tensorflow cnn

import tensorflow as tf
import numpy as np

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('/tmp/', one_hot=True)

n_output_layer = 10

def convolutional_neural_network(data):
  weights = {
    'w_conv1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
    'w_conv2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
    'w_fc': tf.Variable(tf.random_normal([7 * 7 * 64, 1024])),
    'out': tf.Variable(tf.random_normal([1024, n_output_layer]))
  }
  biases = {
    'b_conv1': tf.Variable(tf.random_normal([32])),
    'b_conv2': tf.Variable(tf.random_normal([64])),
    'b_fc': tf.Variable(tf.random_normal([1024])),
    'out': tf.Variable(tf.random_normal([n_output_layer]))
  }
  data = tf.reshape(data, [-1, 28, 28, 1])

  conv1 = tf.nn.relu(tf.add(tf.nn.conv2d(data, weights['w_conv1'], strides=[1, 1, 1, 1], padding='SAME'), biases['b_conv1']))
  conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

  conv2 = tf.nn.relu(tf.add(tf.nn.conv2d(conv1, weights['w_conv2'], strides=[1, 1, 1, 1], padding='SAME'), biases['b_conv2']))
  conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

  fc = tf.reshape(conv2, [-1, 7 * 7  * 64])
  fc = tf.nn.relu(tf.add(tf.matmul(fc, weights['w_fc']), biases['b_fc']))

  # fc = tf.nn.dropout(fc, 0.8)
  
  output = tf.add(tf.matmul(fc, weights['out']), biases['out'])
  return output

batch_size = 100

X = tf.placeholder('float', [None, 28 * 28])
Y = tf.placeholder('float')

def train_neural_network(X, Y):
  predict = convolutional_neural_network(X)
  cost_func = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=predict, labels=Y))
  optimizer = tf.train.AdamOptimizer().minimize(cost_func)

  epochs = 1
  with tf.Session() as session:
    session.run(tf.global_variables_initializer())
    epoch_loss = 0
    for epoch in range(epochs):
      for i in range(int(mnist.train.num_examples / batch_size)):
        x, y = mnist.train.next_batch(batch_size)
        _, c = session.run([optimizer, cost_func], feed_dict={X: x, Y: y})
        epoch_loss += c
      print(epoch, ' : ', epoch_loss)

    correct = tf.equal(tf.argmax(predict, 1), tf.argmax(Y, 1))
    acurracy = tf.reduce_mean(tf.cast(correct, 'float'))
    print('acurracy: ', acurracy.eval({
      X: mnist.test.images,
      Y: mnist.test.labels
    }))

train_neural_network(X, Y)

result:
(0, ' : ', 1624912.0736694336)
('acurracy: ', 0.9483)