![]() ![]() Print(tf._categorical_crossentropy(labels, pred, from_logits=True))Ĥ 其他 tf._crossentropy返回平均损失。 tf.keras.backend. I reimplemented my own 'sparse cat accuracy' out of necessity due to a bug with TPU, and confirmed this matched exactly with tf.trics. Loss_obj = tf.(reduction=tf., from_logits=True) Also, I verified sparse categorical accuracy is doing 'accumulative' averaging, not only over current batch, such that at the very end, the metrics is for over the entire dataset (1 epoch). # tf.Tensor(7.6115312576293945, shape=(), dtype=float64) What does fromlogitsTrue do in SparseCategoricalcrossEntropy loss function Ask Question Asked 3 years, 3 months ago Modified 11 months ago Viewed 44k times 44 In the documentation it has been mentioned that ypred needs to be in the range of -inf to inf when fromlogitsTrue. Loss_obj = tf.(reduction=tf., from_logits=True) Loss_obj = tf.(reduction=tf._OVER_BATCH_SIZE, Print(tf._categorical_crossentropy(labels, pred, from_logits=True)) The only difference between sparse categorical cross entropy and categorical cross entropy is the format of true labels. 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 You will start seeing loss instead of NAN. For images, you can change/edit existing training/testing sample label to missing label and execute the fit function. For almost all cases this defaults to `SUM_OVER_BATCH_SIZE`. A quick hack, if you would like to use sparse categorical entropy in these situations, add just one sample each in training and testing datasets for each missing labels. 文档: * `AUTO`: Indicates that the reduction option will be determined by the usageĬontext. ![]()
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