python之从 tensorflow 中获取预测结果

我正在使用以下代码做我的第一个 tensorflow 示例。

train_x,train_y,test_x,test_y=create_feature_sets_and_labels('pro.txt','neg.txt') 
n_nodes_hl1 = 1500 
n_nodes_hl2 = 1500 
n_nodes_hl3 = 1500 
 
n_classes = 2 
batch_size = 100 
hm_epochs = 7 
 
x = tf.placeholder('float') 
y = tf.placeholder('float') 
 
hidden_1_layer = {'f_fum':n_nodes_hl1, 
              'weight':tf.Variable(tf.random_normal([len(train_x[0]), n_nodes_hl1])), 
              'bias':tf.Variable(tf.random_normal([n_nodes_hl1]))} 
 
hidden_2_layer = {'f_fum':n_nodes_hl2, 
              'weight':tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])), 
              'bias':tf.Variable(tf.random_normal([n_nodes_hl2]))} 
 
hidden_3_layer = {'f_fum':n_nodes_hl3, 
              'weight':tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])), 
              'bias':tf.Variable(tf.random_normal([n_nodes_hl3]))} 
 
output_layer = {'f_fum':None, 
            'weight':tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])), 
            'bias':tf.Variable(tf.random_normal([n_classes])),} 
 
 
def neural_network_model(data): 
 
    l1 = tf.add(tf.matmul(data,hidden_1_layer['weight']), hidden_1_layer['bias']) 
    l1 = tf.nn.relu(l1) 
 
    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weight']), hidden_2_layer['bias']) 
    l2 = tf.nn.relu(l2) 
 
    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weight']), hidden_3_layer['bias']) 
    l3 = tf.nn.relu(l3) 
 
    output = tf.matmul(l3,output_layer['weight']) + output_layer['bias'] 
 
    return output 
 
def train_neural_network(x): 
    prediction = neural_network_model(x) 
 
    cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) ) 
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost) 
 
    with tf.Session() as sess: 
            sess.run(tf.initialize_all_variables()) 
 
            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+1, 'completed out of',hm_epochs,'loss:',epoch_l$                correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1)) 
            accuracy = tf.reduce_mean(tf.cast(correct, 'float')) 
            print(y) 
            print('Accuracy:',accuracy.eval({x:test_x, y:test_y})) 
 
 
train_neural_network(x) 

它给我测试数据的准确性。 我想要的是给我的训练模型输入一个句子，它会返回我预测的标签。

我试着按照这个 example 的形式

#with same length as lexicon                
input = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.$  
output = sess.run(y, feed_dict={x :input}) 

它给了我以下错误。

You must feed a value for placeholder tensor 'Placeholder_1' with dtype float 
     [[Node: Placeholder_1 = Placeholder[dtype=DT_FLOAT, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]] 

请您参考如下方法:

session.run() 的第一个参数应该是您想要获取的张量。

在您的情况下，它应该是 prediction 张量(因此您需要从 train_neural_network 返回它)。对其应用 argmax 以获得预测标签。