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tf.keras.losses.MSE

TensorFlow 1 version View source on GitHub

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)

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].