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Computes the mean squared error between labels and predictions.

Used in the notebooks

Used in the guide Used in the tutorials

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))

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

Mean squared error values. shape = [batch_size, d0, .. dN-1].