TensorFlow 1 version
|
View source on GitHub
|
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)
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))
Args | |
|---|---|
y_true
|
Ground truth values. shape = [batch_size, d0, .. dN].
|
y_pred
|
The predicted values. shape = [batch_size, d0, .. dN].
|
Returns | |
|---|---|
Mean squared error values. shape = [batch_size, d0, .. dN-1].
|