1_modelCreate.py

#**** Copyright 2017 datawrangl3r.
#**** Adapted form the on the MNIST biginners tutorial by Google. 
#
#**** Licensed under the Apache License, Version 2.0 (the "License");
#**** you may not use this file except in compliance with the License.
#**** You may obtain a copy of the License at
#
#****     http://www.apache.org/licenses/LICENSE-2.0
#
#**** Unless required by applicable law or agreed to in writing, software
#**** distributed under the License is distributed on an "AS IS" BASIS,
#**** WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#**** See the License for the specific language governing permissions and
#**** limitations under the License.
# ==============================================================================

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

## This is where the data gets imported
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

# Model Creation - Placeholders & Variables
x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)

# Defining the loss and optimizations
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)

init_op = tf.global_variables_initializer()
saver = tf.train.Saver()

# The model is saved to disk as a model.ckpt file

#Initializing the Session
with tf.Session() as sess:
    sess.run(init_op)
    for i in range(1000):
        batch_xs, batch_ys = mnist.train.next_batch(100)
        sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
        
    save_path = saver.save(sess, "mnist_model.ckpt")
print ("Model saved in file: ", save_path)