reorganize code

RocketFlash · Jun 19, 2020 · e57860a · e57860a
1 parent 11affca
commit e57860a
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Showing 8 changed files with 2 additions and 425 deletions.
diff --git a/embedding_net/backbones.py b/embedding_net/backbones.py
@@ -69,7 +69,6 @@ def get_backbone(input_shape,
         backbone_model = Model(
             inputs=[input_image], outputs=[x])
         x = Flatten()(x)
-        print(backbone_model.output)
         x = Dense(512, activation="relu")(x)
         x = Dropout(0.5)(x)
         encoded_output = Dense(encodings_len, activation='relu',
@@ -195,8 +194,7 @@ def pretrain_backbone_softmax(backbone_model, data_loader, params_softmax,  para
                         verbose=1, 
                         monitor=checkpoint_callback_monitor, 
                         save_best_only=True)]
-    # checkpoints_load_name = 'work_dirs/bengali_efficientnet/pretraining_model/weights/bengali_efficientnet_020_0.932969.h5'
-    # model.load_weights(checkpoints_load_name, by_name=True)
+
     history = model.fit_generator(train_generator,
                                 steps_per_epoch=steps_per_epoch,
                                 epochs=n_epochs,

diff --git a/embedding_net/data_loader.py b/embedding_net/data_loader.py
diff --git a/embedding_net/losses_and_accuracies.py b/embedding_net/losses_and_accuracies.py
@@ -49,31 +49,3 @@ def accuracy(y_true, y_pred):
     '''
     return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))
 
-
-# class tSNECallback(keras.callbacks.Callback):
-
-#     def __init__(self, save_file_name='tSNE.gif'):
-#         super(tSNECallback, self).__init__()
-#         self.save_file_name = save_file_name
-
-#     def on_train_begin(self, logs={}):
-#         self.aucs = []
-#         self.losses = []
-
-#     def on_train_end(self, logs={}):
-#         return
-
-#     def on_epoch_begin(self, epoch, logs={}):
-#         return
-
-#     def on_epoch_end(self, epoch, logs={}):
-#         self.losses.append(logs.get('loss'))
-#         y_pred = self.model.predict(self.model.validation_data[0])
-#         self.aucs.append(roc_auc_score(self.model.validation_data[1], y_pred))
-#         return
-
-#     def on_batch_begin(self, batch, logs={}):
-#         return
-
-#     def on_batch_end(self, batch, logs={}):
-#         return
diff --git a/example_signs.jpg b/example_signs.jpg
diff --git a/requirements.txt b/requirements.txt
@@ -1,6 +1,5 @@
 image-classifiers
-keras==2.2.5
-tensorflow-gpu==1.14.0
+tensorflow-gpu==2.1.0
 matplotlib
 albumentations
 scikit-learn

diff --git a/get_classification_model.py → tools/get_classification_model.py b/get_classification_model.py → tools/get_classification_model.py
diff --git a/test.py → tools/test.py b/test.py → tools/test.py
diff --git a/train.py → tools/train.py b/train.py → tools/train.py
@@ -123,7 +123,6 @@ def main():
         metric = {'output_siamese' : accuracy, 
                   'output_im1' : 'binary_accuracy', 
                   'output_im2' : 'binary_accuracy'}
-        # metric = accuracy
     else:
         model = TripletNet(cfg_params, training=True)
         train_generator = TripletsDataGenerator(embedding_model=model.base_model,
@@ -150,8 +149,6 @@ def main():
     if args.resume_from is not None:
         model.load_model(args.resume_from)
 
-    # model.load_model('/home/rauf/Desktop/sg_efficientnet-b3__simple_fold_0_005_0.9810.hdf5')
-    model.base_model.load_weights('/home/rauf/Desktop/sg_efficientnet-b3__simple_fold_0_005_0.9810.hdf5', by_name=True)
     if 'softmax' in cfg_params:
         params_softmax = cfg_params['softmax']
         params_save_paths = cfg_params['save_paths']
@@ -167,10 +164,6 @@ def main():
                                            verbose=1,
                                            use_multiprocessing=False)
 
-    # encoded_training_data = model.generate_encodings(data_loader,
-                        #  max_n_samples=50000000000000000, shuffle=False)
-    # model.save_encodings(encoded_training_data)
-
     if params_train['plot_history']:
         plot_grapths(history, plots_save_path)