mnist_keras_mlp.py

import os
import struct
import numpy as np
import theano
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import SGD


def load_mnist(path, kind='train'):
    """Load MNIST data from `path`"""
    labels_path = os.path.join(path,
                               '%s-labels-idx1-ubyte' % kind)
    images_path = os.path.join(path,
                               '%s-images-idx3-ubyte' % kind)

    with open(labels_path, 'rb') as lbpath:
        magic, n = struct.unpack('>II',
                                 lbpath.read(8))
        labels = np.fromfile(lbpath,
                             dtype=np.uint8)

    with open(images_path, 'rb') as imgpath:
        magic, num, rows, cols = struct.unpack(">IIII",
                                               imgpath.read(16))
        images = np.fromfile(imgpath,
                             dtype=np.uint8).reshape(len(labels), 784)

    return images, labels


def main():
    X_train, y_train = load_mnist('mnist', kind='train')
    print('Rows: %d, columns: %d' % (X_train.shape[0], X_train.shape[1]))

    X_test, y_test = load_mnist('mnist', kind='t10k')
    print('Rows: %d, columns: %d' % (X_test.shape[0], X_test.shape[1]))

    theano.config.floatX = 'float32'
    X_train = X_train.astype(theano.config.floatX)
    X_test = X_test.astype(theano.config.floatX)

    from keras.utils import np_utils

    print('First 3 labels: ', y_train[:3])

    y_train_ohe = np_utils.to_categorical(y_train)
    print('\nFirst 3 labels (one-hot):\n', y_train_ohe[:3])

    np.random.seed(1)

    model = Sequential()
    model.add(Dense(input_dim=X_train.shape[1],
                    output_dim=50,
                    init='uniform',
                    activation='tanh'))

    model.add(Dense(input_dim=50,
                    output_dim=50,
                    init='uniform',
                    activation='tanh'))

    model.add(Dense(input_dim=50,
                    output_dim=y_train_ohe.shape[1],
                    init='uniform',
                    activation='softmax'))

    sgd = SGD(lr=0.001, decay=1e-7, momentum=.9)
    model.compile(loss='categorical_crossentropy', optimizer=sgd)

    model.fit(X_train, y_train_ohe,
              nb_epoch=10,
              batch_size=300,
              verbose=1,
              validation_split=0.1,
              show_accuracy=True)

    y_train_pred = model.predict_classes(X_train, verbose=0)
    print('First 3 predictions: ', y_train_pred[:3])

    train_acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]
    print('Training accuracy: %.2f%%' % (train_acc * 100))

    y_test_pred = model.predict_classes(X_test, verbose=0)
    test_acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]
    print('Test accuracy: %.2f%%' % (test_acc * 100))


if __name__ == '__main__':
    main()