keras_test.py

# -*- coding: utf-8 -*-
import numpy as np
np.random.seed(1337)  # for reproducibility

from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.layers import Convolution2D
from keras import backend as K
from keras.optimizers import SGD
import matplotlib.pyplot as plt


batch_size = 500
nb_output = 1
nb_epoch = 10

# input image dimensions
img_rows, img_cols = 16, 16
# number of convolutional filters to use
nb_filters = 20
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = (3, 3)


X_train = np.random.randn(10000, 16*16)
Y_train = np.mean(X_train, 1)

X_train = X_train.astype('float32')
X_test = np.random.randn(1000, 16*16)
Y_test = np.mean(X_test, 1)

if K._BACKEND == 'theano':
    X_train = np.reshape(X_train, (10000, 1, 16, 16))
    X_test = np.reshape(X_test, (1000, 1, 16, 16))
else:
    X_train = np.reshape(X_train, (10000, 16, 16, 1))
    X_test = np.reshape(X_test, (1000, 16, 16, 1))

print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')


model = Sequential()

model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
                        border_mode='same',
                        input_shape=X_train.shape[1:], init='glorot_uniform'))
model.add(Activation('relu'))

#model.add(Flatten(input_shape=X_train.shape[1:]))
model.add(Flatten())
model.add(Dense(10, init='glorot_uniform'))
model.add(Activation('sigmoid'))
model.add(Dense(nb_output, init='glorot_uniform'))
model.add(Activation('linear'))

sgd = SGD(lr=0.1,  momentum=0.9, nesterov=True)#decay=1e-6,
model.compile(loss='mse',
              optimizer=sgd)

model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
          verbose=1, validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=1)
predicts = model.predict(X_test, batch_size=1000, verbose=0)
print('Test score:', score[0])
plt.figure()
plt.scatter(Y_test, predicts)