def inference(x, sequence_len, training, full_sequence_len, rnn_layer_num=3):
"""Infer a logits of the input signal batch.
Args:
x: Tensor of shape [batch_size, max_time,channel], a batch of the input signal with a maximum length `max_time`.
sequence_len: Tensor of shape [batch_size], given the real lenghs of the segments.
training: Placeholder of Boolean, Ture if the inference is during training.
full_sequence_len: Scalar float, the maximum length of the sample in the batch.
rnn_layer_num:Scalar Int, default is 3, the number of layer of RNN in the network.
Returns:
logits: Tensor of shape [batch_size, max_time, class_num]
ratio: Scalar float, the scale factor between the output logits and the input maximum length.
"""
cnn_feature = getcnnfeature(x, training=training)
feashape = cnn_feature.get_shape().as_list()
ratio = full_sequence_len / feashape[1]
if rnn_layer_num == 0:
logits = getcnnlogit(cnn_feature)
else:
logits = rnn_layers(cnn_feature,
sequence_len,
training,
layer_num=rnn_layer_num)
#logits = cudnn_rnn(cnn_feature,rnn_layer_num)
return logits, ratio
def inference(x,
sequence_len,
training,
full_sequence_len,
configure,
apply_ratio=False):
"""Infer a logits of the input signal batch.
Args:
x: Tensor of shape [batch_size, max_time,channel], a batch of the input signal with a maximum length `max_time`.
sequence_len: Tensor of shape [batch_size], given the real lenghs of the segments.
training: Placeholder of Boolean, Ture if the inference is during training.
full_sequence_len: Scalar float, the maximum length of the sample in the batch.
configure:Model configuration.
apply_ratio: If apply the ration to the sequence_len or not.
Returns:
logits: Tensor of shape [batch_size, max_time, class_num]
ratio: Scalar float, the scale factor between the output logits and the input maximum length.
"""
cnn_feature = getcnnfeature(x,
training=training,
cnn_config=configure['cnn'])
feashape = cnn_feature.get_shape().as_list()
ratio = full_sequence_len / feashape[1]
if apply_ratio:
sequence_len = tf.cast(
tf.ceil(tf.cast(sequence_len, tf.float32) / ratio), tf.int32)
if configure['rnn']['layer_num'] == 0:
logits = getcnnlogit(cnn_feature)
elif configure['rnn']['layer_type'] == 'rna':
logits = rnn_layers_rna(cnn_feature,
sequence_len,
training,
layer_num=configure['rnn']['layer_num'],
hidden_num=configure['rnn']['hidden_num'],
cell=configure['rnn']['cell_type'])
else:
logits = rnn_layers(cnn_feature,
sequence_len,
training,
layer_num=configure['rnn']['layer_num'],
hidden_num=configure['rnn']['hidden_num'],
cell=configure['rnn']['cell_type'])
#logits = cudnn_rnn(cnn_feature,rnn_layer_num)
return logits, ratio