ability to set batch sizes to zero
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84aa7f288f
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@ -144,7 +144,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "6b0243a17f70405fb3bbb83918efd12e",
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"model_id": "a4899ee1720f4db4a136a96657f3283a",
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"version_major": 2,
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"version_minor": 0
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},
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@ -633,7 +633,7 @@ class trainer(object):
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named_steps[s].fit = lambda self, X, y=None: self
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named_steps[s].fit_transform = named_steps[s].transform
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if batch_size is None:
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if batch_size is not None:
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for k in keras_batch_fitting_layer:
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# forcing batch fitting on keras
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disabled_keras_fits[k]=named_steps[k].fit
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@ -641,7 +641,7 @@ class trainer(object):
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named_steps[k].fit = lambda X, y: named_steps[k].train_on_batch(to_dense_if_sparse(X), y) # ← why has keras no sparse support on batch progressing!?!?!
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if batch_size is None:
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self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size])
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self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size], validation_split=0.1, epochs=n_epochs)
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else:
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n = len(self.sdm.X) // batch_size
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for i in range(n_epochs):
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@ -659,7 +659,7 @@ class trainer(object):
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named_steps[s].fit = disabled_fits[s]
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named_steps[s].fit_transform = disabled_fit_transforms[s]
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if batch_size is None:
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if batch_size is not None:
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for k in keras_batch_fitting_layer:
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named_steps[k].fit = disabled_keras_fits[k]
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