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