rawCNN.py

# MNIST dataset classification using Raw CNN.
from __future__ import print_function

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

import tensorflow as tf

# Set up Parameters
learning_rate = 0.1
num_steps = 500
batch_size = 128
display_step = 10

# Network Parameters
n_hidden_1 = 256
n_hidden_2 = 256
num_input = 784
num_classes = 10

# Graph Input Layer
X = tf.placeholder(tf.float32,[None,num_input])
Y = tf.placeholder(tf.float32,[None,num_classes])

# Weights and Biases
weights = {
    'h1' : tf.Variable(tf.random_normal([num_input,n_hidden_1])),
    'h2' : tf.Variable(tf.random_normal([n_hidden_1,n_hidden_2])),
    'out' : tf.Variable(tf.random_normal([n_hidden_2,num_classes]))
}
biases = {
    'b1' : tf.Variable(tf.random_normal([n_hidden_1])),
    'b2' : tf.Variable(tf.random_normal([n_hidden_2])),
    'out' : tf.Variable(tf.random_normal([num_classes]))
}

# Model
def neural_net(x):
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    out_layer = tf.add(tf.matmul(layer_2,weights['out']),biases['out'])
    return out_layer

# use the model
logits = neural_net(X)
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits,labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)

correct_pred = tf.equal(tf.argmax(logits,1),tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))

# Initialise Variables
init = tf.global_variables_initializer()

# Start Training

with tf.Session() as sess:
    sess.run(init)

    # get batches
    for i in range(1,num_steps+1):
        batch_x,batch_y = mnist.train.next_batch(batch_size)
        # Run optimization op for Backpropogation
        sess.run(train_op,feed_dict={X:batch_x,Y:batch_y})
        if i%display_step == 0 or i == 1:
            loss,acc = sess.run([loss_op,accuracy],feed_dict={X:batch_x,Y:batch_y})
            print("Step " + str(i) + ", Minibatch Loss= " + "{:.4f}".format(loss) + ", Training Accuracy= " + "{:.3f}".format(acc))
    print('Done Optimisation')
    print('Testing accuracy :',sess.run(accuracy,feed_dict={X:mnist.test.images,Y:mnist.test.labels}))