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