import tensorflow as tf

# Setting the placeholders

X = tf.placeholder(tf.float32, [None, 28, 28, 1])

W = tf.Variable(tf.zeros([784, 10]))

b = tf.Variable(tf.zeros([10]))

init = tf.initialize_all_variables()

# Setting the model, Y from Y = WX + b

# And we're using softmax

Y = tf.nn.softmax(tf.matmul(tf.reshape(X, [-1, 784]), W) + b)

# And these are the answers

Y_ = tf.placeholder(tf.float32, [None, 10])

# We use cross entropy for our loss function

# Reduce sum just adds all the elements of the vector

cross_entropy = -tf.reduce_sum(Y_ * tf.log(Y))

# Number of correct answers found in thebatch

is_correct = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))

# Accuracy in the model for the iteration

accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))

# Gradient descent

# Partial derivatives are calculated automatically

optimizer = tf.train.GradientDescentOptimizer(0.003)

# Size of the gradient step for each iteration - it's currently consistent

train_step = optimizer.minimize(cross_entropy)

# We need to start the session so all the Lazy setup gets executed

sess = tf.Session()

sess.run(init)

# Get the images for the actual test

from tensorflow.contrib.learn.python.learn.datasets.mnist import read_data_sets

mnist = read_data_sets("data", one_hot=True, reshape=False, validation_size=0)

# We begin the actual computation

for i in range(1000):

# We load the images in the placeholders

batch_X, batch_Y = mnist.train.next_batch(100)

# And we create our results dictionary

# Updating training data

train_data = {X: batch_X, Y_: batch_Y}

a,c = sess.run([accuracy, cross_entropy], feed_dict=train_data)

print(str(i) + ": accuracy:" + str(a) + " loss: " + str(c))

# Updating test data

test_data={X: mnist.test.images, Y_: mnist.test.labels}

a,c = sess.run([accuracy, cross_entropy], feed_dict=test_data)

print(str(i) + ": ********* epoch " + str(i*100//mnist.train.images.shape[0]+1) + " ********* test accuracy:" + str(a) + " test loss: " + str(c))

# Backpropagation training step

sess.run(train_step, feed_dict=train_data)