def padded_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that predictions matches labels on non-0s."""
with tf.variable_scope("padded_accuracy", values=[predictions, labels]):
padded_labels = common_layers.pad_with_zeros(predictions, labels)
weights = weights_fn(padded_labels)
outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
return tf.to_float(tf.equal(outputs, padded_labels)), weights
def padded_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that predictions matches labels on non-0s."""
with tf.variable_scope("padded_accuracy", values=[predictions, labels]):
padded_labels = common_layers.pad_with_zeros(predictions, labels)
weights = weights_fn(padded_labels)
outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
return tf.to_float(tf.equal(outputs, padded_labels)), weights
def padded_sequence_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that predictions matches labels everywhere (non-0)."""
with tf.variable_scope(
"padded_sequence_accuracy", values=[predictions, labels]):
padded_labels = common_layers.pad_with_zeros(predictions, labels)
weights = weights_fn(padded_labels)
outputs = tf.to_int32(tf.argmax(predictions, axis=-1))
not_correct = tf.to_float(tf.not_equal(outputs, padded_labels)) * weights
axis = list(range(1, len(outputs.get_shape())))
correct_seq = 1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis))
return correct_seq, tf.constant(1.0)
def padded_sequence_accuracy(predictions,
labels,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that predictions matches labels everywhere (non-0)."""
with tf.variable_scope(
"padded_sequence_accuracy", values=[predictions, labels]):
padded_predictions, padded_labels = common_layers.pad_with_zeros(
predictions, labels)
weights = weights_fn(padded_labels)
outputs = tf.to_int32(tf.argmax(padded_predictions, axis=-1))
not_correct = tf.to_float(tf.not_equal(outputs, padded_labels)) * weights
axis = list(range(1, len(outputs.get_shape())))
correct_seq = 1.0 - tf.minimum(1.0, tf.reduce_sum(not_correct, axis=axis))
return correct_seq, tf.constant(1.0)
def padded_accuracy_topk(predictions,
labels,
k,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that top-k predictions matches labels on non-0s."""
with tf.variable_scope("padded_accuracy_topk", values=[predictions, labels]):
padded_labels = common_layers.pad_with_zeros(predictions, labels)
weights = weights_fn(padded_labels)
effective_k = tf.minimum(k, tf.shape(predictions)[-1])
_, outputs = tf.nn.top_k(predictions, k=effective_k)
outputs = tf.to_int32(outputs)
padded_labels = tf.expand_dims(padded_labels, axis=-1)
padded_labels += tf.zeros_like(outputs) # Pad to same shape.
same = tf.to_float(tf.equal(outputs, padded_labels))
same_topk = tf.reduce_sum(same, axis=-1)
return same_topk, weights
def padded_accuracy_topk(predictions,
labels,
k,
weights_fn=common_layers.weights_nonzero):
"""Percentage of times that top-k predictions matches labels on non-0s."""
with tf.variable_scope("padded_accuracy_topk",
values=[predictions, labels]):
padded_labels = common_layers.pad_with_zeros(predictions, labels)
weights = weights_fn(padded_labels)
effective_k = tf.minimum(k, tf.shape(predictions)[-1])
_, outputs = tf.nn.top_k(predictions, k=effective_k)
outputs = tf.to_int32(outputs)
padded_labels = tf.expand_dims(padded_labels, axis=-1)
padded_labels += tf.zeros_like(outputs) # Pad to same shape.
same = tf.to_float(tf.equal(outputs, padded_labels))
same_topk = tf.reduce_sum(same, axis=-1)
return same_topk, weights