jfsantos · November 3, 2020 06:28 · jfsantos · Dec 1, 2016 · laventura · Aug 30, 2017
diff --git a/logistic_regression_with_checkpointing.py b/logistic_regression_with_checkpointing.py
 '''
 A logistic regression example using the meta-graph checkpointing
 features of Tensorflow.

 Author: João Felipe Santos, based on code by Aymeric Damien
 (https://github.com/aymericdamien/TensorFlow-Examples/)
 '''

 from __future__ import print_function

 import tensorflow as tf
 import numpy as np
 import argparse

 # 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
 batch_size = 100
 display_step = 1

 parser = argparse.ArgumentParser()
 parser.add_argument('--load', default=False)
 parser.add_argument('--max_epochs', type=int, default=5)
 args = parser.parse_args()
 load = args.load
 training_epochs = args.max_epochs

 # Instantiate saver
 if not load:
    # tf Graph Input
    x = tf.placeholder(tf.float32, [None, 784], name='x') # mnist data image of shape 28*28=784
    y = tf.placeholder(tf.float32, [None, 10], name='y') # 0-9 digits recognition => 10 classes

    # Set model weights
    W = tf.Variable(tf.zeros([784, 10]), name='W')
    b = tf.Variable(tf.zeros([10]), name='b')

    # 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)

    init = tf.initialize_all_variables()

    saver = tf.train.Saver()
    # In order to be able to easily retrieve variables and ops later,
    # we add them to collections
    tf.add_to_collection('train_op', optimizer)
    tf.add_to_collection('cost_op', cost)
    tf.add_to_collection('input', x)
    tf.add_to_collection('target', y)
    tf.add_to_collection('pred', pred)

    initial_epoch = 0
 else:
    # Find last executed epoch
    from glob import glob
    history = list(map(lambda x: int(x.split('-')[1][:-5]), glob('model.ckpt-*.meta')))
    last_epoch = np.max(history)
    # Instantiate saver object using previously saved meta-graph
    saver = tf.train.import_meta_graph('model.ckpt-{}.meta'.format(last_epoch))
    initial_epoch = last_epoch + 1

 # Launch the graph
 with tf.Session() as sess:
    if not load:
        sess.run(init)
    else:
        saver.restore(sess, 'model.ckpt')
        optimizer = tf.get_collection('train_op')[0]
        cost = tf.get_collection('cost_op')[0]
        x = tf.get_collection('input')[0]
        y = tf.get_collection('target')[0]
        pred = tf.get_collection('pred')[0]

    # Training cycle
    for epoch in range(initial_epoch, 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))
        saver.save(sess, 'model.ckpt', global_step=epoch)

    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}))
	'''
	A logistic regression example using the meta-graph checkpointing
	features of Tensorflow.

	Author: João Felipe Santos, based on code by Aymeric Damien
	(https://github.com/aymericdamien/TensorFlow-Examples/)
	'''

	from __future__ import print_function

	import tensorflow as tf
	import numpy as np
	import argparse

	# 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
	batch_size = 100
	display_step = 1

	parser = argparse.ArgumentParser()
	parser.add_argument('--load', default=False)
	parser.add_argument('--max_epochs', type=int, default=5)
	args = parser.parse_args()
	load = args.load
	training_epochs = args.max_epochs

	# Instantiate saver
	if not load:
	# tf Graph Input
	x = tf.placeholder(tf.float32, [None, 784], name='x') # mnist data image of shape 28*28=784
	y = tf.placeholder(tf.float32, [None, 10], name='y') # 0-9 digits recognition => 10 classes

	# Set model weights
	W = tf.Variable(tf.zeros([784, 10]), name='W')
	b = tf.Variable(tf.zeros([10]), name='b')

	# 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)

	init = tf.initialize_all_variables()

	saver = tf.train.Saver()
	# In order to be able to easily retrieve variables and ops later,
	# we add them to collections
	tf.add_to_collection('train_op', optimizer)
	tf.add_to_collection('cost_op', cost)
	tf.add_to_collection('input', x)
	tf.add_to_collection('target', y)
	tf.add_to_collection('pred', pred)

	initial_epoch = 0
	else:
	# Find last executed epoch
	from glob import glob
	history = list(map(lambda x: int(x.split('-')[1][:-5]), glob('model.ckpt-*.meta')))
	last_epoch = np.max(history)
	# Instantiate saver object using previously saved meta-graph
	saver = tf.train.import_meta_graph('model.ckpt-{}.meta'.format(last_epoch))
	initial_epoch = last_epoch + 1

	# Launch the graph
	with tf.Session() as sess:
	if not load:
	sess.run(init)
	else:
	saver.restore(sess, 'model.ckpt')
	optimizer = tf.get_collection('train_op')[0]
	cost = tf.get_collection('cost_op')[0]
	x = tf.get_collection('input')[0]
	y = tf.get_collection('target')[0]
	pred = tf.get_collection('pred')[0]

	# Training cycle
	for epoch in range(initial_epoch, 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))
	saver.save(sess, 'model.ckpt', global_step=epoch)

	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}))