X_train, Y_train, X_test, Y_test = Helper.load_data(train_path, train_labels_path, test_path, test_labels_path)
# convert class vectors to binary class matrices
#Y_train = np_utils.to_categorical(Y_train, nb_classes)
#Y_test = np_utils.to_categorical(Y_test, nb_classes)
#print(np.shape(X_train))
#print(len(Y_train))
#print(np.shape(X_test))
#print(len(Y_test))
model = Graph()
model.input(shape=(img_channels, img_rows, img_cols), name='input1')
model.node(Convolution2D(32, 7, 7), name='conv11', input='input1', activation='relu')
model.node(Convolution2D(32, 3, 3), name='conv12', input='conv11', activation='relu')
model.node(MaxPooling2D(poolsize=(2, 2)), name='pool1', input='conv12')
model.node(Convolution2D(32, 1, 1), name='conv21', input='pool1', activation='relu')
model.node(Convolution2D(32, 1, 1), name='conv22', input='pool1', activation='relu')
model.node(Convolution2D(32, 1, 1), name='conv23', input='pool1', activation='relu')
model.node(MaxPooling2D(poolsize=(2, 2)), name='pool21', input='pool1')
model.node(Convolution2D(32, 3, 3), name='conv31', input='conv22', activation='relu')
model.node(Convolution2D(32, 5, 5), name='conv32', input='conv23', activation='relu')
model.node(Convolution2D(32, 1, 1), name='conv33', input='pool21', activation='relu')
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
