tf.keras.losses.LogCosh

Computes the mean absolute error between labels and predictions.

View aliases

Main aliases

tf.keras.losses.mae, tf.keras.losses.mean_absolute_error, tf.keras.metrics.MAE, tf.keras.metrics.mae, tf.keras.metrics.mean_absolute_error, tf.losses.MAE, tf.losses.mae, tf.losses.mean_absolute_error, tf.metrics.MAE, tf.metrics.mae, tf.metrics.mean_absolute_error

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.losses.MAE, tf.compat.v1.keras.losses.mae, tf.compat.v1.keras.losses.mean_absolute_error, tf.compat.v1.keras.metrics.MAE, tf.compat.v1.keras.metrics.mae, tf.compat.v1.keras.metrics.mean_absolute_error

tf.keras.losses.MAE(
    y_true, y_pred
)

Used in the notebooks

Used in the tutorials
Intro to Autoencoders

loss = mean(abs(y_true - y_pred), axis=-1)

Standalone usage:

y_true = np.random.randint(0, 2, size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = tf.keras.losses.mean_absolute_error(y_true, y_pred)
assert loss.shape == (2,)
assert np.array_equal(
    loss.numpy(), np.mean(np.abs(y_true - y_pred), axis=-1))

Args
`y_true`	Ground truth values. shape = `[batch_size, d0, .. dN]`.
`y_pred`	The predicted values. shape = `[batch_size, d0, .. dN]`.

Returns
Mean absolute error values. shape = `[batch_size, d0, .. dN-1]`.

TensorFlow

tf.keras / losses / losses.LogCosh

View aliases

Used in the notebooks

Standalone usage:

Args

Returns

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.keras / losses / losses.LogCosh

View aliases

Used in the notebooks

Standalone usage:

Args

Returns