Derrick Mwiti mwitiderrick

mwitiderrick / smooth_labels.py

Created July 14, 2022 05:01

	labels = jnp.array([0.20,0.30,0.10,0.20,0.2])
	optax.smooth_labels(labels,alpha=0.4)
	# DeviceArray([0.2 , 0.26, 0.14, 0.2 , 0.2 ], dtype=float32)

mwitiderrick / log_cosh.py

Created July 14, 2022 05:00

	predictions = jnp.array([0.50,0.60,0.70,0.30,0.25])
	targets = jnp.array([0.20,0.30,0.10,0.20,0.2])
	optax.log_cosh(predictions,targets)
	# DeviceArray([0.04434085, 0.04434085, 0.17013526, 0.00499171, 0.00124949], dtype=float32)

mwitiderrick / l2_loss.py

Created July 14, 2022 05:00

	predictions = jnp.array([0.50,0.60,0.70,0.30,0.25])
	targets = jnp.array([0.20,0.30,0.10,0.20,0.2])
	optax.l2_loss(predictions,targets)
	# DeviceArray([0.045 , 0.045 , 0.17999998, 0.005 , 0.00125 ], dtype=float32)

mwitiderrick / huber_loss.py

Created July 14, 2022 04:58

	logits = jnp.array([0.50,0.60,0.70,0.30,0.25])
	labels = jnp.array([0.20,0.30,0.10,0.20,0.2])
	optax.huber_loss(logits,labels)
	# DeviceArray([0.045 , 0.045 , 0.17999998, 0.005 , 0.00125 ], dtype=float32)

mwitiderrick / cosine_similarity.py

Created July 14, 2022 04:58

	predictions = jnp.array([0.50,0.60,0.70,0.30,0.25])
	targets = jnp.array([0.20,0.30,0.10,0.20,0.2])
	optax.cosine_similarity(predictions,targets,epsilon=0.5)
	# DeviceArray(0.8220514, dtype=float32)

mwitiderrick / cosine_distance.py

Created July 14, 2022 04:57

	predictions = jnp.array([0.50,0.60,0.70,0.30,0.25])
	targets = jnp.array([0.20,0.30,0.10,0.20,0.2])
	optax.cosine_distance(predictions,targets,epsilon=0.7)
	# DeviceArray(0.4128204, dtype=float32)

mwitiderrick / softmax_cross_entropy.py

Created July 14, 2022 04:56

	logits = jnp.array([0.50,0.60,0.70,0.30,0.25])
	labels = jnp.array([0.20,0.30,0.10,0.20,0.2])
	optax.softmax_cross_entropy(logits,labels)
	# DeviceArray(1.6341426, dtype=float32)

mwitiderrick / sigmoid_binary_cross_entropy.py

Created July 14, 2022 04:55

	optax.sigmoid_binary_cross_entropy(0.5,0.0)
	# DeviceArray(0.974077, dtype=float32, weak_type=True)

mwitiderrick / custom_sigmoid_binary_cross_entropy.py

Last active July 14, 2022 04:44

	import jax
	def custom_sigmoid_binary_cross_entropy(logits, labels):
	log_p = jax.nn.log_sigmoid(logits)
	log_not_p = jax.nn.log_sigmoid(-logits)
	return -labels * log_p - (1. - labels) * log_not_p

	custom_sigmoid_binary_cross_entropy(0.5,0.0)
	# DeviceArray(0.974077, dtype=float32, weak_type=True)

mwitiderrick / load_data.py

Created July 11, 2022 18:28

	import tensorflow as tf
	# Ensure TF does not see GPU and grab all GPU memory.
	tf.config.set_visible_devices([], device_type='GPU')

	import tensorflow_datasets as tfds

	data_dir = '/tmp/tfds'

	# Fetch full datasets for evaluation
	# tfds.load returns tf.Tensors (or tf.data.Datasets if batch_size != -1)