python cnn 实例_在Keras中CNN联合LSTM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))(