Tensorflow:使用神经网络对正面或负面短语进行分类

Question

我正在通过这里的教程:https: //pythonprogramming.net/train-test-tensorflow-deep-learning-tutorial/

我可以训练神经网络并打印出准确性.

但是,我不知道如何使用神经网络进行预测.

这是我的尝试.具体问题是这一行 - 我相信我的问题是我无法将我的输入字符串转换为模型所期望的格式:

features = get_features_for_input("This was the best store i've ever seen.")
result = (sess.run(tf.argmax(prediction.eval(feed_dict={x:features}),1)))

这是一个更大的列表:

def train_neural_network(x):
    prediction = neural_network_model(x)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y)) 
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for epoch in range(hm_epochs):
            epoch_loss = 0
            i = 0
            while i < len(train_x):
                start = i
                end = i + batch_size

                batch_x = np.array(train_x[start:end])
                batch_y = np.array(train_y[start:end])

                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})

                epoch_loss += c 
                i+=batch_size

            print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', epoch_loss)

        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y,1))        
        accuracy = tf.reduce_mean(tf.cast(correct,'float'))
        print('Accuracy', accuracy.eval({x:test_x, y:test_y}))

        # pos: [1,0] , argmax: 0
        # neg: [0,1] , argmax: 1
        features = get_features_for_input("This was the best store i've ever seen.")
        result = (sess.run(tf.argmax(prediction.eval(feed_dict={x:features}),1)))
        if result[0] == 0:
            print('Positive:',input_data)
        elif result[0] == 1:
            print('Negative:',input_data)

def get_features_for_input(input):
    current_words = word_tokenize(input.lower())
    current_words = [lemmatizer.lemmatize(i) for i in current_words]
    features = np.zeros(len(lexicon))

    for word in current_words:
        if word.lower() in lexicon:
            index_value = lexicon.index(word.lower())
            # OR DO +=1, test both
            features[index_value] += 1

    features = np.array(list(features))

train_neural_network(x)

Answer 1

按照上面的评论,感觉就像你的错误ValueError: Cannot feed value of shape ()是由于这样的事实features是None,因为你的函数get_features_for_input不返回任何东西.

我添加了return features一行,并为功能提供了正确的形状,[1, len(lexicon)]以匹配占位符的形状.

def get_features_for_input(input):
    current_words = word_tokenize(input.lower())
    current_words = [lemmatizer.lemmatize(i) for i in current_words]
    features = np.zeros((1, len(lexicon)))

    for word in current_words:
        if word.lower() in lexicon:
            index_value = lexicon.index(word.lower())
            # OR DO +=1, test both
            features[0, index_value] += 1

    return features