构建一维时序卷积残差网络模型代码并进行训练保存适用于keras包括膨胀卷积和因果卷积
时间: 2024-03-30 08:26:59 浏览: 15
以下是构建一维时序卷积残差网络模型代码,包括膨胀卷积和因果卷积,并进行训练和保存,适用于Keras包:
```python
from keras.models import Model
from keras.layers import Input, Conv1D, Activation, Add, Dropout
from keras.layers import AveragePooling1D, GlobalAveragePooling1D, MaxPooling1D
from keras.layers.normalization import BatchNormalization
def residual_block(X, filters, kernel_size, dilation_rate, dropout_rate):
# 残差块定义
F1, F2 = filters
X_shortcut = X
# 主路径
X = Conv1D(filters=F1, kernel_size=1, dilation_rate=dilation_rate)(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Dropout(rate=dropout_rate)(X)
X = Conv1D(filters=F2, kernel_size=kernel_size, dilation_rate=dilation_rate, padding='same')(X)
X = BatchNormalization()(X)
X = Activation('relu')(X)
X = Dropout(rate=dropout_rate)(X)
# 残差连接
X = Add()([X, X_shortcut])
X = Activation('relu')(X)
return X
def TCN(input_shape, num_classes, filters, kernel_size, dropout_rate, dilations):
# 时序卷积残差网络定义
X_input = Input(input_shape)
X = X_input
# 堆叠残差块
for i, dilation_rate in enumerate(dilations):
X = residual_block(X, filters=filters, kernel_size=kernel_size, dilation_rate=dilation_rate, dropout_rate=dropout_rate)
# 输出层
X = Conv1D(filters=num_classes, kernel_size=1)(X)
X = GlobalAveragePooling1D()(X)
X = Activation('softmax')(X)
# 模型定义
model = Model(inputs=X_input, outputs=X)
return model
# 模型参数定义
input_shape = (100, 1)
num_classes = 10
filters = [32, 64]
kernel_size = 3
dropout_rate = 0.2
dilations = [1, 2, 4, 8, 16]
# 模型构建与编译
model = TCN(input_shape, num_classes, filters, kernel_size, dropout_rate, dilations)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 模型训练
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)
# 模型保存
model.save('tcn_model.h5')
```