bawn.py 文件源码

def run_semi_online_v2(sess,
                       out_ops,
                       skips_noisy_batch, 
                       indices, 
                       inputs_noisy, 
                       num_samples):
    skips_noisy_sum = sess.run(skips_noisy_batch)
    predictions_ = []
    for step in xrange(num_samples):
        feed_dict = feed_dict={self.inputs_clean: indices,
                               self.skips_noisy: skips_noisy_sum[:,:,step]}
        output_dist = sess.run([out_ops], feed_dict=feed_dict)[0]
        #output dim = 1 x 256, it is 2D but we need 1D input to argmax
        indices = random_bins(NUM_CLASSES, output_dist)
        inputs = self.bins[indices]
        #inputs = np.array(np.matmul(output_dist,self.bins), dtype=np.float32)[:,None]
        #indices = np.digitize(inputs[:,0], self.bins, right=False)[:,None]
        predictions_.append(inputs)