Created
July 7, 2017 20:34
-
-
Save nesterione/7fd799bc8153871dd880fae7b2f911b1 to your computer and use it in GitHub Desktop.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| from __future__ import print_function | |
| import keras | |
| from keras.datasets import cifar10 | |
| from keras.preprocessing.image import ImageDataGenerator | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Dropout, Activation, Flatten | |
| from keras.layers import Conv2D, MaxPooling2D | |
| import numpy as np | |
| batch_size = 32 | |
| num_classes = 10 | |
| epochs = 1 | |
| data_augmentation = True | |
| import pandas as pd | |
| data = pd.read_csv("./data/train.csv") | |
| data_set = data.as_matrix() | |
| x_data = data_set[:,1:] | |
| y_data = data_set[:,0] | |
| # print(x_train.shape) | |
| # print(y_train.shape) | |
| x_data = np.reshape(x_data,(42000,28,28,1)) | |
| print("data shape",x_data.shape) | |
| # print('x_train shape:', x_train.shape) | |
| # print(x_train.shape[0], 'train samples') | |
| # print(x_test.shape[0], 'test samples') | |
| b = int(x_data.shape[0]*0.8) | |
| x_train, x_test= x_data[:b], x_data[b:] | |
| y_train, y_test= y_data[:b], y_data[b:] | |
| print('x_train shape:', x_train.shape) | |
| print(x_train.shape[0], 'train samples') | |
| print(x_test.shape[0], 'test samples') | |
| # Convert class vectors to binary class matrices. | |
| y_train = keras.utils.to_categorical(y_train, num_classes) | |
| y_test = keras.utils.to_categorical(y_test, num_classes) | |
| model = Sequential() | |
| model.add(Conv2D(32, (3, 3), padding='same', | |
| input_shape=x_train.shape[1:])) | |
| model.add(Activation('relu')) | |
| model.add(Conv2D(32, (3, 3))) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(2, 2))) | |
| model.add(Dropout(0.25)) | |
| model.add(Conv2D(64, (3, 3), padding='same')) | |
| model.add(Activation('relu')) | |
| model.add(Conv2D(64, (3, 3))) | |
| model.add(Activation('relu')) | |
| model.add(MaxPooling2D(pool_size=(2, 2))) | |
| model.add(Dropout(0.25)) | |
| model.add(Flatten()) | |
| model.add(Dense(512)) | |
| model.add(Activation('relu')) | |
| model.add(Dropout(0.5)) | |
| model.add(Dense(num_classes)) | |
| model.add(Activation('softmax')) | |
| # initiate RMSprop optimizer | |
| opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6) | |
| # Let's train the model using RMSprop | |
| model.compile(loss='categorical_crossentropy', | |
| optimizer=opt, | |
| metrics=['accuracy']) | |
| x_train = x_train.astype('float32') | |
| x_test = x_test.astype('float32') | |
| x_train /= 255 | |
| x_test /= 255 | |
| print('Not using data augmentation.') | |
| model.fit(x_train, y_train, | |
| batch_size=batch_size, | |
| epochs=epochs, | |
| validation_data=(x_test, y_test), | |
| shuffle=True) | |
| print('saving model') | |
| model.save('nn_cnn.h5') | |
| model.save_weights('nn_cnn_weights.h5') | |
| print('model saved') |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment