我正在按照这个教程进行学习：https://pythonprogramming.net/train-test-tensorflow-deep-learning-tutorial/

我已经能够训练神经网络并打印出准确率。

然而，我不知道如何使用神经网络进行预测。

这是我的尝试。具体问题在于这一行 – 我认为我的问题是我无法将输入字符串转换为模型期望的格式：

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)))

这里是更大的代码列表：

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)

回答：

根据你上面的评论，你的错误ValueError: Cannot feed value of shape ()似乎是因为features是None，因为你的函数get_features_for_input没有返回任何内容。

我添加了return features行，并将features调整为正确的形状[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