def compute_logits(inputs, batch_size=-1):
#
# logits.append(FullyConnected.build_model(logits[-1], 'fullyconnected'))
#
logits = [inputs]
inputs_expanded = tf.expand_dims(inputs, 3)
logits.append(CNN.build_model(inputs_expanded, config.encoder('cnn')))
temp_shape = logits[-1].get_shape().as_list()
logits.append(
tf.reshape(
logits[-1],
shape=[batch_size, temp_shape[1], temp_shape[2] * temp_shape[3]]))
# logits.append(BottleNeck.build_model(logits[-1], config.encoder('bottleneck')))
# temp_shape = logits[-1].get_shape().as_list()
# logits.append(tf.reshape(logits[-1], shape=[batch_size, temp_shape[1], -1]))
logits.append(
BiDirectionLSTM.build_model(logits[-1],
config.encoder('bidirection_lstm')))
logits.append(
tf.reshape(logits[-1], shape=[-1, logits[-1].get_shape()[-1]]))
logits.append(
FullyConnected.build_model(logits[-1],
config.encoder('fullyconnected')))
logits.append(tf.reshape(logits[-1], shape=[batch_size, -1, 72]))
return logits[-1], tf.fill([batch_size], temp_shape[1])
def compute_logits(inputs, batch_size=-1):
#
# logits.append(FullyConnected.build_model(logits[-1], 'fullyconnected'))
#
temp_shape = inputs.get_shape().as_list()
logits = list([
BiDirectionLSTM.build_model(inputs, config.encoder('bidirection_lstm'))
])
logits.append(
tf.reshape(logits[-1], shape=[-1, logits[-1].get_shape()[-1]]))
logits.append(
FullyConnected.build_model(logits[-1],
config.encoder('fullyconnected_2')))
logits.append(tf.reshape(logits[-1], shape=[batch_size, -1, 72]))
return logits[-1], tf.fill([batch_size], temp_shape[1])
def compute_logits(inputs, batch_size=-1):
#
# logits.append(FullyConnected.build_model(logits[-1], 'fullyconnected'))
#
# logits.append(CNN.build_model(logits[-1], 'cnn'))
# temp_shape = logits[-1].get_shape()
# logits.append(tf.reshape(logits[-1], shape=[-1, temp_shape[1] * temp_shape[2] * temp_shape[3]]))
# logits.append(BottleNeck.build_model(logits[-1], 'bottleneck'))
# logits.append(FullyConnected.build_model(logits[-1], 'fullyconnected'))
logits = BiDirectionLSTM.build_model(inputs,
config.encoder('bidirection_lstm'))
logits = tf.reshape(logits, shape=[-1, logits.get_shape()[-1]])
logits = FullyConnected.build_model(logits,
config.encoder('fullyconnected'))
logits = tf.reshape(logits, shape=[batch_size, -1, 72])
return logits
def compute_logits(inputs, batch_size=-1):
logits = [inputs]
#
# logits.append(FullyConnected.build_model(logits[-1], 'fullyconnected'))
#
# logits.append(CNN.build_model(logits[-1], 'cnn'))
# temp_shape = logits[-1].get_shape()
# logits.append(tf.reshape(logits[-1], shape=[-1, temp_shape[1] * temp_shape[2] * temp_shape[3]]))
# logits.append(BottleNeck.build_model(logits[-1], 'bottleneck'))
# logits.append(FullyConnected.build_model(logits[-1], 'fullyconnected'))
logits.append(LSTM.build_model(logits[-1], 'lstm'))
logits.append(tf.reshape(logits[-1], shape=[-1, logits[-1].get_shape()[-1]]))
# logits.append(BottleNeck.build_model(logits[-1], 'bottleneck'))
# log(logits[-1].get_shape())
logits.append(FullyConnected.build_model(logits[-1], 'fullyconnected'))
logits.append(tf.reshape(logits[-1], shape=[batch_size, -1, 72]))
logits.append(logits[-1])
return logits[-1]