mnist_keras_mlp.py

import os
import struct
import numpy as np
import theano
from keras.utils import np_utils
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

#### Loading the data

X_train, y_train = load_mnist('mnist', kind='train')
X_test, y_test = load_mnist('mnist', kind='t10k')

#### Preparing the data

X_train = X_train.astype(theano.config.floatX)
X_test = X_test.astype(theano.config.floatX)
y_train_ohe = np_utils.to_categorical(y_train)


#### Training

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=50,
          batch_size=300,
          verbose=1,
          validation_split=0.1,
          show_accuracy=True)

y_train_pred = model.predict_classes(X_train, verbose=0)
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))