tf.keras.losses.MSE

Computes the mean squared error between labels and predictions.

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

Formula:

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

Example:

y_true = np.random.randint(0, 2, size=(2, 3))
y_pred = np.random.random(size=(2, 3))
loss = keras.losses.mean_squared_error(y_true, y_pred)

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

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