Keras使用Lambda图层错误和K.ctc_decode [英] Keras using Lambda layers error with K.ctc_decode

问题描述

当涉及到CTC功能时，Keras似乎为您做了很多繁重的工作.但是我发现构建一个我不想作为我的神经网络的一部分运行的解码函数很棘手.我有一个自定义函数，该函数在纪元末期执行，然后循环访问所有测试数据并评估指标，我目前正在手动执行此操作，但想使用k.ctc_decode函数(贪心和梁)我发现很难访问并将其合并到我的自定义功能中.

It appears that Keras has done alot of the heavy lifting for you when it comes to the CTC function. However I am finding it tricky to build a decode function which I don't want to run as part of my neural network. I have a custom function that is executed on epoch end which I then iterate through all my test data and evaluate the metrics, I am currently doing this by hand but want to make use of the k.ctc_decode function (both greedy and beam) however I am finding it hard to access and incorporate into my custom function.

我有一个模特:

 # Define CTC loss
    def ctc_lambda_func(args):
        y_pred, labels, input_length, label_length = args
        return K.ctc_batch_cost(labels, y_pred, input_length, label_length)

def ctc_decode(args):
     y_pred, input_length =args
     seq_len = tf.squeeze(input_length,axis=1)

     return K.ctc_decode(y_pred=y_pred, input_length=seq_len, greedy=True, beam_width=100, top_paths=1)

input_data = Input(name='the_input', shape=(None,mfcc_features))  
x = TimeDistributed(Dense(fc_size, name='fc1', activation='relu'))(input_data) 
y_pred = TimeDistributed(Dense(num_classes, name="y_pred", activation="softmax"))(x)

labels = Input(name='the_labels', shape=[None,], dtype='int32')
input_length = Input(name='input_length', shape=[1], dtype='int32')
label_length = Input(name='label_length', shape=[1], dtype='int32')

loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')([y_pred,labels,input_length,label_length])

dec = Lambda(ctc_decode, output_shape=[None,], name='decoder')([y_pred,input_length])

model = Model(inputs=[input_data, labels, input_length, label_length], outputs=[loss_out])



iterate = K.function([input_data, K.learning_phase()], [y_pred])
decode = K.function([y_pred, input_length], [dec])

当前错误是:

dec = Lambda(ctc_decode，name ='decoder')([y_pred，input_length])文件 "/home/rob/py27/local/lib/python2.7/site-packages/keras/engine/topology.py"， 第604行，在致电中 output_shape = self.compute_output_shape(input_shape)文件"/home/rob/py27/local/lib/python2.7/site-packages/keras/layers/core.py"， 第631行，在compute_output_shape中 返回K.int_shape(x)文件"/home/rob/py27/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py"， 第451行，int_shape shape = x.get_shape()AttributeError:元组"对象没有属性"get_shape"

dec = Lambda(ctc_decode, name='decoder')([y_pred,input_length]) File "/home/rob/py27/local/lib/python2.7/site-packages/keras/engine/topology.py", line 604, in call output_shape = self.compute_output_shape(input_shape) File "/home/rob/py27/local/lib/python2.7/site-packages/keras/layers/core.py", line 631, in compute_output_shape return K.int_shape(x) File "/home/rob/py27/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 451, in int_shape shape = x.get_shape() AttributeError: 'tuple' object has no attribute 'get_shape'

有什么想法我该怎么做?

Any ideas how I can do this?

推荐答案

一个棘手的部分是K.ctc_decode返回单个张量列表的元组，而不是单个张量，因此您不能直接创建层.而是尝试使用K.function:

One tricky part is that K.ctc_decode returns tuple of single list of tensors, not a single tensor, so you can't create a layer straightforwardly. Instead try creating a decoder with K.function:

top_k_decoded, _ = K.ctc_decode(y_pred, input_lengths)
decoder = K.function([input_data, input_lengths], [top_k_decoded[0]])

稍后您可以致电您的解码器:

Later you can call your decoder:

decoded_sequences = decoder([test_input_data, test_input_lengths])

您可能需要重新整形，因为K.ctc_decoder要求长度的形状像(样本)，而长度张量是形状(样本1).

You may need some reshaping, as K.ctc_decoder requires lengths to have shape like (samples), while the lengths tensor was of shape (samples, 1).

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