Keras Why binary classification isn't as accurate as categorical calssification - tensorflow

Question

I am trying to create a model which can tell whether there are birds in an image or not.
I was using categorical classification to train the model to recognize Bird v.s. Flowers, the results turned to be very successful in terms of recognizing these 2 classes.
BUT, when I change it to Binary Classification to detect the existence of birds in an images, the accuracy dropped dramatically.
The reason why I changed to use Binary classification is that if I
provided a dog to my Categorical Classification trained model, it
recognized the dog as a bird.
btw, here is my data set structure:
Training:
5000 images for birds and 2000 images for not-birds
Validating:
1000 images for birds and 500 images for not-birds
Someone said, the inblanced dataset will also cause problems. Is it true?
Could someone please point out where I get wrong in the following code?
def get_num_files(path):
if not os.path.exists(path):
return 0
return sum([len(files) for r, d, files in os.walk(path)])
def get_num_subfolders(path):
if not os.path.exists(path):
return 0
return sum([len(d) for r, d, files in os.walk(path)])
def create_img_generator():
return ImageDataGenerator(
preprocessing_function=preprocess_input,
rotation_range=30,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True
)
INIT_LT = 1e-3
Image_width, Image_height = 299, 299
Training_Epochs = 30
Batch_Size = 32
Number_FC_Neurons = 1024
Num_Classes = 2
train_dir = 'to my train folder'
validate_dir = 'to my validation folder'
num_train_samples = get_num_files(train_dir)
num_classes = get_num_subfolders(train_dir)
num_validate_samples = get_num_files(validate_dir)
num_epoch = Training_Epochs
batch_size = Batch_Size
train_image_gen = create_img_generator()
test_image_gen = create_img_generator()
train_generator = train_image_gen.flow_from_directory(
train_dir,
target_size=(Image_width, Image_height),
batch_size = batch_size,
seed = 42
)
validation_generator = test_image_gen.flow_from_directory(
validate_dir,
target_size=(Image_width, Image_height),
batch_size=batch_size,
seed=42
)
Inceptionv3_model = InceptionV3(weights='imagenet', include_top=False)
print('Inception v3 model without last FC loaded')
x = Inceptionv3_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(Number_FC_Neurons, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)
# model = Model(inputs=Inceptionv3_model.input, outputs=predictions)
v3model = Model(inputs=Inceptionv3_model.input, outputs=predictions)
# Use new Sequential model to add v3model and add a bath normalization layer after
model = Sequential()
model.add(v3model)
model.add(BatchNormalization()) # added normalization
print(model.summary())
print('\nFine tuning existing model')
Layers_To_Freeze = 172
for layer in model.layers[:Layers_To_Freeze]:
layer.trainable = False
for layer in model.layers[Layers_To_Freeze:]:
layer.trainable = True
optizer = Adam(lr=INIT_LT, decay=INIT_LT / Training_Epochs)
# optizer = SGD(lr=0.0001, momentum=0.9)
model.compile(optimizer=optizer, loss='binary_crossentropy', metrics=['accuracy'])
cbk_early_stopping = EarlyStopping(monitor='val_acc', mode='max')
history_transfer_learning = model.fit_generator(
train_generator,
steps_per_epoch = num_train_samples,
epochs=num_epoch,
validation_data=validation_generator,
validation_steps = num_validate_samples,
class_weight='auto',
callbacks=[cbk_early_stopping]
)
model.save('incepv3_transfer_mini_binary.h5', overwrite=True, include_optimizer=True)

Answer 1

Categorical
Use Num_Classes = 2
Use one-hot-encoded targets (example: Bird = [1, 0], Flower = [0, 1])
Use 'softmax' activation
Use 'categorical_crossentropy'
Binary
Use Num_Classes = 1
Use binary targets (example: is flower = 1 | not flower = 0)
Use 'sigmoid' activation
Use 'binary_crossentropy'
Details here: Using categorical_crossentropy for only two classes