""" Using convolutional net on MNIST dataset of handwritten digits
MNIST dataset: http://yann.lecun.com/exdb/mnist/
CS 20: "TensorFlow for Deep Learning Research"
cs20.stanford.edu
Chip Huyen ([email protected])
Lecture 07
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import time
import tensorflow as tf
import utils
class ConvNet(object):
def __init__(self):
self.lr = 0.001
self.batch_size = 128
self.keep_prob = tf.constant(0.75)
self.gstep = tf.Variable(0, dtype=tf.int32,
trainable=False, name='global_step')
self.n_classes = 10
self.skip_step = 20
self.n_test = 10000
self.training=False
def get_data(self):
with tf.name_scope('data'):
train_data, test_data = utils.get_mnist_dataset(self.batch_size)
iterator = tf.data.Iterator.from_structure(train_data.output_types,
train_data.output_shapes)
img, self.label = iterator.get_next()
self.img = tf.reshape(img, shape=[-1, 28, 28, 1])
self.train_init = iterator.make_initializer(train_data)
self.test_init = iterator.make_initializer(test_data)
def inference(self):
conv1 = tf.layers.conv2d(inputs=self.img,
filters=32,
kernel_size=[5, 5],
padding='SAME',
activation=tf.nn.relu,
name='conv1')
pool1 = tf.layers.max_pooling2d(inputs=conv1,
pool_size=[2, 2],
strides=2,
name='pool1')
conv2 = tf.layers.conv2d(inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding='SAME',
activation=tf.nn.relu,
name='conv2')
pool2 = tf.layers.max_pooling2d(inputs=conv2,
pool_size=[2, 2],
strides=2,
name='pool2')
feature_dim = pool2.shape[1] * pool2.shape[2] * pool2.shape[3]
pool2 = tf.reshape(pool2, [-1, feature_dim])
fc = tf.layers.dense(pool2, 1024, activation=tf.nn.relu, name='fc')
dropout = tf.layers.dropout(fc,
self.keep_prob,
training=self.training,
name='dropout')
self.logits = tf.layers.dense(dropout, self.n_classes, name='logits')
def loss(self):
'''
define loss function
use softmax cross entropy with logits as the loss function
compute mean cross entropy, softmax is applied internally
'''
with tf.name_scope('loss'):
entropy = tf.nn.softmax_cross_entropy_with_logits(labels=self.label, logits=self.logits)
self.loss = tf.reduce_mean(entropy, name='loss')
def optimize(self):
'''
Define training op
using Adam Gradient Descent to minimize cost
'''
self.opt = tf.train.AdamOptimizer(self.lr).minimize(self.loss,
global_step=self.gstep)
def summary(self):
'''
Create summaries to write on TensorBoard
'''
with tf.name_scope('summaries'):
tf.summary.scalar('loss', self.loss)
tf.summary.scalar('accuracy', self.accuracy)
tf.summary.histogram('histogram loss', self.loss)
self.summary_op = tf.summary.merge_all()
def eval(self):
'''
Count the number of right predictions in a batch
'''
with tf.name_scope('predict'):
preds = tf.nn.softmax(self.logits)
correct_preds = tf.equal(tf.argmax(preds, 1), tf.argmax(self.label, 1))
self.accuracy = tf.reduce_sum(tf.cast(correct_preds, tf.float32))
def build(self):
'''
Build the computation graph
'''
self.get_data()
self.inference()
self.loss()
self.optimize()
self.eval()
self.summary()
def train_one_epoch(self, sess, saver, init, writer, epoch, step):
start_time = time.time()
sess.run(init)
self.training = True
total_loss = 0
n_batches = 0
try:
while True:
_, l, summaries = sess.run([self.opt, self.loss, self.summary_op])
writer.add_summary(summaries, global_step=step)
if (step + 1) % self.skip_step == 0:
print('Loss at step {0}: {1}'.format(step, l))
step += 1
total_loss += l
n_batches += 1
except tf.errors.OutOfRangeError:
pass
saver.save(sess, 'checkpoints/convnet_layers/mnist-convnet', step)
print('Average loss at epoch {0}: {1}'.format(epoch, total_loss/n_batches))
print('Took: {0} seconds'.format(time.time() - start_time))
return step
def eval_once(self, sess, init, writer, epoch, step):
start_time = time.time()
sess.run(init)
self.training = False
total_correct_preds = 0
try:
while True:
accuracy_batch, summaries = sess.run([self.accuracy, self.summary_op])
writer.add_summary(summaries, global_step=step)
total_correct_preds += accuracy_batch
except tf.errors.OutOfRangeError:
pass
print('Accuracy at epoch {0}: {1} '.format(epoch, total_correct_preds/self.n_test))
print('Took: {0} seconds'.format(time.time() - start_time))
def train(self, n_epochs):
'''
The train function alternates between training one epoch and evaluating
'''
utils.safe_mkdir('checkpoints')
utils.safe_mkdir('checkpoints/convnet_layers')
writer = tf.summary.FileWriter('./graphs/convnet_layers', tf.get_default_graph())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(os.path.dirname('checkpoints/convnet_layers/checkpoint'))
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
step = self.gstep.eval()
for epoch in range(n_epochs):
step = self.train_one_epoch(sess, saver, self.train_init, writer, epoch, step)
self.eval_once(sess, self.test_init, writer, epoch, step)
writer.close()
if __name__ == '__main__':
model = ConvNet()
model.build()
model.train(n_epochs=15)