/TensorFlow 2.4


Computes the mean squared error between labels and predictions.

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].

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