def main(train_dir, batch_size, num_batches, log_dir, checkpoint_dir=None):
if checkpoint_dir is None:
checkpoint_dir = log_dir
images, labels = inputs(train_dir, False, batch_size, num_batches)
predictions, total_loss = network(images, labels)
tf.summary.scalar('loss', total_loss)
predictions = tf.to_int32(tf.argmax(predictions, 1))
tf.summary.scalar('accuracy', slim.metrics.accuracy(predictions, labels))
# These are streaming metrics which compute the "running" metric,
# e.g running accuracy
metrics_to_values, metrics_to_updates = slim.metrics.aggregate_metric_map({
'accuracy':
slim.metrics.streaming_accuracy(predictions, labels),
})
# Define the streaming summaries to write:
for metric_name, metric_value in metrics_to_values.items():
tf.summary.scalar(metric_name, metric_value)
# Evaluate every 30 seconds
slim.evaluation.evaluation_loop('',
checkpoint_dir,
log_dir,
num_evals=1,
eval_op=list(metrics_to_updates.values()),
summary_op=tf.summary.merge_all(),
eval_interval_secs=30,
max_number_of_evaluations=100000000)
def main(train_dir, batch_size, num_batches, log_dir):
images, labels = inputs(train_dir,
True,
batch_size,
num_batches)
predictions, total_loss = network(images, labels)
tf.summary.scalar('loss', total_loss)
optimizer = tf.train.GradientDescentOptimizer(0.001)
train_op = slim.learning.create_train_op(total_loss, optimizer, summarize_gradients=True)
slim.learning.train(train_op, log_dir, save_summaries_secs=10, save_interval_secs=10)
def main(train_dir, batch_size, num_batches, log_dir):
images, labels = inputs(train_dir, True, batch_size, num_batches)
predictions, total_loss = network(images, labels)
tf.summary.scalar('loss', total_loss)
optimizer = tf.train.MomentumOptimizer(learning_rate=0.025,
momentum=0.9,
use_nesterov=True)
train_op = slim.learning.create_train_op(total_loss,
optimizer,
summarize_gradients=True)
slim.learning.train(train_op,
log_dir,
save_summaries_secs=20,
save_interval_secs=20)