回归中的神经网络不能在tensorflow中学习

我修改了张量流量示例以适合我的数据，这里给出：数据

但是我的神经网络根本没有学习，我试着用不同的号码。 隐藏层，学习率和优化功能，但没有帮助。我的代码如下：

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
from tensorflow.contrib import learn
import matplotlib.pyplot as plt

from sklearn.pipeline import Pipeline
from sklearn import datasets, linear_model
from sklearn import cross_validation
from sklearn import preprocessing
import numpy as np

filename_queue = tf.train.string_input_producer(["file0.csv"])

reader = tf.TextLineReader()
key, value = reader.read(filename_queue)

# Default values, in case of empty columns. Also specifies the type of the
# decoded result.
record_defaults = [[0], [0], [0], [0]]
col1, col2, col3, col4 = tf.decode_csv(
    value, record_defaults=record_defaults)
features = tf.stack([col1, col2, col3])

with tf.Session() as sess:
  # Start populating the filename queue.
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(coord=coord)

  x = np.zeros(shape=(1813,3))
  y = np.zeros(shape=(1813))

  for i in range(1813):
    # Retrieve a single instance:
    x1, y1 = sess.run([features, col4])
    x[i] = x1
    y[i] = y1

  coord.request_stop()
  coord.join(threads)

#standard_scaler = preprocessing.StandardScaler()
#x = standard_scaler.fit_transform(x)

# Split in test and train data

X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(x, y, test_size=0.2)
total_len = X_train.shape[0]

# Parameters
learning_rate = 0.001
training_epochs = 500
batch_size = 5
display_step = 1

# Network Parameters
n_hidden_1 = 50
n_input = X_train.shape[1]
n_classes = 1

# tf Graph input
x = tf.placeholder("float", [None, 3])
y = tf.placeholder("float", [None])

# Create model
def multilayer_perceptron(x, weights, biases):
    # Hidden layer with RELU activation
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_1 = tf.nn.relu(layer_1)

    # Output layer with linear activation
    out_layer = tf.matmul(layer_1, weights['out']) + biases['out']
    return out_layer

# Store layers weight & bias
weights = {
    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1], 0, 0.1)),
    'out': tf.Variable(tf.random_normal([n_hidden_1, n_classes], 0, 0.1))
}
biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1], 0, 0.1)),
    'out': tf.Variable(tf.random_normal([n_classes], 0, 0.1))
}

# Construct model
pred = multilayer_perceptron(x, weights, biases)
#reshape(pred, [-1])
tf.shape(pred)
tf.shape(y)
print("Prediction matrix:", pred)
print("Output matrix:", y)

# Define loss and optimizer
cost = tf.reduce_mean(tf.square(pred-y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)

# Launch the graph
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    # Training cycle
    for epoch in range(training_epochs):
        avg_cost = 0.
        total_batch = int(total_len/batch_size)

        print(total_batch)

        # Loop over all batches
        for i in range(total_batch-1):
            batch_x = X_train[i*batch_size:(i+1)*batch_size]
            batch_y = Y_train[i*batch_size:(i+1)*batch_size]
            # Run optimization op (backprop) and cost op (to get loss value)
            _, c, p = sess.run([optimizer, cost, pred], feed_dict={x: batch_x,
                                                          y: batch_y})
            # Compute average loss
            avg_cost += c / total_batch

        # sample prediction
        label_value = batch_y
        estimate = p
        err = label_value-estimate
        print ("num batch:", total_batch)

        # Display logs per epoch step
        if epoch % display_step == 0:
            print ("Epoch:", '%04d' % (epoch+1), "cost=", 
                "{:.9f}".format(avg_cost))
            print ("[*]----------------------------")
            for i in xrange(5):
                print ("label value:", label_value[i], 
                    "estimated value:", estimate[i])
            print ("[*]============================")

    print ("Optimization Finished!")

    # Test model
    correct_prediction = tf.equal(tf.argmax(pred), tf.argmax(y))
    # Calculate accuracy
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print ("Accuracy:", accuracy.eval({x: X_test, y: Y_test}))

结果如下所示：（标签值=预期结果）

结果

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