def create_output_node(model=None, input_sequences=None, num_gru=None, old_h0s=None, reset=None, num_pixelCNN_layer = None):
assert(model is not None)
assert(input_sequences is not None)
assert(num_gru is not None)
assert(old_h0s is not None)
assert(reset is not None)
assert(num_pixelCNN_layer is not None)
new_h0s = T.zeros_like(old_h0s)
h0s = theano.ifelse.ifelse(reset, new_h0s, old_h0s)
for i in range(num_gru):
gru_layer = GRU(DIM, DIM, last_layer, s0 = h0s[i,:,:], name = model.name+"GRU_{}".format(i))
last_hidden_list.append(gru_layer.output()[:,-1])
model.add_layer(gru_layer)
last_layer = gru_layer
fc1 = FC(DIM, Q_LEVELS, last_layer, name = model.name+"FullyConnected")
model.add_layer(fc1)
softmax = Softmax(fc1, name= model.name+"Softmax")
model.add_layer(softmax)
return softmax.output(), T.stack(last_hidden_list, axis = 0)
def create_output_node(model=None, input_sequences=None, num_gru=None, old_h0s=None, reset=None, num_pixelCNN_layer = None):
assert(model is not None)
assert(input_sequences is not None)
assert(num_gru is not None)
assert(old_h0s is not None)
assert(reset is not None)
assert(num_pixelCNN_layer is not None)
new_h0s = T.zeros_like(old_h0s)
h0s = theano.ifelse.ifelse(reset, new_h0s, old_h0s)
embedding_layer = Embedding(Q_LEVELS, DIM, input_sequences, name = model.name+"Embedding.Q_LEVELS")
model.add_layer(embedding_layer)
prev_out = embedding_layer.output()
last_layer = WrapperLayer(prev_out.reshape((prev_out.shape[0], prev_out.shape[1], WIDTH, DEPTH)))
pixel_CNN = pixelConv(
last_layer,
DEPTH,
DEPTH,
name = model.name + ".pxCNN",
num_layers = NUM_PIXEL_CNN_LAYER
)
prev_out = pixel_CNN.output()
last_layer = WrapperLayer(prev_out.reshape((prev_out.shape[0], prev_out.shape[1], -1)))
last_hidden_list = []
for i in range(num_gru):
gru_layer = GRU(DIM, DIM, last_layer, s0 = h0s[i,:,:], name = model.name+"GRU_{}".format(i))
last_hidden_list.append(gru_layer.output()[:,-1])
model.add_layer(gru_layer)
last_layer = gru_layer
fc1 = FC(DIM, Q_LEVELS, last_layer, name = model.name+"FullyConnected")
model.add_layer(fc1)
softmax = Softmax(fc1, name= model.name+"Softmax")
model.add_layer(softmax)
return softmax.output(), T.stack(last_hidden_list, axis = 0)