I recommende you, when creating a solution, think necessarily in context mathematical statement. Becouse of:
- It's easily save project boundaries, while you coding thought
- There're more opportunity for maneuver in space of programme
Cross-entropy for AI help train a neural network in best practice each era. Often used different Math construction, like stochastic descent method.
Weight coefficient map focus our characterictics neural network in a right way. For avoiding gross errors in the resulting values.
best_w = keras.callbacks.ModelCheckpoint('unet_best.h5',
monitor='val_loss',
verbose=0,
save_best_only=True,
save_weights_only=True,
mode='auto',
period=1)
last_w = keras.callbacks.ModelCheckpoint('unet_last.h5',
monitor='val_loss',
verbose=0,
save_best_only=False,
save_weights_only=True,
mode='auto',
period=1)
callbacks = [best_w, last_w]
It's better to create already 2 lists: best and last weights of model. This will be useful when calculating the error value.
Finish result looks, like:
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