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cifar10_estimator_dataset.py
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cifar10_estimator_dataset.py
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'''
Using tf.estimator and tf.data to train a cnn model in TensorFlow 1.4.
GitHub: https://github.com/secsilm/understaing-datasets-estimators-tfrecords
Chinese blog: https://alanlee.fun/2017/12/22/understanding-estimators-datasets/
'''
import tensorflow as tf
import os
import json
tf.logging.set_verbosity(tf.logging.INFO)
flags = tf.app.flags
flags.DEFINE_integer('num_epochs', 10, 'Number of training epochs')
flags.DEFINE_integer('batch_size', 64, 'Batch size')
flags.DEFINE_float('learning_rate', 0.01, 'Learning rate')
flags.DEFINE_float('dropout_rate', 0.5, 'Dropout rate')
flags.DEFINE_string('train_dataset', 'train.tfrecords',
'Filename of training dataset')
flags.DEFINE_string('eval_dataset', 'eval.tfrecords',
'Filename of evaluation dataset')
flags.DEFINE_string('test_dataset', 'test.tfrecords',
'Filename of testing dataset')
flags.DEFINE_string('model_dir', 'models/cifar10_cnn_model',
'Directory to save models')
FLAGS = flags.FLAGS
def cifar_model_fn(features, labels, mode):
"""Model function for cifar10"""
# Input layer
x = tf.reshape(features, [-1, 32, 32, 3])
regularizer = tf.contrib.layers.l2_regularizer(scale=0.1)
x = tf.layers.conv2d(inputs=x, filters=64, kernel_size=[
3, 3], padding='same', activation=tf.nn.relu, kernel_regularizer=regularizer, name='CONV1')
x = tf.layers.batch_normalization(
inputs=x, training=mode == tf.estimator.ModeKeys.TRAIN, name='BN1')
x = tf.layers.conv2d(inputs=x, filters=128, kernel_size=[
3, 3], padding='same', activation=tf.nn.relu, kernel_regularizer=regularizer, name='CONV2')
x = tf.layers.batch_normalization(
inputs=x, training=mode == tf.estimator.ModeKeys.TRAIN, name='BN2')
x = tf.layers.conv2d(inputs=x, filters=128, kernel_size=[
3, 3], padding='same', activation=tf.nn.relu, kernel_regularizer=regularizer, name='CONV3')
x = tf.layers.batch_normalization(
inputs=x, training=mode == tf.estimator.ModeKeys.TRAIN, name='BN3')
x = tf.layers.conv2d(inputs=x, filters=128, kernel_size=[
3, 3], padding='same', activation=tf.nn.relu, kernel_regularizer=regularizer, name='CONV4')
x = tf.layers.batch_normalization(
inputs=x, training=mode == tf.estimator.ModeKeys.TRAIN, name='BN4')
x = tf.layers.max_pooling2d(inputs=x, pool_size=[
3, 3], strides=2, padding='same', name='POOL1')
x = tf.layers.conv2d(inputs=x, filters=128, kernel_size=3, padding='same',
activation=tf.nn.relu, kernel_regularizer=regularizer, name='CONV5')
x = tf.layers.batch_normalization(
inputs=x, training=mode == tf.estimator.ModeKeys.TRAIN, name='BN5')
x = tf.layers.max_pooling2d(inputs=x, pool_size=[
3, 3], strides=2, padding='same', name='POOL2')
# Dense layer
x = tf.reshape(x, [-1, 8 * 8 * 128])
x = tf.layers.dense(inputs=x, units=512, activation=tf.nn.relu,
kernel_regularizer=regularizer, name='DENSE1')
x = tf.layers.dense(inputs=x, units=512, activation=tf.nn.relu,
kernel_regularizer=regularizer, name='DENSE2')
x = tf.layers.dropout(inputs=x, rate=FLAGS.dropout_rate,
training=mode == tf.estimator.ModeKeys.TRAIN, name='DROPOUT')
logits = tf.layers.dense(inputs=x, units=10,
kernel_regularizer=regularizer, name='FINAL')
# Predicition
predictions = {
'classes': tf.argmax(input=logits, axis=1, name='classes'),
'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
# Loss for train and eval
onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10)
# print('onehot_labels', onehot_labels.shape)
loss = tf.losses.softmax_cross_entropy(onehot_labels, logits, scope='LOSS')
# print(labels.shape, predictions['classes'].shape)
accuracy, update_op = tf.metrics.accuracy(
labels=labels, predictions=predictions['classes'], name='accuracy')
batch_acc = tf.reduce_mean(tf.cast(
tf.equal(tf.cast(labels, tf.int64), predictions['classes']), tf.float32))
tf.summary.scalar('batch_acc', batch_acc)
tf.summary.scalar('streaming_acc', update_op)
# tf.summary.scalar('accuracy', accuracy)
# eval_metric_ops = {
# 'accuracy': tf.metrics.accuracy(labels=labels, predictions=predictions['classes'], name='accuracy')
# }
# Train
if mode == tf.estimator.ModeKeys.TRAIN:
# tensors_to_log = {
# 'Accuracy': accuracy,
# 'My accuracy': my_acc}
# logging_hook = tf.train.LoggingTensorHook(tensors=tensors_to_log, every_n_iter=100)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
optimizer = tf.train.RMSPropOptimizer(learning_rate=FLAGS.learning_rate)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(
loss=loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op)
eval_metric_ops = {
'accuracy': (accuracy, update_op)
}
return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)
def parser(record):
keys_to_features = {
'image_raw': tf.FixedLenFeature((), tf.string),
'label': tf.FixedLenFeature((), tf.int64)
}
parsed = tf.parse_single_example(record, keys_to_features)
image = tf.decode_raw(parsed['image_raw'], tf.uint8)
image = tf.cast(image, tf.float32)
label = tf.cast(parsed['label'], tf.int32)
return image, label
def save_hp_to_json():
'''Save hyperparameters to a json file'''
filename = os.path.join(FLAGS.model_dir, 'hparams.json')
hparams = FLAGS.flag_values_dict()
with open(filename, 'w') as f:
json.dump(hparams, f, indent=4, sort_keys=True)
def get_estimator(config):
'''Return the model as a Tensorflow Estimator object.'''
return tf.estimator.Estimator(model_fn=cifar_model_fn, config=config)
def main(unused_argv):
def train_input_fn():
train_dataset = tf.data.TFRecordDataset(FLAGS.train_dataset)
train_dataset = train_dataset.map(parser)
train_dataset = train_dataset.repeat(FLAGS.num_epochs)
train_dataset = train_dataset.batch(FLAGS.batch_size)
train_iterator = train_dataset.make_one_shot_iterator()
features, labels = train_iterator.get_next()
return features, labels
def eval_input_fn():
eval_dataset = tf.data.TFRecordDataset(FLAGS.eval_dataset)
eval_dataset = eval_dataset.map(parser)
# eval_dataset = eval_dataset.repeat(FLAGS.num_epochs)
eval_dataset = eval_dataset.batch(FLAGS.batch_size)
eval_iterator = eval_dataset.make_one_shot_iterator()
features, labels = eval_iterator.get_next()
return features, labels
config = tf.estimator.RunConfig()
config = config.replace(model_dir=FLAGS.model_dir)
cifar10_classifier = get_estimator(config=config)
# Train
cifar10_classifier.train(input_fn=train_input_fn)
# Evaluation
eval_results = cifar10_classifier.evaluate(input_fn=eval_input_fn)
print(eval_results)
tf.logging.info('Saving hyperparameters ...')
save_hp_to_json()
if __name__ == '__main__':
tf.app.run()