lab-10-X1-mnist_back_prop.py

# http://blog.aloni.org/posts/backprop-with-tensorflow/
# https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b#.b3rvzhx89
import tensorflow as tf

tf.set_random_seed(777)  # reproducibility

# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist dataset
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])

w1 = tf.Variable(tf.truncated_normal([784, 30]))
b1 = tf.Variable(tf.truncated_normal([1, 30]))
w2 = tf.Variable(tf.truncated_normal([30, 10]))
b2 = tf.Variable(tf.truncated_normal([1, 10]))


def sigma(x):
    #  sigmoid function
    return tf.div(tf.constant(1.0),
                  tf.add(tf.constant(1.0), tf.exp(-x)))


def sigma_prime(x):
    # derivative of the sigmoid function
    return sigma(x) * (1 - sigma(x))

# Forward prop
l1 = tf.add(tf.matmul(X, w1), b1)
a1 = sigma(l1)
l2 = tf.add(tf.matmul(a1, w2), b2)
y_pred = sigma(l2)

# diff
assert y_pred.shape.as_list() == Y.shape.as_list()
diff = (y_pred - Y)


# Back prop (chain rule)
d_l2 = diff * sigma_prime(l2)
d_b2 = d_l2
d_w2 = tf.matmul(tf.transpose(a1), d_l2)

d_a1 = tf.matmul(d_l2, tf.transpose(w2))
d_l1 = d_a1 * sigma_prime(l1)
d_b1 = d_l1
d_w1 = tf.matmul(tf.transpose(X), d_l1)


# Updating network using gradients
learning_rate = 0.5
step = [
    tf.assign(w1, w1 - learning_rate * d_w1),
    tf.assign(b1, b1 - learning_rate *
              tf.reduce_mean(d_b1, reduction_indices=[0])),
    tf.assign(w2, w2 - learning_rate * d_w2),
    tf.assign(b2, b2 - learning_rate *
              tf.reduce_mean(d_b2, reduction_indices=[0]))
]

# 7. Running and testing the training process
acct_mat = tf.equal(tf.argmax(y_pred, 1), tf.argmax(Y, 1))
acct_res = tf.reduce_sum(tf.cast(acct_mat, tf.float32))

sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())

for i in range(10000):
    batch_xs, batch_ys = mnist.train.next_batch(10)
    sess.run(step, feed_dict={X: batch_xs,
                              Y: batch_ys})
    if i % 1000 == 0:
        res = sess.run(acct_res, feed_dict={X: mnist.test.images[:1000],
                                            Y: mnist.test.labels[:1000]})
        print(res)

# 8. Automatic differentiation in TensorFlow
cost = diff * diff
step = tf.train.GradientDescentOptimizer(0.1).minimize(cost)