http://d2l.ai/chapter_multilayer-perceptrons/mlp-concise.html
There is something strange happening in here, when I change loss to
loss = lambda y_hat, y : tf.keras.losses.sparse_categorical_crossentropy(y, y_hat, from_logits=True)
I got loss more than 1,
The same is happening in previous subsection " Implementation of Multilayer Perceptron from Scratch ", when I change loss to
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
I got loss more than 1
There should be no problems because in d2l.train_epoch_ch3 when calculating loss, we check weather loss is our own defined function or instance of keras loss since keras loss takes argument y_true followed by y_pred. Then why it is happening?
Please have a look at it.
Thank you.
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