深度残差收缩网络tensorflow2.0

深度残差收缩网络是一种用于图像分类和物体识别的神经网络模型。它结合了残差网络和通道压缩技术，实现了更高的准确率和更小的模型规模。下面是使用TensorFlow 2.0实现深度残差收缩网络的简单示例代码。

首先，导入必要的库：

import tensorflow as tf
from tensorflow.keras import layers

定义一个ResNet模块的函数：

def resnet_module(input_tensor, filters, stride, reduce=False):
    shortcut = input_tensor
    bn_axis = 3
    if reduce:
        shortcut = layers.Conv2D(filters, (1, 1), strides=stride)(shortcut)
        shortcut = layers.BatchNormalization(axis=bn_axis)(shortcut)

    x = layers.Conv2D(filters, (3, 3), strides=stride, padding="same")(input_tensor)
    x = layers.BatchNormalization(axis=bn_axis)(x)
    x = layers.Activation("relu")(x)
    x = layers.Conv2D(filters, (3, 3), padding="same")(x)
    x = layers.BatchNormalization(axis=bn_axis)(x)

    x = layers.add([x, shortcut])
    x = layers.Activation("relu")(x)
    return x

接下来，定义一个深度残差收缩网络模型：

def ResNet(input_shape, num_classes):
    input_tensor = layers.Input(shape=input_shape)
    bn_axis = 3

    x = layers.Conv2D(64, (7, 7), strides=(2, 2), padding="same")(input_tensor)
    x = layers.BatchNormalization(axis=bn_axis)(x)
    x = layers.Activation("relu")(x)
    x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)

    x = resnet_module(x, 64, stride=(1, 1))
    x = resnet_module(x, 64, stride=(1, 1))

    x = resnet_module(x, 128, stride=(2, 2), reduce=True)
    x = resnet_module(x, 128, stride=(1, 1))

    x = resnet_module(x, 256, stride=(2, 2), reduce=True)
    x = resnet_module(x, 256, stride=(1, 1))

    x = resnet_module(x, 512, stride=(2, 2), reduce=True)
    x = resnet_module(x, 512, stride=(1, 1))

    x = layers.GlobalAveragePooling2D()(x)
    x = layers.Dense(num_classes, activation="softmax")(x)

    model = tf.keras.Model(inputs=input_tensor, outputs=x, name="ResNet")
    return model

最后，编译并训练模型：

model = ResNet(input_shape=(224, 224, 3), num_classes=1000)
model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.01), loss="categorical_crossentropy", metrics=["accuracy"])

# train the model
model.fit(train_data, epochs=10, validation_data=val_data)

以上代码只是一个简单示例，可以根据实际需求进行修改和优化。