通过张量流复制到角膜的归一化

Question

实际上，我正在尝试在keras上重现张量流模型，我在这个话题上真的很陌生。我想复制这些行

embedding = tf.layers.conv2d(conv6, 128, (16, 16), padding='VALID', name='embedding')
embedding = tf.reshape(embedding, (-1, 128))
embedding = embedding - tf.reduce_min(embedding, keepdims =True)
z_n = embedding/tf.reduce_max(embedding, keepdims =True)

我的实际代码是：

def conv_conv_pool(n_filters,
                   name,
                   pool=True,
                   activation=tf.nn.relu, padding='same', filters=(3,3)):
    """{Conv -> BN -> RELU}x2 -> {Pool, optional}
    Args:
        input_ (4-D Tensor): (batch_size, H, W, C)
        n_filters (list): number of filters [int, int]
        training (1-D Tensor): Boolean Tensor
        name (str): name postfix
        pool (bool): If True, MaxPool2D
        activation: Activaion functions
    Returns:
        net: output of the Convolution operations
        pool (optional): output of the max pooling operations
    """
    net = Sequential()
    for i, F in enumerate(n_filters):
        conv = Conv2D(
            filters = F,
            kernel_size = (3,3),
            padding = 'same',
            )
        net.add(conv)
        batch_norm = BatchNormalization()
        net.add(batch_norm)
        net.add(Activation('relu'))

    if pool is False:
        return net

    pool = Conv2D(
        filters = F,
        kernel_size = (3,3),
        strides = (2,2),
        padding = 'same',  
        )
    net.add(pool)
    batch_norm = BatchNormalization()
    net.add(batch_norm)
    net.add(Activation('relu'))
    return net


def model_keras():
    model = Sequential()
    model.add(conv_conv_pool(n_filters = [8, 8], name="1"))
    model.add(conv_conv_pool([32, 32], name="2"))
    model.add(conv_conv_pool([32, 32], name="3"))
    model.add(conv_conv_pool([64, 64], name="4"))
    model.add(conv_conv_pool([64, 64], name="5"))
    model.add(conv_conv_pool([128, 128], name="6", pool=False))
    return model

归一化应该在第6层之后。

我当时想使用lambda层，这正确吗？如果是，我应该怎么写？

Answer 1

我相信您想切换到使用keras作为API的tensorflow 2。您将需要安装/升级到tensorflow 2，然后您可以尝试以下方法：

import tensorflow as tf

embedding = tf.keras.layers.conv2d(conv6, 128, (16, 16), padding='VALID', 
            name='embedding')
embedding = tf.keras.layers.reshape(embedding, (-1, 128))
embedding = embedding - tf.math.reduce_min(embedding, keepdims =True)
z_n = embedding/tf.math.reduce_max(embedding, keepdims =True)

Answer 2

如果您想使用keras层api，可以创建一个自定义层，您可以在此处找到有关如何做的文档 https://www.tensorflow.org/guide/keras/custom_layers_and_models，您应该以这样的结尾：

class NormalizationLayer(layers.Layer):

  def __init__(self, filters=128):
    super(NormalizationLayer, self).__init__()
    self.filters = filters

  def call(self, inputs):
    embedding = tf.keras.layers.conv2d(inputs, self.filters, (16, 16), padding='VALID', 
            name='embedding')
    embedding = tf.keras.layers.reshape(embedding, (-1, self.filters))
    embedding = embedding - tf.math.reduce_min(embedding, keepdims =True)
    z_n = embedding/tf.math.reduce_max(embedding, keepdims =True)
    return zn