Keras Example

keras_example.py

import keras
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD

# Batch size = 32, Input Dimension = 500, Hidden Dimension = 50

# Create the model
model = Sequential()
model.add(Dense(input_dim=500, output_dim=50))
model.add(Activation('relu'))
model.add(Dense(input_dim=50, output_dim=500))

# Define optimizer object
optimizer = SGD(lr=1e0)

# Compile the model
model.compile(loss='mean_squared_error', optimizer=optimizer)

# Randomize data
x = np.random.randn(32, 500)
y = np.random.randn(32, 500)

# Fit the model
model.fit(x, y, epochs=50, batch_size=64, verbose=0)