KerryHalupka’s gists

KerryHalupka / pytorch_pretrained_results.csv

Created August 16, 2020 10:08

	Train acc	Val acc	Time per epoch
	92.9%	96.0%	79 sec

KerryHalupka / tensorflow_pretrained_results.csv

Created August 16, 2020 10:05

	Train acc	Val acc	Time per epoch
	96.2%	95.3%	20 sec

KerryHalupka / pytorch_simple_results.csv

Created August 16, 2020 09:57

	Train acc	Val acc	Time per epoch
	85.0%	65.0%	80 sec

KerryHalupka / tensorflow_simple_results_2.csv

Created August 16, 2020 09:56

	Train acc	Val acc	Time per epoch
	88.0%	72.0%	33 sec

KerryHalupka / tensorflow_simple_results.csv

Created August 16, 2020 09:22

	Train acc	Val acc	Time per epoch
	88.0%	67.3%	32 sec

KerryHalupka / tensorflow_eval.py

Created August 16, 2020 09:04

	model.load_weights(ckpt_dir)
	for inputs in test_gen:
	outputs = model(inputs, training=False)
	break

KerryHalupka / pytorch_eval.py

Created August 16, 2020 08:47

	#load model weights
	model.load_state_dict(torch.load(ckpt_dir))

	for inputs, targets in test_gen:
	# use GPU if available
	inputs = inputs.to(device)
	inputs = inputs.float()
	outputs = model(inputs)
	break

KerryHalupka / pytorch_pretrained_model.py

Last active August 16, 2020 09:52

	learning_rate = 0.0001

	#get mobilenet
	model = torchvision.models.mobilenet_v2(pretrained=True)

	# freeze the feature extraction convolutional layers
	for param in model.parameters():
	param.requires_grad = False

	# define a classification layer

KerryHalupka / pytorch_training_loop.py

Last active August 16, 2020 09:42

	from torch.utils.tensorboard import SummaryWriter

	# Setup tensorboard
	file_writer = SummaryWriter(log_dir=log_dir + '/metrics')
	best_val_acc = 0 # for model check pointing
	# Epoch loop
	for epoch in range(1, num_epoch + 1):
	start_time = timer()
	# Reset metrics
	train_loss = 0.0

KerryHalupka / tensorflow_pretrained_model.py

Last active August 16, 2020 09:48

	learning_rate = 0.0001
	# get just the feature extraction layers of mobilenet
	base_model = tf.keras.applications.MobileNetV2(
	include_top=False, weights='imagenet', input_shape=(im_size, im_size, 3))

	# freeze the feature extractor convolutional layers
	base_model.trainable = False

	# define a classification layer on top
	global_average_layer = tf.keras.layers.GlobalAveragePooling2D()

Kerry Halupka KerryHalupka