Question

我正在使用以下代码执行我的第一个张量流示例。

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。我想要的是给我的火车模型一个输入句子，它返回我预测的标签。

我尝试了以下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"]()]]

Answer 1

session.run()的第一个参数应该是你想得到的张量。

在您的情况下，它应该是prediction张量（因此您需要从train_neural_network返回）。将argmax应用于其中以获得预测标签。

从tensorflow

1 个答案: