View source on GitHub
description: Computes the mean squared error between labels and predictions.
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View source on GitHub
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Computes the mean squared error between labels and predictions.
tf.keras.losses.MSE(
y_true, y_pred
)
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)
>>> 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 | |
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y_true
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Ground truth values. shape = [batch_size, d0, .. dN].
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y_pred
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The predicted values. shape = [batch_size, d0, .. dN].
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Returns | |
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Mean squared error values. shape = [batch_size, d0, .. dN-1].
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