擅长:python、mysql、java
<p>最后我想出了一个代码:</p>
<pre><code>from keras import backend as K
import tensorflow as tf
def mask_output2(x):
inp, soft_out = x
# add a very small value in order to avoid having 0 everywhere
c = K.constant(0.0000001, dtype='float32', shape=(32, 13))
y = soft_out + c
y = Lambda(lambda x: K.switch(K.equal(x[0],0), x[1], K.zeros_like(x[1])))([inp, soft_out])
y_sum = K.sum(y, axis=-1)
y_sum_corrected = Lambda(lambda x: K.switch(K.equal(x[0],0), K.ones_like(x[0]), x[0] ))([y_sum])
y_sum_corrected = tf.divide(1,y_sum_corrected)
y = tf.einsum('ij,i->ij', y, y_sum_corrected)
return y
</code></pre>