**Tensorflow MNIST Part 1 Softmax Regression**

```
In [2]:
```from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

```
```

```
In [3]:
```import matplotlib.pyplot as plt
import numpy as np

```
In [4]:
```def display_digit(x,y):
'''
Visualize x,y image/label set'''
label = y.argmax(axis=0)
image = x.reshape([28,28])
plt.title('Label: %d' % (label))
plt.imshow(image, cmap=plt.get_cmap('gray_r'))
plt.show()

```
In [5]:
```#Visualize 1st 5 Training Examples
for i in range (5):
display_digit(mnist.train.images[i],mnist.train.labels[i])

```
```

```
In [6]:
```#Import Tf and start a session
import tensorflow as tf
sess = tf.InteractiveSession()

```
In [7]:
```x = tf.placeholder(tf.float32, shape=[None, 784])
y_ = tf.placeholder(tf.float32, shape=[None, 10])

```
In [8]:
```W = tf.Variable(tf.zeros([784,10]))
b = tf.Variable(tf.zeros([10]))

```
In [9]:
```sess.run(tf.global_variables_initializer())

```
In [10]:
```y = tf.matmul(x,W) + b

```
In [11]:
```cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))

```
In [12]:
```train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

```
In [13]:
```for _ in range(1000):
batch = mnist.train.next_batch(100)
train_step.run(feed_dict={x: batch[0], y_: batch[1]})

```
In [14]:
```correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print("Test Set Accuracy :",accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

```
```

```
In [20]:
```feed_dict = {x: [mnist.test.images[2]]}
classification = sess.run(y, feed_dict)
print (classification)

```
```

```
In [21]:
```display_digit(mnist.test.images[2],mnist.test.labels[2])

```
```

**Part 2 : CNN **

```
In [ ]:
```

```
In [87]:
```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 [88]:
```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 [89]:
```W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

```
In [92]:
```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 [93]:
```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 [94]:
```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 [95]:
```keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

```
In [96]:
```W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

```
In [97]:
```cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(20000):
batch = mnist.train.next_batch(50)
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: 0.5})
print('test accuracy %g' % accuracy.eval(feed_dict={
x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

```
```

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In [ ]:
```