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- Dictionaryloss/lôs/
noun
- 1. the fact or process of losing something or someone: "avoiding loss of time" Similar Opposite
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Nov 13, 2019 · After reading about how to solve an ODE with neural networks following the paper Neural Ordinary Differential Equations and the blog that uses the library JAX I tried to do the same thing with "pla...
Sep 18, 2016 · However, this loss function processes all the training data equally. But in our situation, we want to process the data discriminately. For example, we have a csv file corresponding to the training data to indicate the train data is original or augmented.
Sep 20, 2019 · You need only compute your two-component loss function within a GradientTape context and then call an optimizer with the produced gradients. For example, you could create a function custom_loss which computes both losses given the arguments to each: def custom_loss(model, loss1_args, loss2_args): # model: tf.model.Keras.
Sep 18, 2019 · For this model I have a custom cosine contrastive loss function, def cosine_constrastive_loss(y_true, y_pred): cosine_distance = 1 - y_pred margin = 0.9 cdist = y_true * y_pred + (1 - y_true) * keras.backend.maximum(margin - y_pred, 0.0) return keras.backend.mean(cdist) Structurally everything runs OK with my model.
May 6, 2017 · My question is, how i can change the loss function for a custom one to train for the new classes? The loss function that i want to implement is defined as: where distillation loss corresponds to the outputs for old classes to avoid forgetting, and classification loss corresponds to the new classes.
Oct 8, 2020 · I have a dataset that has multiple labels, and I want to define a loss that depends on the labels. The labels in the dataset are stored as a dictionary, for example: y = tf.data.Dataset.from_tensor_slices({'values': [1, 2, 3], 'symbols': [4, 5, 6]}) Then I want to define a loss for each label, to later make some combination of the losses.
May 27, 2021 · I am training a PyTorch model to perform binary classification. My minority class makes up about 10% of the data, so I want to use a weighted loss function. The docs for BCELoss and CrossEntropyLos...
Oct 24, 2020 · plt.plot(mlp.loss_curve_,label="validation") #doubt. plt.legend() The resulting graph is the following: In this model, I doubt if it's the correct marked part because as long as I know one should leave apart the validation or testing set, so maybe the fit function is not correct there. The score that I got is 0.95.
Oct 10, 2017 · y_true and y_pred. The tensor y_true is the true data (or target, ground truth) you pass to the fit method. It's a conversion of the numpy array y_train into a tensor. The tensor y_pred is the data predicted (calculated, output) by your model. Usually, both y_true and y_pred have exactly the same shape.
Jun 23, 2022 · DeepLearningのコード（WideResNet）を実装しようと思い、自作のデータセットによる学習を試みているのですが、損失の計算時にエラーが出ます。 実装をメインとしており、自作データで学習をすることが目標で、アルゴリズムそのものの理解ができておらず、至らぬ点がありますが、ご教示 ...
