Seq2Seq_model_for_TextSummarizer-600L.py 文件源码

def loop_fn_transition(time,previous_output,previous_state,previous_loop_state):
    #print time
    elements_finished = (time >= decoder_lengths)
    def next_input():
        prev_out_with_weights = tf.matmul(previous_output,w['score'])
        prev_out_with_weights = tf.reshape(prev_out_with_weights,[-1,final_hidden_units,1])
        score = tf.matmul(encoder_outputs,prev_out_with_weights)
        score = tf.reshape(score,[-1,num_steps])
        attention = tf.nn.softmax(score)
        attention = tf.reshape(attention,[-1,1,num_steps])
        ct = tf.matmul(attention,encoder_outputs)
        ct = tf.reshape(ct,[-1,final_hidden_units])
        ctht = tf.concat((ct,previous_output),1)
        ht_dash = tf.nn.tanh(tf.add(tf.matmul(ctht,w['hdash']),b['hdash']))
        pred = tf.nn.softmax(tf.add(tf.matmul(ctht,w['decoder']),b['decoder']))
        prediction = tf.argmax(pred,axis=1)
        inputn = tf.nn.embedding_lookup(embeddings,prediction)
        return inputn
    finished = tf.reduce_all(elements_finished)
    next_input = tf.cond(finished,lambda:pad_embedded,next_input)
    state = previous_state
    output = previous_output
    #print output.shape
    loop_state = None
    return (elements_finished,
            next_input,
            state,
            output,
            loop_state)


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