吴良超 融合 cnn+lstm
融合 CNN lstm
2023-09-14 09:15:51 时间
from keras.applications.vgg16 import VGG16
from keras.models import Sequential, Model
from keras.layers import Input, TimeDistributed, Flatten, GRU, Dense, Dropout
from keras import optimizers
def build_model():
pretrained_cnn = VGG16(weights='imagenet', include_top=False)
# pretrained_cnn.trainable = False
for layer in pretrained_cnn.layers[:-5]:
layer.trainable = False
# input shape required by pretrained_cnn
input = Input(shape = (224, 224, 3))
x = pretrained_cnn(input)
x = Flatten()(x)
x = Dense(2048)(x)
x = Dropout(0.5)(x)
pretrained_cnn = Model(inputs = input, output = x)
input_shape = (None, 224, 224, 3) # (seq_len, width, height, channel)
model = Sequential()
model.add(TimeDistributed(pretrained_cnn, input_shape=input_shape))
model.add(GRU(1024, kernel_initializer='orthogonal', bias_initializer='ones', dropout=0.5, recurrent_dropout=0.5))
model.add(Dense(categories, activation = 'softmax'))
model.compile(loss='categorical_crossentropy',
optimizer = optimizers.SGD(lr=0.01, momentum=0.9, clipnorm=1., clipvalue=0.5),
metrics=['accuracy'])
return model
keras 官方给出的例子
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.applications import InceptionV3
video=keras.Input(shape=(None,150,150,3))
cnn=InceptionV3(weights='imagenet',include_top=False,pooling='avg')
cnn.trainable=False
frame_features=layers.TimeDistributed(cnn)(video)
video_vector=layers.LSTM(256)(frame_features)
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