p71_TextRCNN_model.py 文件源码

def test():
    #below is a function test; if you use this for text classifiction, you need to tranform sentence to indices of vocabulary first. then feed data to the graph.
    num_classes=10
    learning_rate=0.01
    batch_size=8
    decay_steps=1000
    decay_rate=0.9
    sequence_length=5
    vocab_size=10000
    embed_size=100
    is_training=True
    dropout_keep_prob=1#0.5
    textRNN=TextRCNN(num_classes, learning_rate, batch_size, decay_steps, decay_rate,sequence_length,vocab_size,embed_size,is_training)
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(100):
            input_x=np.zeros((batch_size,sequence_length)) #[None, self.sequence_length]
            input_y=input_y=np.array([1,0,1,1,1,2,1,1]) #np.zeros((batch_size),dtype=np.int32) #[None, self.sequence_length]
            loss,acc,predict,_=sess.run([textRNN.loss_val,textRNN.accuracy,textRNN.predictions,textRNN.train_op],
                                        feed_dict={textRNN.input_x:input_x,textRNN.input_y:input_y,textRNN.dropout_keep_prob:dropout_keep_prob})
            print("loss:",loss,"acc:",acc,"label:",input_y,"prediction:",predict)
#test()