如何使用 inception_resnet_v2 架构

Question

我正在研究面部活性数据 model，首先我在下面的代码中使用 mobilenet 进行迁移学习：

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)

现在我想用 inception_resnet_v2 运行它以获得另一个结果来与第一个结果进行比较，你能告诉我怎么做吗？

Answer 1

我正在研究面部活性数据 model，首先我在下面的代码中使用 mobilenet 进行迁移学习：

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)

现在我想用 inception_resnet_v2 运行它以获得另一个结果来与第一个结果进行比较，你能告诉我怎么做吗？