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 18:27:00 浏览: 9
以下是一维时序卷积残差网络模型的代码,包括膨胀卷积和因果卷积,并且将模型进行了训练和保存:
```python
from keras.models import Model
from keras.layers import Input, Conv1D, Dense, Activation, Dropout, add
from keras.layers import MaxPooling1D, GlobalMaxPooling1D, concatenate
from keras.layers import Conv1DTranspose, UpSampling1D
from keras.layers import Layer, Convolution1D
from keras.initializers import glorot_uniform
def residual_block(X, filters, dilation_rate):
# Shortcut
X_shortcut = X
# Main path
X = Conv1D(filters=filters, kernel_size=1, strides=1, padding='same', dilation_rate=dilation_rate)(X)
X = Activation('relu')(X)
X = Conv1D(filters=filters, kernel_size=3, strides=1, padding='same', dilation_rate=dilation_rate)(X)
X = Activation('relu')(X)
X = Conv1D(filters=filters, kernel_size=1, strides=1, padding='same', dilation_rate=dilation_rate)(X)
# Add shortcut
X = add([X, X_shortcut])
X = Activation('relu')(X)
return X
def TCN_block(X, filters, kernel_size, dilation_rate):
# Main path
X = Conv1D(filters=filters, kernel_size=kernel_size, strides=1, padding='causal', dilation_rate=dilation_rate)(X)
X = Activation('relu')(X)
return X
def TCN_resnet(input_shape, output_shape, filters, kernel_size, dilation_rates):
# Input
X_input = Input(input_shape)
# Residual path
X_residual = X_input
for dilation_rate in dilation_rates:
X_residual = residual_block(X_residual, filters, dilation_rate)
# TCN path
X_tcn = X_input
for dilation_rate in dilation_rates:
X_tcn = TCN_block(X_tcn, filters, kernel_size, dilation_rate)
X_tcn = GlobalMaxPooling1D()(X_tcn)
# Merge
X = concatenate([X_residual, X_tcn], axis=-1)
# Output
X = Dense(output_shape, activation='softmax')(X)
# Model
model = Model(inputs=X_input, outputs=X)
return model
# Example usage
input_shape = (100, 1)
output_shape = 10
filters = 16
kernel_size = 3
dilation_rates = [1, 2, 4, 8]
model = TCN_resnet(input_shape, output_shape, filters, kernel_size, dilation_rates)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
# Train the model
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))
# Save the model
model.save('tcn_resnet_model.h5')
```
说明:
- `residual_block`函数是残差块,包括一个卷积层和一个激活函数层,并且在主路径中使用了膨胀卷积。这里使用了`add`函数将残差路径和主路径合并。
- `TCN_block`函数是TCN块,包括一个膨胀卷积层和一个激活函数层,并且在主路径中使用了因果卷积。
- `TCN_resnet`函数是整个模型,包括一个残差路径和一个TCN路径,并且在最后使用了`concatenate`函数将两个路径合并,并且使用了一个全局最大池化层作为输出层。
- 模型的训练和保存与常规的keras模型相同,不再赘述。
注意:以上代码仅供参考,实际使用时需要根据具体情况进行调整和修改。