Computes the mean squared error between labels and predictions.
View aliases
Main aliases
tf.keras.losses.mse
, tf.keras.metrics.MSE
, tf.keras.metrics.mse
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)
Args | |
---|---|
y_true
|
Ground truth values with shape = [batch_size, d0, .. dN] .
|
y_pred
|
The predicted values with shape = [batch_size, d0, .. dN] .
|
Returns | |
---|---|
Mean squared error values with shape = [batch_size, d0, .. dN-1] .
|