# Convolutional Neural Network (CNN) using TensorFlow on MNIST dataset

In [1]:

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

In [2]:

```
import tensorflow as tf
sess = tf.InteractiveSession()
```

In [3]:

```
# Define functions for weights and bias initialization
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
```

In [4]:

```
# Define CNN and max_pool operations
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding='SAME')
```

In [5]:

```
# Input, output placeholders
x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])
# First cnn layer
W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])
x_image = tf.reshape(x, [-1,28,28,1])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
```

In [6]:

```
# 2nd CNN layer
# 64 features with 5x5 filter
W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
```

In [7]:

```
# FC 1 with Relu
W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
```

In [8]:

```
# Dropout
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
```

In [9]:

```
# Last FC layer
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
```

In [12]:

```
# Softmax classifier
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv, y_))
# Adam updater
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
# Calculate accuracy
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Initialize variables
sess.run(tf.global_variables_initializer())
# Training
iterations = 20000
batch_size = 50
dropout = 0.5
for i in range(iterations):
batch = mnist.train.next_batch(batch_size)
if i%100 == 0:
train_accuracy = accuracy.eval(feed_dict={
x:batch[0], y_: batch[1], keep_prob: 1.0})
print("step %d, training accuracy %g"%(i, train_accuracy))
train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: dropout})
#print("test accuracy %g"%accuracy.eval(feed_dict={
# x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))
```

In [14]:

```
# 10000 test cases are too big for laptop GPU, split it into two pieces to evalute
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images[:5000], y_: mnist.test.labels[:5000], keep_prob: 1.0}))
```

In [15]:

```
print("test accuracy %g"%accuracy.eval(feed_dict={
x: mnist.test.images[5000:], y_: mnist.test.labels[5000:], keep_prob: 1.0}))
```

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