Python 为什么我的成本函数等于零

为什么运行此代码时成本函数等于零？我的代码怎么了

import tensorflow as tf

filename_queue = tf.train.string_input_producer(["data.csv"])

line_reader = tf.TextLineReader(skip_header_lines=0)
_, csv_row = line_reader.read(filename_queue)

record_defaults = [[1],[1.0],[1.0],[1.0],[1.0]]
out,in1,in2,in3,in4 = tf.decode_csv(csv_row, record_defaults=record_defaults)

features = tf.stack([in1,in2,in3,in4])

learning_rate = 0.6
training_epochs = 10
batch_size = 2
display_step = 1
num_examples= 10

n_hidden_1 = 10
n_hidden_2 = 10
n_input = 4
n_classes = 1

x = tf.placeholder("float", [None, n_input])
y = tf.placeholder("float", [n_classes])

def multilayer_perceptron(x, weights, biases):
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)

    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    layer_2 = tf.nn.relu(layer_2)

    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']

    return out_layer

weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1])),
    'b2': tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

prediction = multilayer_perceptron(x, weights, biases)

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(coord=coord)

    for epoch in range(training_epochs):
        avg_cost = 0
        total_batch = int(num_examples/batch_size)

        for i in range(total_batch):
            batch_x = []
            batch_y = []
            for _ in range(1, batch_size):
                example, label = sess.run([features, out])
                batch_x.append(example)
                batch_y.append(label)
                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,
                                                              y: batch_y})
                avg_cost += c/total_batch

        if epoch % display_step == 0:
            print ("Epoch:", '%04d' % (epoch+1), "cost=", \
                          "{:.9f}".format(avg_cost))
    print ("Optimization Finished!")
    coord.request_stop()
    coord.join(threads)

data.csv文件：

结果：

历元：0001成本=0.000000000
历元：0002成本=0.000000000
历元：0003成本=0.000000000
历元：0004成本=0.000000000
历元：0005成本=0.000000000
历元：0006成本=0.000000000
历元：0007成本=0.000000000
历元：0008成本=0.000000000
历元：0009成本=0.000000000
历元：0010成本=0.000000000
优化完成了

---

会话返回的c值实际上等于零

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

---

您确定tensorflow正在正确执行吗？

但为什么？运行feed_dict和optimizer后，要求c的值等于成本函数计算输出的值。不是吗？

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