Регрессия нейронной сети в тензорном потоке: ошибка в коде

Я не понимаю, почему мой код не запускается. Я начал с учебника TensorFlow, чтобы классифицировать изображения в наборе данных mnist с помощью однослойной нейронной сети с прямой связью. Затем модифицировал код, чтобы создать многослойный персептрон, который отображает 37 входных данных в 1 выходной. Входные и выходные данные обучения загружаются из файла данных Matlab (.mat)

Вот мой код..

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from scipy.io import loadmat
%matplotlib inline
import tensorflow as tf
from tensorflow.contrib import learn

import sklearn
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from warnings import filterwarnings
filterwarnings('ignore')
sns.set_style('white')
from sklearn import datasets
from sklearn.preprocessing import scale
from sklearn.cross_validation import train_test_split
from sklearn.datasets import make_moons

X = np.array(loadmat("Data/DataIn.mat")['TrainingDataIn'])
Y = np.array(loadmat("Data/DataOut.mat")['TrainingDataOut'])

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)
total_len = X_train.shape[0]

# Parameters
learning_rate = 0.001
training_epochs = 500
batch_size = 10
display_step = 1
dropout_rate = 0.9
# Network Parameters
n_hidden_1 = 19 # 1st layer number of features
n_hidden_2 = 26 # 2nd layer number of features
n_input = X_train.shape[1]
n_classes = 1

# tf Graph input
X = tf.placeholder("float32", [None, 37])
Y = tf.placeholder("float32", [None])

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)

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

    # Output layer with linear activation
    out_layer = tf.matmul(layer_2, 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)),
    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], 0, 0.1)),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], 0, 0.1))
}

biases = {
    'b1': tf.Variable(tf.random_normal([n_hidden_1], 0, 0.1)),
    'b2': tf.Variable(tf.random_normal([n_hidden_2], 0, 0.1)),
    'out': tf.Variable(tf.random_normal([n_classes], 0, 0.1))
}

# Construct model
pred = multilayer_perceptron(X, weights, biases)
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))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    print ("Accuracy:", accuracy.eval({X: X_test, Y: Y_test}))

когда я запускаю код, я получаю сообщения об ошибках:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-6b8af9192775> in <module>()
     93             # Run optimization op (backprop) and cost op (to get loss value)
     94             _, c, p = sess.run([optimizer, cost, pred], feed_dict={X: batch_x,
---> 95                                                           Y: batch_y})
     96             # Compute average loss
     97             avg_cost += c / total_batch

~\AppData\Local\Continuum\Anaconda3\envs\ann\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    787     try:
    788       result = self._run(None, fetches, feed_dict, options_ptr,
--> 789                          run_metadata_ptr)
    790       if run_metadata:
    791         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~\AppData\Local\Continuum\Anaconda3\envs\ann\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    973                 'Cannot feed value of shape %r for Tensor %r, '
    974                 'which has shape %r'
--> 975                 % (np_val.shape, subfeed_t.name, str(subfeed_t.get_shape())))
    976           if not self.graph.is_feedable(subfeed_t):
    977             raise ValueError('Tensor %s may not be fed.' % subfeed_t)

ValueError: Cannot feed value of shape (10, 1) for Tensor 'Placeholder_7:0', which has shape '(?,)'