path: "tensorflow.losses" tf_module { member { name: "BinaryCrossentropy" mtype: "" } member { name: "BinaryFocalCrossentropy" mtype: "" } member { name: "CategoricalCrossentropy" mtype: "" } member { name: "CategoricalHinge" mtype: "" } member { name: "CosineSimilarity" mtype: "" } member { name: "Hinge" mtype: "" } member { name: "Huber" mtype: "" } member { name: "KLDivergence" mtype: "" } member { name: "LogCosh" mtype: "" } member { name: "Loss" mtype: "" } member { name: "MeanAbsoluteError" mtype: "" } member { name: "MeanAbsolutePercentageError" mtype: "" } member { name: "MeanSquaredError" mtype: "" } member { name: "MeanSquaredLogarithmicError" mtype: "" } member { name: "Poisson" mtype: "" } member { name: "Reduction" mtype: "" } member { name: "SparseCategoricalCrossentropy" mtype: "" } member { name: "SquaredHinge" mtype: "" } member_method { name: "KLD" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "MAE" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "MAPE" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "MSE" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "MSLE" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "binary_crossentropy" argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\', \'axis\'], varargs=None, keywords=None, defaults=[\'False\', \'0.0\', \'-1\'], " } member_method { name: "binary_focal_crossentropy" argspec: "args=[\'y_true\', \'y_pred\', \'apply_class_balancing\', \'alpha\', \'gamma\', \'from_logits\', \'label_smoothing\', \'axis\'], varargs=None, keywords=None, defaults=[\'False\', \'0.25\', \'2.0\', \'False\', \'0.0\', \'-1\'], " } member_method { name: "categorical_crossentropy" argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'label_smoothing\', \'axis\'], varargs=None, keywords=None, defaults=[\'False\', \'0.0\', \'-1\'], " } member_method { name: "categorical_hinge" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "cosine_similarity" argspec: "args=[\'y_true\', \'y_pred\', \'axis\'], varargs=None, keywords=None, defaults=[\'-1\'], " } member_method { name: "deserialize" argspec: "args=[\'name\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " } member_method { name: "get" argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None" } member_method { name: "hinge" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "huber" argspec: "args=[\'y_true\', \'y_pred\', \'delta\'], varargs=None, keywords=None, defaults=[\'1.0\'], " } member_method { name: "kl_divergence" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "kld" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "kullback_leibler_divergence" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "log_cosh" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "logcosh" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "mae" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "mape" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "mean_absolute_error" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "mean_absolute_percentage_error" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "mean_squared_error" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "mean_squared_logarithmic_error" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "mse" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "msle" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "poisson" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } member_method { name: "serialize" argspec: "args=[\'loss\'], varargs=None, keywords=None, defaults=None" } member_method { name: "sparse_categorical_crossentropy" argspec: "args=[\'y_true\', \'y_pred\', \'from_logits\', \'axis\', \'ignore_class\'], varargs=None, keywords=None, defaults=[\'False\', \'-1\', \'None\'], " } member_method { name: "squared_hinge" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } }