From 8cbd7cbe90969553ecdfe098750086bf14704988 Mon Sep 17 00:00:00 2001 From: Jonas Weinz Date: Tue, 3 Jul 2018 15:37:15 +0200 Subject: [PATCH] ability to set batch sizes to zero --- Project/simple_approach/Continous_Learner.ipynb | 2 +- Project/simple_approach/simple_twitter_learning.py | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/Project/simple_approach/Continous_Learner.ipynb b/Project/simple_approach/Continous_Learner.ipynb index e78bfff..8514c2c 100644 --- a/Project/simple_approach/Continous_Learner.ipynb +++ b/Project/simple_approach/Continous_Learner.ipynb @@ -144,7 +144,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6b0243a17f70405fb3bbb83918efd12e", + "model_id": "a4899ee1720f4db4a136a96657f3283a", "version_major": 2, "version_minor": 0 }, diff --git a/Project/simple_approach/simple_twitter_learning.py b/Project/simple_approach/simple_twitter_learning.py index 792181e..73ebd5e 100644 --- a/Project/simple_approach/simple_twitter_learning.py +++ b/Project/simple_approach/simple_twitter_learning.py @@ -633,7 +633,7 @@ class trainer(object): named_steps[s].fit = lambda self, X, y=None: self named_steps[s].fit_transform = named_steps[s].transform - if batch_size is None: + if batch_size is not None: for k in keras_batch_fitting_layer: # forcing batch fitting on keras disabled_keras_fits[k]=named_steps[k].fit @@ -641,7 +641,7 @@ class trainer(object): 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!?!?! if batch_size is None: - self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size]) + self.pm.fit(X = self.sdm.X[:max_size], y = self.sdm.y[:max_size], validation_split=0.1, epochs=n_epochs) else: n = len(self.sdm.X) // batch_size for i in range(n_epochs): @@ -659,7 +659,7 @@ class trainer(object): named_steps[s].fit = disabled_fits[s] named_steps[s].fit_transform = disabled_fit_transforms[s] - if batch_size is None: + if batch_size is not None: for k in keras_batch_fitting_layer: named_steps[k].fit = disabled_keras_fits[k]