09_tensorboard.py

from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
from keras.datasets import mnist
from keras import initializations
from keras.utils import np_utils
from keras.callbacks import TensorBoard

# Hyper parameters
batch_size = 128
nb_epoch = 100

# Parameters for MNIST dataset
nb_classes = 10

# Parameters for MLP
prob_drop_input = 0.2               # drop probability for dropout @ input layer
prob_drop_hidden = 0.5              # drop probability for dropout @ fc layer


def init_weights(shape, name=None):
    return initializations.normal(shape, scale=0.01, name=name)

# Load MNIST dataset
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
Y_Train = np_utils.to_categorical(y_train, nb_classes)
Y_Test = np_utils.to_categorical(y_test, nb_classes)

# Multilayer Perceptron model
model = Sequential()
model.add(Dense(output_dim=625, input_dim=784, init=init_weights, activation='sigmoid', name='dense1'))
model.add(Dropout(prob_drop_input, name='dropout1'))
model.add(Dense(output_dim=625, input_dim=625, init=init_weights, activation='sigmoid', name='dense2'))
model.add(Dropout(prob_drop_hidden, name='dropout2'))
model.add(Dense(output_dim=10, input_dim=625, init=init_weights, activation='softmax', name='dense3'))
model.compile(optimizer=RMSprop(lr=0.001, rho=0.9), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()

# Train
history = model.fit(X_train, Y_Train, nb_epoch=nb_epoch, batch_size=batch_size, verbose=1,
                    callbacks=[TensorBoard(log_dir='./logs/09_tensorboard', histogram_freq=1)])

# Evaluate
evaluation = model.evaluate(X_test, Y_Test, verbose=1)
print('Summary: Loss over the test dataset: %.2f, Accuracy: %.2f' % (evaluation[0], evaluation[1]))