face_mask_detection.py

model = Sequential()

model.add(Conv2D(32, (3, 3), padding = "same", activation='relu', input_shape=(124,124,3)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
 
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(50, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam' ,metrics=['accuracy'])
xtrain,xval,ytrain,yval=train_test_split(X, Y,train_size=0.8,random_state=0)
        featurewise_center=False,  
        samplewise_center=False,  
        featurewise_std_normalization=False,  
        samplewise_std_normalization=False,  
        zca_whitening=False,    
        rotation_range=15,    
        width_shift_range=0.1,
        height_shift_range=0.1,  
        horizontal_flip=True,  
        vertical_flip=False)
datagen.fit(xtrain)

history = model.fit_generator(datagen.flow(xtrain, ytrain, batch_size=32),
                    steps_per_epoch=xtrain.shape[0]//32,
                    epochs=50,
                    verbose=1,
                    validation_data=(xval, yval))

datagen = ImageDataGenerator(featurewise_center=...