import tensorflow as tf

import math

# Creates an inference graph.

# Hidden 1

images = tf.constant(1.2, tf.float32, shape=[100, 28])

with tf.name_scope("hidden1"):

weights = tf.Variable(

tf.truncated_normal([28, 128],

stddev=1.0 / math.sqrt(float(28))),

name="weights")

biases = tf.Variable(tf.zeros([128]),

name="biases")

hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)

# Hidden 2

with tf.name_scope("hidden2"):

weights = tf.Variable(

tf.truncated_normal([128, 32],

stddev=1.0 / math.sqrt(float(128))),

name="weights")

biases = tf.Variable(tf.zeros([32]),

name="biases")

hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)

# Linear

with tf.name_scope("softmax_linear"):

weights = tf.Variable(

tf.truncated_normal([32, 10],

stddev=1.0 / math.sqrt(float(32))),

name="weights")

biases = tf.Variable(tf.zeros([10]),

name="biases")

logits = tf.matmul(hidden2, weights) + biases

tf.add_to_collection("logits", logits)

init_all_op = tf.global_variables_initializer()

with tf.Session() as sess:

# Initializes all the variables.

sess.run(init_all_op)

# Runs to logit.

sess.run(logits)

# Creates a saver.

saver0 = tf.train.Saver()

saver0.save(sess, 'my-save-dir/my-model-10000')

# Generates MetaGraphDef.

saver0.export_meta_graph('my-save-dir/my-model-10000.meta')

# Then later import it and extend it to a training graph.

with tf.Session() as sess:

new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')

new_saver.restore(sess, 'my-save-dir/my-model-10000')

# Addes loss and train.

labels = tf.constant(0, tf.int32, shape=[100], name="labels")

batch_size = tf.size(labels)

logits = tf.get_collection("logits")[0]

loss = tf.losses.sparse_softmax_cross_entropy(labels=labels,

logits=logits)

tf.summary.scalar('loss', loss)

# Creates the gradient descent optimizer with the given learning rate.

optimizer = tf.train.GradientDescentOptimizer(0.01)

# Runs train_op.

train_op = optimizer.minimize(loss)

sess.run(train_op)