tf.keras.losses.MeanSquaredError

Computes the mean squared error between labels and predictions.

View aliases

Main aliases

tf.keras.losses.mean_squared_error, tf.keras.losses.mse, tf.keras.metrics.MSE, tf.keras.metrics.mean_squared_error, tf.keras.metrics.mse, tf.losses.MSE, tf.losses.mean_squared_error, tf.losses.mse, tf.metrics.MSE, tf.metrics.mean_squared_error, tf.metrics.mse

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.losses.MSE, tf.compat.v1.keras.losses.mean_squared_error, tf.compat.v1.keras.losses.mse, tf.compat.v1.keras.metrics.MSE, tf.compat.v1.keras.metrics.mean_squared_error, tf.compat.v1.keras.metrics.mse

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

Used in the notebooks

Used in the guide	Used in the tutorials
Customize what happens in Model.fit	Optimizers in TensorFlow Probability

After computing the squared distance between the inputs, the mean value over the last dimension is returned.

loss = mean(square(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_squared_error(y_true, y_pred)
assert loss.shape == (2,)
assert np.array_equal(
    loss.numpy(), np.mean(np.square(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 squared error values. shape = `[batch_size, d0, .. dN-1]`.

TensorFlow

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tf.keras / losses / losses.MeanSquaredError

View aliases

Used in the notebooks

Standalone usage:

Args

Returns