'''

A logistic regression learning algorithm example using TensorFlow library.

This example is using the MNIST database of handwritten digits

(http://yann.lecun.com/exdb/mnist/)

Author: Aymeric Damien

Project: https://github.com/aymericdamien/TensorFlow-Examples/

'''

from __future__ import print_function

import tensorflow as tf

# Import MNIST data

from tensorflow.examples.tutorials.mnist import input_data

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

# Parameters

learning_rate = 0.01

training_epochs = 10

batch_size = 100

display_step = 1

# tf Graph Input

x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784

y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes

# Set model weights

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

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

# Construct model

pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax

# Minimize error using cross entropy

cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))

# Gradient Descent

optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# Initialize the variables (i.e. assign their default value)

init = tf.global_variables_initializer()

# Start training

with tf.Session() as sess:

# Run the initializer

sess.run(init)

# Training cycle

for epoch in range(training_epochs):

avg_cost = 0.

total_batch = int(mnist.train.num_examples/batch_size)

# Loop over all batches

for i in range(total_batch):

batch_xs, batch_ys = mnist.train.next_batch(batch_size)

# Run optimization op (backprop) and cost op (to get loss value)

_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,

y: batch_ys})

# Compute average loss

avg_cost += c / total_batch

# Display logs per epoch step

if (epoch+1) % display_step == 0:

print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))

print("Optimization Finished!")

# Test model

correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))

# Calculate accuracy

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

print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

# predict

# results = sess.run(pred, feed_dict={x: batch_xs[:1]})

# save model

saver = tf.train.Saver()

save_path = saver.save(sess, "logistic_regression/model.ckpt")

tf.train.write_graph(sess.graph.as_graph_def(),'logistic_regression','model.pbtxt', as_text=True)

freeze_graph.freeze_graph(input_graph = 'logistic_regression/model.pbtxt',

input_saver = "",

input_binary = False,

input_checkpoint = 'logistic_regression/model.ckpt',

output_node_names = "Softmax",

restore_op_name = "save/restore_all",

filename_tensor_name = "save/Const:0",

output_graph = 'logistic_regression/model.pb',

clear_devices = True,

initializer_nodes = "")

# restoring the model

saver = tf.train.import_meta_graph('logistic_regression/tensorflowModel.ckpt.meta')

saver.restore(sess,tf.train.latest_checkpoint('logistic_regression'))

# predict

# pred = graph._nodes_by_name["Softmax"]

# output = pred.outputs[0]

# x = graph._nodes_by_name["Placeholder"]

# input = x.outputs[0]

# results = sess.run(output, feed_dict={input: batch_xs[:1]})