how can I use inception_resnet_v2 architecture

Question

I'm working on a face liveness datection model, first I used mobilenet for transfer learning in the code below:

import keras
from keras import backend as K
from keras.layers.core import Dense, Activation
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.models import Model
from keras.applications import imagenet_utils
from keras.layers import Dense,GlobalAveragePooling2D
from keras.applications import MobileNet
from keras.applications.mobilenet import preprocess_input
import numpy as np
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from sklearn.metrics import classification_report, confusion_matrix

INIT_LR = 1e-5
BS = 32
EPOCHS = 20

# MobileNetV2
base_model = keras.applications.mobilenet_v2.MobileNetV2(input_shape=None, alpha=1.0, include_top=False, weights='imagenet', input_tensor=None, pooling=None, classes=2)

x=base_model.output # a luat outputul inainte de FC
x=GlobalAveragePooling2D()(x)
x=Dense(512,activation='relu')(x) #dense layer 3
preds=Dense(2,activation='softmax')(x) #final layer with softmax activation

# specify the inputs
# specify the outputs
# now a model has been created based on our architecture
new_model=Model(inputs=base_model.input, outputs=preds)

now I want to run it with inception_resnet_v2 in order to get another results to compare it with the first result, could you show me how to do it?

Answer 1

I'm working on a face liveness datection model, first I used mobilenet for transfer learning in the code below:

import keras
from keras import backend as K
from keras.layers.core import Dense, Activation
from keras.optimizers import Adam
from keras.metrics import categorical_crossentropy
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing import image
from keras.models import Model
from keras.applications import imagenet_utils
from keras.layers import Dense,GlobalAveragePooling2D
from keras.applications import MobileNet
from keras.applications.mobilenet import preprocess_input
import numpy as np
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from sklearn.metrics import classification_report, confusion_matrix

INIT_LR = 1e-5
BS = 32
EPOCHS = 20

# MobileNetV2
base_model = keras.applications.mobilenet_v2.MobileNetV2(input_shape=None, alpha=1.0, include_top=False, weights='imagenet', input_tensor=None, pooling=None, classes=2)

x=base_model.output # a luat outputul inainte de FC
x=GlobalAveragePooling2D()(x)
x=Dense(512,activation='relu')(x) #dense layer 3
preds=Dense(2,activation='softmax')(x) #final layer with softmax activation

# specify the inputs
# specify the outputs
# now a model has been created based on our architecture
new_model=Model(inputs=base_model.input, outputs=preds)

now I want to run it with inception_resnet_v2 in order to get another results to compare it with the first result, could you show me how to do it?