使用Resblock代码示例
时间: 2023-09-01 17:08:41 浏览: 45
下面是一个使用Resblock的代码示例:
```python
import tensorflow as tf
def resblock(x, filters, kernel_size, strides, padding, activation):
shortcut = x
x = tf.keras.layers.Conv2D(filters, kernel_size=kernel_size, strides=strides, padding=padding)(x)
x = tf.keras.layers.BatchNormalization()(x)
x = activation(x)
x = tf.keras.layers.Conv2D(filters, kernel_size=kernel_size, strides=1, padding=padding)(x)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.Add()([x, shortcut])
x = activation(x)
return x
inputs = tf.keras.layers.Input(shape=(224, 224, 3))
x = resblock(inputs, filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
x = resblock(x, filters=64, kernel_size=3, strides=1, padding='same', activation=tf.nn.relu)
x = tf.keras.layers.Flatten()(x)
outputs = tf.keras.layers.Dense(10, activation=tf.nn.softmax)(x)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
```
这个示例代码实现了一个简单的卷积神经网络模型,其中包含了两个Resblock模块。每个Resblock模块都包含了两个卷积层、两个批量归一化层和一个跳跃连接。在实现中,我们首先定义了一个名为resblock的函数,然后在模型中调用这个函数来构建Resblock模块。最后,我们使用这个模型对图像进行分类。