tf.keras.metrics.mean_squared_error  Computes the mean squared error between labels and predictions.  View aliases  Main aliases 
tf.keras.losses.MSE, tf.keras.losses.mean_squared_error, tf.keras.losses.mse, tf.keras.metrics.MSE, tf.keras.metrics.mse Compat aliases for migration See Migration guide for more details. tf.compat.v1.keras.losses.MSE, tf.compat.v1.keras.losses.mean_squared_error, tf.compat.v1.keras.losses.mse, tf.compat.v1.keras.metrics.MSE, tf.compat.v1.keras.metrics.mean_squared_error, tf.compat.v1.keras.metrics.mse  
tf.keras.metrics.mean_squared_error(
    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) Standalone 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].  
  

    
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