#!/usr/bin/env python

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

def init_weights(shape, name):

return tf.Variable(tf.random_normal(shape, stddev=0.01), name=name)

# This network is the same as the previous one except with an extra hidden layer + dropout

def model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden):

return tf.matmul(h2, w_o)

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

trX, trY, teX, teY = mnist.train.images, mnist.train.labels, mnist.test.images, mnist.test.labels

X = tf.placeholder("float", [None, 784], name="X")

Y = tf.placeholder("float", [None, 10], name="Y")

w_h = init_weights([784, 625], "w_h")

w_h2 = init_weights([625, 625], "w_h2")

w_o = init_weights([625, 10], "w_o")

# Add histogram summaries for weights

tf.summary.histogram("w_h_summ", w_h)

tf.summary.histogram("w_h2_summ", w_h2)

tf.summary.histogram("w_o_summ", w_o)

p_keep_input = tf.placeholder("float", name="p_keep_input")

p_keep_hidden = tf.placeholder("float", name="p_keep_hidden")

py_x = model(X, w_h, w_h2, w_o, p_keep_input, p_keep_hidden)

with tf.name_scope("cost"):

cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=py_x, labels=Y))

train_op = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(cost)

# Add scalar summary for cost

tf.summary.scalar("cost", cost)

with tf.name_scope("accuracy"):

correct_pred = tf.equal(tf.argmax(Y, 1), tf.argmax(py_x, 1)) # Count correct predictions

acc_op = tf.reduce_mean(tf.cast(correct_pred, "float")) # Cast boolean to float to average

# Add scalar summary for accuracy

tf.summary.scalar("accuracy", acc_op)

with tf.Session() as sess:

# create a log writer. run 'tensorboard --logdir=./logs/nn_logs'

writer = tf.summary.FileWriter("./logs/nn_logs", sess.graph) # for 1.0

merged = tf.summary.merge_all()

# you need to initialize all variables

tf.global_variables_initializer().run()

for i in range(100):

for start, end in zip(range(0, len(trX), 128), range(128, len(trX)+1, 128)):

sess.run(train_op, feed_dict={X: trX[start:end], Y: trY[start:end],

p_keep_input: 0.8, p_keep_hidden: 0.5})

summary, acc = sess.run([merged, acc_op], feed_dict={X: teX, Y: teY,

p_keep_input: 1.0, p_keep_hidden: 1.0})

writer.add_summary(summary, i) # Write summary

print(i, acc) # Report the accuracy

writer.close()