New answers tagged keras
0
Since you're using categorical cross-entropy loss, the last layer (output layer) should come with softmax activation instead of identity (as being blank in layers.Dense(4)).
model = tf.keras.Sequential([
normalize,
...,
layers.Dense(4, activation=tf.keras.activations.softmax)
])
And SparseCategoricalCrossentropy is different from ...
0
The solution for my problem was implementing Batch Renormalization: BatchNormalization(renorm=True). In addition normalizing the inputs helped a lot improving the overall performance of the neural network.
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