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allegroai 2019-06-14 01:50:33 +03:00
parent 774b39af9e
commit 783b0b99c9
1 changed files with 33 additions and 40 deletions
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examples/tensorflow_mnist_with_summaries.py
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@ -34,13 +34,12 @@ from tensorflow.examples.tutorials.mnist import input_data
from trains import Task from trains import Task
FLAGS = None FLAGS = None
task = Task.init(project_name='examples', task_name='Tensorflow mnist with summaries example') task = Task.init(project_name='examples', task_name='Tensorflow mnist with summaries')
def train(): def train():
# Import data # Import data
mnist = input_data.read_data_sets(FLAGS.data_dir, mnist = input_data.read_data_sets(FLAGS.data_dir, fake_data=FLAGS.fake_data)
fake_data=FLAGS.fake_data)
sess = tf.InteractiveSession() sess = tf.InteractiveSession()
# Create a multilayer model. # Create a multilayer model.
@ -123,12 +122,12 @@ def train():
# the batch. # the batch.
with tf.name_scope('total'): with tf.name_scope('total'):
cross_entropy = tf.losses.sparse_softmax_cross_entropy( cross_entropy = tf.losses.sparse_softmax_cross_entropy(
labels=y_, logits=y) labels=y_, logits=y)
tf.summary.scalar('cross_entropy', cross_entropy) tf.summary.scalar('cross_entropy', cross_entropy)
with tf.name_scope('train'): with tf.name_scope('train'):
train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize( train_step = tf.train.AdamOptimizer(FLAGS.learning_rate).minimize(
cross_entropy) cross_entropy)
with tf.name_scope('accuracy'): with tf.name_scope('accuracy'):
with tf.name_scope('correct_prediction'): with tf.name_scope('correct_prediction'):
@ -142,7 +141,7 @@ def train():
merged = tf.summary.merge_all() merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph) train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)
test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test') test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/test')
tf.global_variables_initializer().run() tf.global_variables_initializer().run()
# Train the model, and also write summaries. # Train the model, and also write summaries.
@ -161,29 +160,31 @@ def train():
saver = tf.train.Saver() saver = tf.train.Saver()
for i in range(FLAGS.max_steps): for i in range(FLAGS.max_steps):
if i % 10 == 0: # Record summaries and test-set accuracy if i % 10 == 0: # Record summaries and test-set accuracy
summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False)) summary, acc = sess.run([merged, accuracy], feed_dict=feed_dict(False))
test_writer.add_summary(summary, i) test_writer.add_summary(summary, i)
print('Accuracy at step %s: %s' % (i, acc)) print('Accuracy at step %s: %s' % (i, acc))
else: # Record train set summaries, and train else: # Record train set summaries, and train
if i % 100 == 99: # Record execution stats if i % 100 == 99: # Record execution stats
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE) run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata() run_metadata = tf.RunMetadata()
summary, _ = sess.run([merged, train_step], summary, _ = sess.run([merged, train_step],
feed_dict=feed_dict(True), feed_dict=feed_dict(True),
options=run_options, options=run_options,
run_metadata=run_metadata) run_metadata=run_metadata)
train_writer.add_run_metadata(run_metadata, 'step%03d' % i) train_writer.add_run_metadata(run_metadata, 'step%04d' % i)
train_writer.add_summary(summary, i) train_writer.add_summary(summary, i)
print('Adding run metadata for', i) print('Adding run metadata for', i)
else: # Record a summary else: # Record a summary
summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True)) summary, _ = sess.run([merged, train_step], feed_dict=feed_dict(True))
train_writer.add_summary(summary, i) # train_writer.add_summary(summary, i)
save_path = saver.save(sess,FLAGS.save_path) save_path = saver.save(sess, FLAGS.save_path)
print("Model saved in path: %s" % save_path) print("Saved model: %s" % save_path)
print('Flushing all images, this may take a couple of minutes')
train_writer.close() train_writer.close()
test_writer.close() test_writer.close()
print('Finished storing all metrics & images')
def main(_): def main(_):
@ -197,30 +198,22 @@ def main(_):
if __name__ == '__main__': if __name__ == '__main__':
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('--fake_data', nargs='?', const=True, type=bool, parser.add_argument('--fake_data', nargs='?', const=True, type=bool,
default=False, default=False,
help='If true, uses fake data for unit testing.') help='If true, uses fake data for unit testing.')
parser.add_argument('--max_steps', type=int, default=1000, parser.add_argument('--max_steps', type=int, default=300,
help='Number of steps to run trainer.') help='Number of steps to run trainer.')
parser.add_argument('--learning_rate', type=float, default=0.001, parser.add_argument('--learning_rate', type=float, default=0.001,
help='Initial learning rate') help='Initial learning rate')
parser.add_argument('--dropout', type=float, default=0.9, parser.add_argument('--dropout', type=float, default=0.9,
help='Keep probability for training dropout.') help='Keep probability for training dropout.')
parser.add_argument( parser.add_argument('--data_dir', type=str,
'--data_dir', default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'), 'tensorflow/mnist/input_data'),
type=str, help='Directory for storing input data')
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'), parser.add_argument('--log_dir', type=str,
'tensorflow/mnist/input_data'), default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
help='Directory for storing input data') 'tensorflow/mnist/logs/mnist_with_summaries'),
parser.add_argument( help='Summaries log directory')
'--log_dir', parser.add_argument('--save_path', default="/tmp/model.ckpt",
type=str, help='Save the trained model under this path')
default=os.path.join(os.getenv('TEST_TMPDIR', '/tmp'),
'tensorflow/mnist/logs/mnist_with_summaries'),
help='Summaries log directory')
parser.add_argument(
'--save_path',
default="/tmp/model.ckpt",
help='Save the trained model under this path'
)
FLAGS, unparsed = parser.parse_known_args() FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed) tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)