在Keras中CNN联合LSTM进行分类实例
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2020-12-17
11:15:12
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在在Keras中中CNN联合联合LSTM进行分类实例进行分类实例
我就废话不多说，大家还是直接看代码吧~
def get_model():
n_classes = 6
inp=Input(shape=(40, 80))
reshape=Reshape((1,40,80))(inp)
# pre=ZeroPadding2D(padding=(1, 1))(reshape)
# 1
conv1=Convolution2D(32, 3, 3, border_mode='same',init='glorot_uniform')(reshape)
#model.add(Activation('relu'))
l1=LeakyReLU(alpha=0.33)(conv1)
conv2=ZeroPadding2D(padding=(1, 1))(l1)
conv2=Convolution2D(32, 3, 3, border_mode='same',init='glorot_uniform')(conv2)
#model.add(Activation('relu'))
l2=LeakyReLU(alpha=0.33)(conv2)
m2=MaxPooling2D((3, 3), strides=(3, 3))(l2)
d2=Dropout(0.25)(m2)
# 2
conv3=ZeroPadding2D(padding=(1, 1))(d2)
conv3=Convolution2D(64, 3, 3, border_mode='same',init='glorot_uniform')(conv3)
#model.add(Activation('relu'))
l3=LeakyReLU(alpha=0.33)(conv3)
conv4=ZeroPadding2D(padding=(1, 1))(l3)
conv4=Convolution2D(64, 3, 3, border_mode='same',init='glorot_uniform')(conv4)
#model.add(Activation('relu'))
l4=LeakyReLU(alpha=0.33)(conv4)
m4=MaxPooling2D((3, 3), strides=(3, 3))(l4)
d4=Dropout(0.25)(m4)
# 3
conv5=ZeroPadding2D(padding=(1, 1))(d4)
conv5=Convolution2D(128, 3, 3, border_mode='same',init='glorot_uniform')(conv5)
#model.add(Activation('relu'))
l5=LeakyReLU(alpha=0.33)(conv5)
conv6=ZeroPadding2D(padding=(1, 1))(l5)
conv6=Convolution2D(128, 3, 3, border_mode='same',init='glorot_uniform')(conv6)
#model.add(Activation('relu'))
l6=LeakyReLU(alpha=0.33)(conv6)
m6=MaxPooling2D((3, 3), strides=(3, 3))(l6)
d6=Dropout(0.25)(m6)
# 4
conv7=ZeroPadding2D(padding=(1, 1))(d6)
conv7=Convolution2D(256, 3, 3, border_mode='same',init='glorot_uniform')(conv7)
#model.add(Activation('relu'))
l7=LeakyReLU(alpha=0.33)(conv7)
conv8=ZeroPadding2D(padding=(1, 1))(l7)
conv8=Convolution2D(256, 3, 3, border_mode='same',init='glorot_uniform')(conv8)
#model.add(Activation('relu'))
l8=LeakyReLU(alpha=0.33)(conv8)
g=GlobalMaxPooling2D()(l8)
print("g=",g)
#g1=Flatten()(g)
lstm1=LSTM(
input_shape=(40,80),
output_dim=256,
activation='tanh',
return_sequences=False)(inp)
dl1=Dropout(0.3)(lstm1)
den1=Dense(200,activation="relu")(dl1)
#model.add(Activation('relu'))
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