![]() ![]() ![]() Print(tf._categorical_crossentropy(labels, pred, from_logits=True))Ĥ 其他 tf._crossentropy返回平均损失。 tf.keras.backend. Loss_obj = tf.(reduction=tf., from_logits=True) When using SparseCategoricalCrossentropy the targets are represented by the index of the category (starting from 0). The only difference is in how the targets/labels should be encoded. # tf.Tensor(2.537177085876465, shape=(), dtype=float64) SparseCategoricalCrossentropy and CategoricalCrossentropy both compute categorical cross-entropy. and AdaMax as optimiser (Kingma and Ba, 2014), a modified version of Adam with infinity norm and categorical cross-entropy as loss function. Loss_obj = tf.(reduction=tf._OVER_BATCH_SIZE, sparse without changing the resulting classier. Print(tf._categorical_crossentropy(labels, pred, from_logits=True)) Pred = tf.constant(np.random.randn(3, 10)) Print(labels) # tf.Tensor(, shape=(3,), dtype=int64) Labels = tf.constant(np.random.randint(0, 2, (3,))) Gpus = tf._physical_devices(device_type='GPU') Using `AUTO` in that case will raise an error. Used with `tf.distribute.Strategy`, outside of built-in training loops suchĪs `tf.keras` `compile` and `fit`, we expect reduction value to be 73 First of all, I realized if I need to perform binary predictions, I have to create at least two classes through performing a one-hot-encoding. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. 文档: * `AUTO`: Indicates that the reduction option will be determined by the usageĬontext. Is limited to multi-class classification. ![]() The layers of Caffe, Pytorch and Tensorflow than use a Cross-Entropy loss without an embedded activation function are: Caffe: Multinomial Logistic Loss Layer. Logistic Loss and Multinomial Logistic Loss are other names for Cross-Entropy loss. 参数 reduction: reduction=tf.时,返回每个样本的损失。 reduction=tf.时,返回所有样本的累加损失。 reduction=tf._OVER_BATCH_SIZE时,返回平均损失。 reduction=tf.时,几乎和reduction=tf._OVER_BATCH_SIZE效果一样。 See next Binary Cross-Entropy Loss section for more details. ![]()
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