Epoch')\n    axs[1].legend(['train', 'validate'], loc='upper left')\n    plt.show()\n\n# list all data in history\nprint(history.history.keys())\n\nplotmodelhistory(history)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"scores = model.evaluate(x_test, y_test, verbose=1)\nprint('Test loss:', scores[0])\nprint('Test accuracy:', scores[1])\n\n# make prediction.\npred = model.predict(x_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"pred.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"results = np.argmax(pred,axis = 1)\nresults = pd.Series(results,name=\"Label\")\nsubmission = pd.concat([pd.Series(range(1,10001),name = \"id\"),results],axis = 1)\n\nprint(submission)\n\nsubmission.to_csv(\"submission.csv\",index=False)","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}