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