def createSAE(input_height, input_width, X_train, X_out):
encode_size = 200
cnn1 = NeuralNet(layers=[
('input', layers.InputLayer),
('hidden', layers.DenseLayer),
('hiddenOut', layers.DenseLayer),
('output_layer', ReshapeLayer),
],
input_shape=(None, 1, input_width, input_height),
hidden_num_units= 10000,
hiddenOut_num_units= 42000,
output_layer_shape = (([0], -1)),
update_learning_rate=learning_rate,
update_momentum=update_momentum,
update=nesterov_momentum,
train_split=TrainSplit(eval_size=train_valid_split),
# batch_iterator_train=BatchIterator(batch_size=batch_size),
batch_iterator_train=FlipBatchIterator(batch_size=batch_size),
regression=True,
max_epochs=epochs,
verbose=1,
hiddenLayer_to_output=-3)
cnn1.fit(X_train, X_out)
trian_last_hiddenLayer = cnn1.output_hiddenLayer(X_train)
test_last_hiddenLayer = cnn1.output_hiddenLayer(test_x)
cnn2 = NeuralNet(layers=[
('input', layers.InputLayer),
('hidden', layers.DenseLayer),
('output_layer', layers.DenseLayer),
],
input_shape=(None,10000),
hidden_num_units= 3000,
output_layer_num_units = 10000,
update_learning_rate=learning_rate,
update_momentum=update_momentum,
update=nesterov_momentum,
train_split=TrainSplit(eval_size=train_valid_split),
batch_iterator_train=BatchIterator(batch_size=batch_size),
# batch_iterator_train=FlipBatchIterator(batch_size=batch_size),
regression=True,
max_epochs=epochs,
verbose=1,
hiddenLayer_to_output=-2)
trian_last_hiddenLayer = trian_last_hiddenLayer.astype(np.float32)
cnn2.fit(trian_last_hiddenLayer, trian_last_hiddenLayer)
trian_last_hiddenLayer = cnn2.output_hiddenLayer(trian_last_hiddenLayer)
test_last_hiddenLayer = cnn2.output_hiddenLayer(test_last_hiddenLayer)
cnn3 = NeuralNet(layers=[
('input', layers.InputLayer),
('hidden', layers.DenseLayer),
('output_layer', layers.DenseLayer),
],
input_shape=(None,3000),
hidden_num_units= 1000,
output_layer_num_units = 3000,
update_learning_rate=learning_rate,
update_momentum=update_momentum,
update=nesterov_momentum,
train_split=TrainSplit(eval_size=train_valid_split),
batch_iterator_train=BatchIterator(batch_size=batch_size),
# batch_iterator_train=FlipBatchIterator(batch_size=batch_size),
regression=True,
max_epochs=epochs,
verbose=1,
hiddenLayer_to_output=-2)
trian_last_hiddenLayer = trian_last_hiddenLayer.astype(np.float32)
cnn3.fit(trian_last_hiddenLayer, trian_last_hiddenLayer)
trian_last_hiddenLayer = cnn3.output_hiddenLayer(trian_last_hiddenLayer)
test_last_hiddenLayer = cnn3.output_hiddenLayer(test_last_hiddenLayer)
cnn4 = NeuralNet(layers=[
('input', layers.InputLayer),
('hidden', layers.DenseLayer),
('output_layer', layers.DenseLayer),
],
input_shape=(None,1000),
hidden_num_units= 300,
output_layer_num_units = 1000,
update_learning_rate=learning_rate,
update_momentum=update_momentum,
update=nesterov_momentum,
train_split=TrainSplit(eval_size=train_valid_split),
batch_iterator_train=BatchIterator(batch_size=batch_size),
# batch_iterator_train=FlipBatchIterator(batch_size=batch_size),
regression=True,
max_epochs=epochs,
verbose=1,
hiddenLayer_to_output=-2)
trian_last_hiddenLayer = trian_last_hiddenLayer.astype(np.float32)
cnn4.fit(trian_last_hiddenLayer, trian_last_hiddenLayer)
trian_last_hiddenLayer = cnn4.output_hiddenLayer(trian_last_hiddenLayer)
test_last_hiddenLayer = cnn4.output_hiddenLayer(test_last_hiddenLayer)
input_layer = cnn1.get_all_layers()[0]
hidden1_layer = cnn1.get_all_layers()[1]
hidden1_layer.input_layer = input_layer
hidden2_layer = cnn2.get_all_layers()[1]
hidden2_layer.input_layer = hidden1_layer
hidden3_layer = cnn3.get_all_layers()[1]
hidden3_layer.input_layer = hidden2_layer
final_layer = cnn4.get_all_layers()[1]
final_layer.input_layer = hidden3_layer
# out_train = final_layer.get_output(x_train).eval()
# out_test = final_layer.get_output(test_x).eval()
f = gzip.open(folder_path + "output.pkl.gz",'wb')
cPickle.dump((trian_last_hiddenLayer, test_last_hiddenLayer), f, protocol=2)
f.close()
# f = gzip.open("pickled_images/tmp.pkl.gz", 'rb')
# trian_last_hiddenLayer, test_last_hiddenLayer = cPickle.load(f)
# f.close()
return cnn1
