从张量流得到预测结果

我正在做我的第一张张量流示例，下面的代码。从张量流得到预测结果

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。我想要的是给我的火车模型输入句子，它返回我预测的标签。

我想下面的表格此example

#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})

它给了我下面的错误。

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"]()]]

来源

2016-10-28 Fazal Wahid

session.run()的第一个参数应该是你想得到的张量。

在你的情况下，它应该是prediction张量（所以你需要从你的train_neural_network返回它）。对其应用argmax以获得预测标签。

来源

2016-10-28 14:27:09

** working：** output = sess.run（tf.argmax（prediction，1），feed_dict = {x：input}） –

我建议用'numpy.argmax（output）替换'tf.argmax' '或将'tf.argmax'移动到图形定义阶段（'neaural_network_model'函数）。否则，每次运行代码行时都会添加新的argmax节点。 –

我可以得到预测输出的准确性吗？ –

从张量流得到预测结果

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