""" 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
def conv_relu(inputs, filters, k_size, stride, padding, scope_name):
'''
A method that does convolution + relu on inputs
'''
with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE) as scope:
in_channels = inputs.shape[-1]
kernel = tf.get_variable('kernel',
[k_size, k_size, in_channels, filters],
initializer=tf.truncated_normal_initializer())
biases = tf.get_variable('biases',
[filters],
initializer=tf.random_normal_initializer())
conv = tf.nn.conv2d(inputs, kernel, strides=[1, stride, stride, 1], padding=padding)
return tf.nn.relu(conv + biases, name=scope.name)
def maxpool(inputs, ksize, stride, padding='VALID', scope_name='pool'):
'''A method that does max pooling on inputs'''
with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE) as scope:
pool = tf.nn.max_pool(inputs,
ksize=[1, ksize, ksize, 1],
strides=[1, stride, stride, 1],
padding=padding)
return pool
def fully_connected(inputs, out_dim, scope_name='fc'):
'''
A fully connected linear layer on inputs
'''
with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE) as scope:
in_dim = inputs.shape[-1]
w = tf.get_variable('weights', [in_dim, out_dim],
initializer=tf.truncated_normal_initializer())
b = tf.get_variable('biases', [out_dim],
initializer=tf.constant_initializer(0.0))
out = tf.matmul(inputs, w) + b
return out
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 = True
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 = conv_relu(inputs=self.img,
filters=32,
k_size=5,
stride=1,
padding='SAME',
scope_name='conv1')
pool1 = maxpool(conv1, 2, 2, 'VALID', 'pool1')
conv2 = conv_relu(inputs=pool1,
filters=64,
k_size=5,
stride=1,
padding='SAME',
scope_name='conv2')
pool2 = maxpool(conv2, 2, 2, 'VALID', 'pool2')
feature_dim = pool2.shape[1] * pool2.shape[2] * pool2.shape[3]
pool2 = tf.reshape(pool2, [-1, feature_dim])
fc = fully_connected(pool2, 1024, 'fc')
dropout = tf.nn.dropout(tf.nn.relu(fc), self.keep_prob, name='relu_dropout')
self.logits = fully_connected(dropout, self.n_classes, '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_mnist/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_mnist')
writer = tf.summary.FileWriter('./graphs/convnet', 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_mnist/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=30)
