84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
#!/usr/bin/env python3
|
||
# -*- coding: utf-8 -*-
|
||
'''
|
||
本代码用来实现神经网络中的CTC层
|
||
CTC层即：Connectionist Temporal Classification （连续型短时分类）
|
||
将这里实现的
|
||
尚未完成
|
||
'''
|
||
|
||
from keras.layers.core import Layer
|
||
from keras.engine import InputSpec
|
||
from keras import backend as K
|
||
|
||
try:
|
||
from keras import initializations
|
||
except ImportError:
|
||
from keras import initializers as initializations
|
||
|
||
import tensorflow as tf
|
||
|
||
# 继承父类Layer
|
||
class ctc_layer(Layer):
|
||
'''
|
||
本类是对CTC层的实现，具体请去参考下论文...
|
||
tensorflow中和keras中有ctc的一些实现，
|
||
并将其通过自定义层加入到keras创建的神经网络层中
|
||
|
||
参数：
|
||
output_dim: 每一条时间序列中，输出的标签序列张量的尺寸长度
|
||
|
||
|
||
目前可能有bug
|
||
'''
|
||
def __init__(self, output_dim, batch_size, **kwargs):
|
||
'''
|
||
这里是神经网络CTC层的初始化模块
|
||
'''
|
||
#if 'input_shape' not in kwargs and 'input_dim' in kwargs:
|
||
# kwargs['input_shape'] = (kwargs.pop('input_dim'), kwargs.pop('input_dim'),)
|
||
super(ctc_layer, self).__init__(**kwargs)
|
||
#self.input_dim = input_dim
|
||
#self.input_spec = [InputSpec(dtype=(None,,output_dim),ndim=3, axes={None: input_dim})]
|
||
self.output_dim = output_dim
|
||
self.batch_size = batch_size
|
||
#self.input_spec = InputSpec(min_ndim=3)
|
||
#super(ctc_layer, self).build(input_shape) # Be sure to call this somewhere!
|
||
pass
|
||
|
||
def build(self, input_shape):
|
||
assert len(input_shape) >= 2
|
||
#input_dim = input_shape[-1]
|
||
# Create a trainable weight variable for this layer.
|
||
self.kernel = self.add_weight(name='kernel',
|
||
shape=(input_shape[1], input_shape[2]),
|
||
initializer='uniform',
|
||
trainable=True)
|
||
|
||
#super(ctc_layer, self).build(input_shape) # Be sure to call this somewhere!
|
||
#self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim})
|
||
self.input_spec = [InputSpec(min_ndim=3)] # , axes={1: 748, -1: self.output_dim}
|
||
#self.built = True
|
||
self.built = False
|
||
|
||
def call(self, x, mask=None):
|
||
#output = K.dot(x, self.kernel)
|
||
output = x
|
||
|
||
#output.shape[0] = self.batch_size
|
||
decoded_dense, log_prob = K.ctc_decode(output,tf.Variable((output.shape[1],output.shape[2]),dtype=tf.int64))
|
||
#decoded_dense, log_prob = K.ctc_decode(output,output.shape[1])
|
||
#decoded_sequence = K.ctc_label_dense_to_sparse(decoded_dense, len(decoded_dense))
|
||
#return decoded_sequence
|
||
return decoded_dense
|
||
|
||
def get_config(self):
|
||
config = {
|
||
'output_dim': self.output_dim
|
||
}
|
||
base_config = super(ctc_layer, self).get_config()
|
||
return dict(list(base_config.items()) + list(config.items()))
|
||
|
||
def compute_output_shape(self, input_shape):
|
||
return (input_shape[0], self.output_dim, input_shape[2])
|
||
|