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April 21, 2019 09:51
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| import numpy | |
| from keras.datasets import mnist | |
| from keras.models import Sequential | |
| from keras.layers import Dense | |
| from keras.layers import Dropout | |
| from keras.layers import Flatten | |
| from keras.layers.convolutional import Conv2D | |
| from keras.layers.convolutional import MaxPooling2D | |
| from keras.utils import np_utils | |
| from keras import backend as K | |
| K.set_image_dim_ordering('tf') | |
| # fix random seed for reproducibility | |
| seed = 7 | |
| numpy.random.seed(seed) | |
| # load data | |
| (X_train, y_train), (X_test, y_test) = mnist.load_data() | |
| # reshape to be [samples][pixels][width][height] | |
| X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32') | |
| X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32') | |
| # normalize inputs from 0-255 to 0-1 | |
| X_train = X_train / 255 | |
| X_test = X_test / 255 | |
| # one hot encode outputs | |
| y_train = np_utils.to_categorical(y_train) | |
| y_test = np_utils.to_categorical(y_test) | |
| num_classes = y_test.shape[1] | |
| def larger_model(): | |
| # create model | |
| model = Sequential() | |
| model.add(Conv2D(30, (5, 5), input_shape=(1, 28, 28), activation='relu')) | |
| model.add(MaxPooling2D(pool_size=(2, 2))) | |
| model.add(Conv2D(15, (3, 3), activation='relu')) | |
| model.add(MaxPooling2D(pool_size=(2, 2))) | |
| model.add(Dropout(0.2)) | |
| model.add(Flatten()) | |
| model.add(Dense(128, activation='relu')) | |
| model.add(Dense(50, activation='relu')) | |
| model.add(Dense(num_classes, activation='softmax')) | |
| # Compile model | |
| model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) | |
| return model | |
| # build the model | |
| model = larger_model() | |
| # Fit the model | |
| model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200) | |
| # Final evaluation of the model | |
| scores = model.evaluate(X_test, y_test, verbose=0) | |
| print("Large CNN Error: %.2f%%" % (100-scores[1]*100)) |
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