update

Author: User

cnn_class2/siamese.py

@@ -425,7 +425,7 @@ def get_test_accuracy(threshold=0.85):
 valid_steps = int(np.ceil(len(test_positives) * 2 / batch_size))
 
 # fit the model
-r = model.fit_generator(
+r = model.fit(
   train_generator(),
   steps_per_epoch=train_steps,
   epochs=20,

cnn_class2/use_pretrained_weights_resnet.py

@@ -8,7 +8,7 @@
 
 from keras.layers import Input, Lambda, Dense, Flatten
 from keras.models import Model
-from keras.applications.resnet50 import ResNet50, preprocess_input
+from keras.applications.resnet import ResNet50, preprocess_input
 # from keras.applications.inception_v3 import InceptionV3, preprocess_input
 from keras.preprocessing import image
 from keras.preprocessing.image import ImageDataGenerator
@@ -21,21 +21,21 @@
 
 
 # re-size all the images to this
-IMAGE_SIZE = [224, 224] # feel free to change depending on dataset
+IMAGE_SIZE = [100, 100] # feel free to change depending on dataset
 
 # training config:
 epochs = 16
 batch_size = 32
 
 # https://www.kaggle.com/paultimothymooney/blood-cells
-train_path = '../large_files/blood_cell_images/TRAIN'
-valid_path = '../large_files/blood_cell_images/TEST'
+# train_path = '../large_files/blood_cell_images/TRAIN'
+# valid_path = '../large_files/blood_cell_images/TEST'
 
 # https://www.kaggle.com/moltean/fruits
 # train_path = '../large_files/fruits-360/Training'
 # valid_path = '../large_files/fruits-360/Validation'
-# train_path = '../large_files/fruits-360-small/Training'
-# valid_path = '../large_files/fruits-360-small/Validation'
+train_path = '../large_files/fruits-360-small/Training'
+valid_path = '../large_files/fruits-360-small/Validation'
 
 # useful for getting number of files
 image_files = glob(train_path + '/*/*.jp*g')
@@ -125,7 +125,7 @@
 
 
 # fit the model
-r = model.fit_generator(
+r = model.fit(
   train_generator,
   validation_data=valid_generator,
   epochs=epochs,

cnn_class2/use_pretrained_weights_vgg.py

@@ -123,7 +123,7 @@
 
 
 # fit the model
-r = model.fit_generator(
+r = model.fit(
   train_generator,
   validation_data=valid_generator,
   epochs=epochs,

recommenders/autorec.py

@@ -102,7 +102,7 @@ def test_generator(A, M, A_test, M_test):
 )
 
 
-r = model.fit_generator(
+r = model.fit(
   generator(A, mask),
   validation_data=test_generator(A_copy, mask_copy, A_test_copy, mask_test_copy),
   epochs=epochs,