Python keras.losses.mean_absolute_error() Examples
The following are 8 code examples for showing how to use keras.losses.mean_absolute_error(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: hyperparameter_hunter Author: HunterMcGushion File: test_keras_helper.py License: MIT License | 6 votes |
def dummy_1_build_fn(input_shape=(1,)): model = Sequential( [ Embedding(input_dim=9999, output_dim=200, input_length=100, trainable=True), SpatialDropout1D(rate=0.5), Flatten(), Dense(100, activation="relu"), Dense(1, activation="sigmoid"), ] ) model.compile( optimizer=RMSprop(lr=0.02, decay=0.001), loss=mean_absolute_error, metrics=["mean_absolute_error"], ) return model
Example 2
Project: voxelmorph Author: voxelmorph File: metrics.py License: GNU General Public License v3.0 | 5 votes |
def l1(y_true, y_pred): """ L1 metric (MAE) """ return losses.mean_absolute_error(y_true, y_pred)
Example 3
Project: UnDeepVO Author: drmaj File: losses.py License: MIT License | 5 votes |
def photometric_consistency_loss(alpha): def loss(y_true, y_pred): return alpha * ssim(y_true, y_pred) + (1 - alpha) * mean_absolute_error(y_true, y_pred) return loss
Example 4
Project: DLWP Author: jweyn File: custom.py License: MIT License | 5 votes |
def anomaly_correlation(y_true, y_pred, mean=0., regularize_mean='mse', reverse=True): """ Calculate the anomaly correlation. FOR NOW, ASSUMES THAT THE CLIMATOLOGICAL MEAN IS 0, AND THEREFORE REQUIRES DATA TO BE SCALED TO REMOVE SPATIALLY-DEPENDENT MEAN. :param y_true: Tensor: target values :param y_pred: Tensor: model-predicted values :param mean: float: subtract this global mean from all predicted and target array values. IGNORED FOR NOW. :param regularize_mean: str or None: if not None, also penalizes a form of mean squared error: global: penalize differences in the global mean spatial: penalize differences in spatially-averaged mean (last two dimensions) mse: penalize the mean squared error mae: penalize the mean absolute error :param reverse: bool: if True, inverts the loss so that -1 is the target score :return: float: anomaly correlation loss """ if regularize_mean is not None: assert regularize_mean in ['global', 'spatial', 'mse', 'mae'] a = (K.mean(y_pred * y_true) / K.sqrt(K.mean(K.square(y_pred)) * K.mean(K.square(y_true)))) if regularize_mean is not None: if regularize_mean == 'global': m = K.abs((K.mean(y_true) - K.mean(y_pred)) / K.mean(y_true)) elif regularize_mean == 'spatial': m = K.mean(K.abs((K.mean(y_true, axis=[-2, -1]) - K.mean(y_pred, axis=[-2, -1])) / K.mean(y_true, axis=[-2, -1]))) elif regularize_mean == 'mse': m = mean_squared_error(y_true, y_pred) elif regularize_mean == 'mae': m = mean_absolute_error(y_true, y_pred) if reverse: if regularize_mean is not None: return m - a else: return -a else: if regularize_mean: return a - m else: return a
Example 5
Project: faceswap Author: deepfakes File: _base.py License: GNU General Public License v3.0 | 5 votes |
def loss_dict(self): """ Return the loss dict """ loss_dict = dict(mae=losses.mean_absolute_error, mse=losses.mean_squared_error, logcosh=losses.logcosh, smooth_loss=generalized_loss, l_inf_norm=l_inf_norm, ssim=DSSIMObjective(), gmsd=gmsd_loss, pixel_gradient_diff=gradient_loss) return loss_dict
Example 6
Project: Anime-Super-Resolution Author: wmylxmj File: train.py License: MIT License | 5 votes |
def mae(self, hr, sr): margin = (tf.shape(hr)[1] - tf.shape(sr)[1]) // 2 hr_crop = tf.cond(tf.equal(margin, 0), lambda: hr, lambda: hr[:, margin:-margin, margin:-margin, :]) hr = K.in_train_phase(hr_crop, hr) hr.uses_learning_phase = True return mean_absolute_error(hr, sr)
Example 7
Project: Benchmarks Author: ECP-CANDLE File: helper.py License: MIT License | 5 votes |
def combined_loss(y_true, y_pred): ''' Uses a combination of mean_squared_error and an L1 penalty on the output of AE ''' return mse(y_true, y_pred) + 0.01*mae(0, y_pred)
Example 8
Project: DLWP Author: jweyn File: custom.py License: MIT License | 4 votes |
def anomaly_correlation_loss(mean=None, regularize_mean='mse', reverse=True): """ Create a Keras loss function for anomaly correlation. :param mean: ndarray or None: if not None, must be an array with the same shape as the expected prediction, except that the first (batch) axis should have a dimension of 1. :param regularize_mean: str or None: if not None, also penalizes a form of mean squared error: global: penalize differences in the global mean spatial: penalize differences in spatially-averaged mean (last two dimensions) mse: penalize the mean squared error mae: penalize the mean absolute error :param reverse: bool: if True, inverts the loss so that -1 is the (minimized) target score. Must be True if regularize_mean is not None. :return: method: anomaly correlation loss function """ if mean is not None: assert len(mean.shape) > 1 assert mean.shape[0] == 1 mean_tensor = K.variable(mean, name='anomaly_correlation_mean') if regularize_mean is not None: assert regularize_mean in ['global', 'spatial', 'mse', 'mae'] reverse = True def acc_loss(y_true, y_pred): if mean is not None: a = (K.mean((y_pred - mean_tensor) * (y_true - mean_tensor)) / K.sqrt(K.mean(K.square((y_pred - mean_tensor))) * K.mean(K.square((y_true - mean_tensor))))) else: a = (K.mean(y_pred * y_true) / K.sqrt(K.mean(K.square(y_pred)) * K.mean(K.square(y_true)))) if regularize_mean is not None: if regularize_mean == 'global': m = K.abs((K.mean(y_true) - K.mean(y_pred)) / K.mean(y_true)) elif regularize_mean == 'spatial': m = K.mean(K.abs((K.mean(y_true, axis=[-2, -1]) - K.mean(y_pred, axis=[-2, -1])) / K.mean(y_true, axis=[-2, -1]))) elif regularize_mean == 'mse': m = mean_squared_error(y_true, y_pred) elif regularize_mean == 'mae': m = mean_absolute_error(y_true, y_pred) if reverse: if regularize_mean is not None: return m - a else: return -a else: if regularize_mean: return a - m else: return a return acc_loss # Compatibility names