from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k 
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping
from keras.applications.mobilenet import DepthwiseConv2D, relu6

img_width, img_height = 256, 256
train_data_dir = "data/train"
validation_data_dir = "data/val"
nb_train_samples = 4125
nb_validation_samples = 466 
batch_size = 16
epochs = 50

model = applications.MobileNetV2(weights = "imagenet", include_top=False, input_shape = (img_width, img_height, 3))

from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k 
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping
from keras.applications.mobilenet import DepthwiseConv2D, relu6

img_width, img_height = 256, 256
train_data_dir = "data/train"
validation_data_dir = "data/val"
nb_train_samples = 4125
nb_validation_samples = 466 
batch_size = 16
epochs = 50

model = applications.MobileNetV2(weights = "imagenet", include_top=False, input_shape = (img_width, img_height, 3))
