Function test

a02_TextCNN/p7_TextCNN_model.py:206–233

()

#e.g. given inputs:[1,0,1,1,0]; outputs:[0,1,1,1,0].
#invoke test() below to test the model in this toy task.
def test():
    #below is a function test; if you use this for text classifiction, you need to transform sentence to indices of vocabulary first. then feed data to the graph.
    num_classes=5
    learning_rate=0.001
    batch_size=8
    decay_steps=1000
    decay_rate=0.95
    sequence_length=5
    vocab_size=10000
    embed_size=100
    is_training=True
    dropout_keep_prob=1.0 #0.5
    filter_sizes=[2,3,4]
    num_filters=128
    multi_label_flag=True
    textRNN=TextCNN(filter_sizes,num_filters,num_classes, learning_rate, batch_size, decay_steps, decay_rate,sequence_length,vocab_size,embed_size,is_training,multi_label_flag=multi_label_flag)
    with tf.Session() as sess:
       sess.run(tf.global_variables_initializer())
       for i in range(500):
           input_x=np.random.randn(batch_size,sequence_length) #[None, self.sequence_length]
           input_x[input_x>=0]=1
           input_x[input_x <0] = 0
           input_y_multilabel=get_label_y(input_x)
           loss,possibility,W_projection_value,_=sess.run([textRNN.loss_val,textRNN.possibility,textRNN.W_projection,textRNN.train_op],
                                                    feed_dict={textRNN.input_x:input_x,textRNN.input_y_multilabel:input_y_multilabel,
                                                               textRNN.dropout_keep_prob:dropout_keep_prob,textRNN.tst:False})
           print(i,"loss:",loss,"-------------------------------------------------------")
           print("label:",input_y_multilabel);#print("possibility:",possibility)

def get_label_y(input_x):
    length=input_x.shape[0]