tf.keras.layers.normalization

b'tf.keras.layers.normalization' 是 TensorFlow 中的一种标准化层，用于在深度学习模型中对输入数据进行标准化，提高模型的训练效果和泛化能力。该层支持多种标准化方法，如 BatchNormalization 和 LayerNormalization 等。

相关问题

import tensorflow as tf def build_model(input_shape): inputs = tf.keras.layers.Input(shape=input_shape) # encoder conv1 = tf.keras.layers.Conv2D(32, (3,3), activation='relu', padding='same')(inputs) conv1 = tf.keras.layers.BatchNormalization()(conv1) conv2 = tf.keras.layers.Conv2D(32, (3,3), activation='relu', padding='same')(conv1) conv2 = tf.keras.layers.BatchNormalization()(conv2) pool1 = tf.keras.layers.MaxPooling2D((2, 2))(conv2) conv3 = tf.keras.layers.Conv2D(64, (3,3), activation='relu', padding='same')(pool1) conv3 = tf.keras.layers.BatchNormalization()(conv3) conv4 = tf.keras.layers.Conv2D(64, (3,3), activation='relu', padding='same')(conv3) conv4 = tf.keras.layers.BatchNormalization()(conv4) pool2 = tf.keras.layers.MaxPooling2D((2, 2))(conv4) conv5 = tf.keras.layers.Conv2D(128, (3,3), activation='relu', padding='same')(pool2) conv5 = tf.keras.layers.BatchNormalization()(conv5) conv6 = tf.keras.layers.Conv2D(128, (3,3), activation='relu', padding='same')(conv5) conv6 = tf.keras.layers.BatchNormalization()(conv6) pool3 = tf.keras.layers.MaxPooling2D((2, 2))(conv6) # decoder up1 = tf.keras.layers.Conv2DTranspose(64, (2,2), strides=(2,2), padding='same')(pool3) merge1 = tf.keras.layers.concatenate([conv4, up1]) conv7 = tf.keras.layers.Conv2D(64, (3,3), activation='relu', padding='same')(merge1) conv7 = tf.keras.layers.BatchNormalization()(conv7) conv8 = tf.keras.layers.Conv2D(64, (3,3), activation='relu', padding='same')(conv7) conv8 = tf.keras.layers.BatchNormalization()(conv8) up2 = tf.keras.layers.Conv2DTranspose(32, (2,2), strides=(2,2), padding='same')(conv8) merge2 = tf.keras.layers.concatenate([conv2, up2]) conv9 = tf.keras.layers.Conv2D(32, (3,3), activation='relu', padding='same')(merge2) conv9 = tf.keras.layers.BatchNormalization()(conv9) conv10 = tf.keras.layers.Conv2D(32, (3,3), activation='relu', padding='same')(conv9) conv10 = tf.keras.layers.BatchNormalization()(conv10) outputs = tf.keras.layers.Conv2D(3, (3,3), padding='same')(conv10) model = tf.keras.models.Model(inputs=inputs, outputs=outputs) return model

这段代码是使用TensorFlow库导入并定义了一个函数build_model，在该函数中使用了tf.keras.layers库的Input函数生成一个输入层，该层的输入形状由参数input_shape指定。

import tensorflow as tf class Residual(tf.keras.layers.Layer): def __init__(self, in_c, out_c): super(Residual, self).__init__() self.conv = tf.keras.Sequential([ tf.keras.layers.Conv2D(out_c, kernel_size=3, padding='same'), tf.keras.layers.BatchNormalization(), tf.keras.layers.ReLU(), tf.keras.layers.Conv2D(out_c, kernel_size=3, padding='same'), tf.keras.layers.BatchNormalization(), tf.keras.layers.ReLU() ]) self.botneck = tf.keras.layers.Conv2D(out_c, kernel_size=1) self.pool = tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)) def call(self, x): x_prim = x x = self.conv(x) x = self.botneck(x_prim) + x x = self.pool(x) return x

这是一个ResNet的残差块，使用TensorFlow实现。参数含义如下：

- `in_c`:输入张量的通道数
- `out_c`:输出张量的通道数

在`__init__`方法中，定义了一个由多个卷积、批归一化和ReLU激活函数组成的序列模型`self.conv`，以及一个$1\times 1$的卷积层`self.botneck`和一个最大池化层`self.pool`。

在`call`方法中，将输入张量`x`保存在`x_prim`中，然后通过序列模型`self.conv`进行卷积操作，再通过$1\times 1$的卷积层`self.botneck`将`x_prim`也变成`out_c`通道数的张量，并将这两个张量相加。最后，通过最大池化层`self.pool`进行下采样。