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。

相关问题

将下面代码使用ConvRNN2D层来替换ConvLSTM2D层，并在模块__init__.py中创建类‘convrnn’ def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= tf.keras.layers.ConvRNN2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out)

请注意，ConvRNN2D层在Keras中是一个实验性的层，需要安装Tensorflow 2.0才能使用。下面是使用ConvRNN2D替换ConvLSTM2D的代码：

from tensorflow.keras.layers import ConvRNN2D

class Generator():
    def __init__(self, input_shape=(10, 128, 128, 3)):
        self.input_shape = input_shape
        
    def build_generator(self):
        def res_net(inputs, filters):
            x = inputs
            net = conv2d(x, filters // 2, (1, 1), 1)
            net = conv2d(net, filters, (3, 3), 1)
            net = net + x
            return net 
        
        def conv2d(inputs, filters, kernel_size, strides):
            x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs)
            x = tf.keras.layers.BatchNormalization()(x)
            x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
            return x
        
        d0 = tf.keras.layers.Input(shape=self.input_shape)
        out = ConvRNN2D(filters=32, kernel_size=(3,3), padding='same', return_sequences=False)(d0)
        out = tf.keras.layers.Conv2D(3, 1, 1, 'same')(out)
        return tf.keras.Model(inputs=d0, outputs=out)
        
model = Generator().build_generator()
model.summary()

请注意，ConvRNN2D层的输入形状应该是(batch_size, time_steps, rows, cols, filters)，因此我们将输入形状设置为(10, 128, 128, 3)，其中10是时间步长。ConvRNN2D层的输出形状是(batch_size, rows, cols, filters)，因此我们需要使用return_sequences=False来指示输出一个张量，而不是一个序列。

K.set_learning_phase(0) base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3), ) for layer in base_model.layers: layer.trainable=False K.set_learning_phase(1) x = base_model.output x = layers.GlobalMaxPooling2D()(x) x = layers.Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.0001))(x) x = layers.Dense(128, activation='relu', kernel_regularizer=regularizers.l2(0.0001))(x) predictions = layers.Dense(4, activation='softmax')(x) model = Model(inputs=base_model.input, outputs=predictions) model.summary()怎么在这段代码中加入动态卷积

要加入动态卷积，需要使用TensorFlow的函数式API来构建模型，并且使用tf.keras.layers.Layer创建自定义层。

下面是一个示例代码，它将动态卷积层添加到DenseNet121的顶部：

import tensorflow as tf
from tensorflow.keras.applications import DenseNet121
from tensorflow.keras.layers import Layer, Input, GlobalMaxPooling2D, Dense
from tensorflow.keras.models import Model
from tensorflow.keras.regularizers import l2

class DynamicConv2D(Layer):
    def __init__(self, filters, kernel_size, **kwargs):
        super(DynamicConv2D, self).__init__(**kwargs)
        self.filters = filters
        self.kernel_size = kernel_size

    def build(self, input_shape):
        self.kernel = self.add_weight(name='kernel',
                                      shape=(self.kernel_size, self.kernel_size, input_shape[-1], self.filters),
                                      initializer='glorot_uniform',
                                      trainable=True)

    def call(self, inputs):
        padding = (self.kernel_size - 1) // 2
        padded_inputs = tf.pad(inputs, [[0, 0], [padding, padding], [padding, padding], [0, 0]], mode='CONSTANT')
        conv_outputs = []
        for i in range(-padding, padding+1):
            for j in range(-padding, padding+1):
                shift_inputs = tf.roll(padded_inputs, shift=[i, j], axis=[1, 2])
                conv_outputs.append(tf.nn.conv2d(shift_inputs, self.kernel, strides=1, padding='VALID'))
        output = tf.reduce_max(tf.stack(conv_outputs), axis=0)
        return output

input_shape = (224, 224, 3)
inputs = Input(shape=input_shape)
base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=input_shape)(inputs)
x = GlobalMaxPooling2D()(base_model)
x = Dense(512, activation='relu', kernel_regularizer=l2(0.0001))(x)
x = Dense(128, activation='relu', kernel_regularizer=l2(0.0001))(x)
x = DynamicConv2D(filters=64, kernel_size=3)(x) # 添加动态卷积层
predictions = Dense(4, activation='softmax')(x)
model = Model(inputs=inputs, outputs=predictions)
model.summary()

在上面的代码中，DynamicConv2D类定义了一个动态卷积层。在build()方法中，它创建一个可训练的卷积核。在call()方法中，它使用TensorFlow的tf.pad()函数将输入数据进行零填充，并使用tf.nn.conv2d()函数对每个方向的偏移量执行卷积操作。然后，它使用tf.reduce_max()函数将所有偏移量的卷积结果取最大值。

要在模型中使用DynamicConv2D层，只需要将其添加到模型中即可，如上面的代码所示。