checkpoint = tf.train.get_checkpoint_state(MODEL_DIR)
if checkpoint and checkpoint.model_checkpoint_path:
saver.restore(session, checkpoint.model_checkpoint_path)
print("Loaded checkpoint: {}".format(checkpoint.model_checkpoint_path))
else:
print("Unable to load checkpoint")
counter = 0
print(len(DataSet.TRAIN_DATASET.images))
saver.save(session, os.path.join(MODEL_DIR, "network"), global_step=counter)
for epoch in range(3):
print(epoch)
for images, labels in DataSet.iter_batches(50):
counter += 1
if counter % 100 == 0:
print(counter)
acc, summ = session.run([model.accuracy, summary], feed_dict = {
model.input_var: images,
model.corr_labels: labels,
model.keep_prob: 1.0
})
writer.add_summary(summ, counter)
print("iteration {}, training accuracy {}".format(counter, acc))
session.run([model.train], feed_dict = {
model.input_var: images,
