解释一下这段代码import pdb import tensorflow as tf from matplotlib import pyplot as plt import numpy as np import os from tensorflow.keras import Model from tensorflow.keras.layers import Conv2D,BatchNormalization,Activation,MaxPool2D,Dense,Dropout,Flatten,GlobalAveragePooling2D np.set_printoptions(threshold=np.inf) class ResnetBlock(Model): def __init__(self, filters, strides=1,residual_path=False): super(ResnetBlock, self).__init__() self.filters = filters self.strides = strides self.residual_path = residual_path self.c1 = Conv2D(filters, (3, 3), strides=strides, padding='same', use_bias=False) self.b1 = BatchNormalization() self.a1 = Activation('relu') self.c2 = Conv2D(filters, (3, 3), strides=1, padding='same', use_bias=False) self.b2 = BatchNormalization() if residual_path: self.down_c1 = Conv2D(filters, (1, 1),strides=strides, padding='same', use_bias=False) self.down_b1 = BatchNormalization() self.a2 = Activation('relu') def call(self, inputs): residual = inputs x = self.c1(inputs) x = self.b1(x) x = self.a1(x) x = self.c2(x) y = self.b2(x) if self.residual_path: residual = self.down_c1(inputs) residual = self.down_b1(residual) out = self.a2(y + residual) return out class ResNet18(Model): def __init__(self, block_list, initial_filters=64): super(ResNet18, self).__init__() self.num_blocks = len(block_list) self.block_list = block_list self.out_filters = initial_filters self.c1 = Conv2D(self.out_filters, (3, 3), strides=1, padding='same', use_bias=False, kernel_initializer='he_normal') self.b1 = BatchNormalization() self.a1 = Activation('relu') self.blocks = tf.keras.models.Sequential() for block_id in range(len(block_list)): for layer_id in range(block_list[block_id]): if block_id != 0 and layer_id == 0: block = ResnetBlock(self.out_filters, strides=2, residual_path=True) else: block = ResnetBlock(self.out_filters, residual_path=False) self.blocks.add(block) self.out_filters *= 2 self.p1 = tf.keras.layers.GlobalAveragePooling2D() self.f1 = tf.keras.layers.Dense(41, activation='tanh') def call(self, inputs): x = self.c1(inputs) x = self.b1(x) x = self.a1(x) x = self.blocks(x) x = self.p1(x) y = self.f1(x) return y

这段代码实现了一个ResNet18的模型。ResNet是深度学习中非常著名的神经网络模型之一，它的主要贡献在于解决了深度神经网络中的梯度消失问题，使得神经网络可以更深更复杂。ResNet18是ResNet的一个较小规模的版本，包含了18个卷积层和全连接层。这个模型的输入是一张图片，输出是一个41维的向量，用于表示图片属于41个不同类别中的哪一个。

在代码中，首先定义了一个ResnetBlock类，用于组成ResNet18中的基本模块。每个ResnetBlock包含两个卷积层和一个残差连接，其中第一个卷积层的输出经过BatchNormalization和ReLU激活函数后作为第二个卷积层的输入，第二个卷积层的输出经过BatchNormalization后和残差连接相加后再经过ReLU激活函数。如果需要进行降采样，则在残差连接中添加一个卷积层。

接着定义了一个ResNet18类，它包含了一个初始的卷积层、一系列ResnetBlock和全局平均池化层、一个全连接层。在ResNet18的构建过程中，根据传入的block_list参数的不同，逐步增加ResnetBlock的数量和输出通道数。在每个ResnetBlock中，如果需要进行降采样，则会在第一个卷积层中设置strides=2，否则为1。最后，经过全局平均池化层和全连接层，输出最终的预测结果。

在调用模型时，首先将输入图片经过初始的卷积层、BatchNormalization和ReLU激活函数，再经过一系列ResnetBlock，最后进行全局平均池化和全连接层的计算，得到预测结果。

相关问题

解释以下这段代码：import tensorflow as tf gpus =tf.config.experimental.list_physical_devices(device_type='GPU') tf.config.experimental.set_virtual_device_configuration(gpus[0],[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)]) #import scipy.io as sio import pickle import os,random import matplotlib.pyplot as plt #import scipy.stats from tensorflow import losses from tensorflow.keras import Model from tensorflow.keras import layers import matplotlib.pyplot as plt import tensorflow as tf import numpy as np #import scipy.io as sio #import scipy.stats import math import os import pdb from tensorflow import losses from model import ResNet18 from re_dataset_real import train_image1,train_label1,test_image1,test_label1,val_image1,val_label1 from re_dataset_imag import train_image2,train_label2,test_image2,test_label2,val_image2,val_label2 def phsical_loss(y_true, y_pred): y_true =tf.cast(y_true, y_pred.dtype) loss_real=tf.keras.losses.MSE(y_true[0],y_pred[0]) loss_img= tf.keras.losses.MSE(y_true[1],y_pred[1]) amp_ture=tf.pow(y_true[0],2)+tf.pow(y_true[1],2) amp_pred=tf.pow(y_pred[0],2)+tf.pow(y_pred[1],2) loss_amp=tf.keras.losses.MSE(amp_ture,amp_pred) return loss_real+loss_img+loss_amp#两个子模型各加一个完整约束 def angle_loss(y_true, y_pred): y_true = tf.cast(y_true, y_pred.dtype) img_ture=tf.atan2(y_true[1],y_true[0]) img_pred=tf.atan2(y_pred[1],y_pred[0]) return tf.keras.losses.MAE(img_ture,img_pred) def amp_loss(y_true, y_pred): y_true = tf.cast(y_true, y_pred.dtype) amp_ture=tf.pow(y_true[0],2)+tf.pow(y_true[1],2) amp_pred=tf.pow(y_pred[0],2)+tf.pow(y_pred[1],2) loss_phsical=tf.keras.losses.MSE(amp_ture,amp_pred) return loss_phsical model_in=tf.keras.Input((16,16,1)) model_real_out=ResNet18([2,2,2,2])(model_in) model_img_out=ResNet18([2,2,2,2])(model_in) model_all=tf.keras.Model(model_in,[model_real_out,model_img_out]) model_all.compile(loss=phsical_loss, optimizer=tf.keras.optimizers.Adam(tf.keras.optimizers.schedules.InverseTimeDecay( 0.001, decay_steps=250*25, decay_rate=1, staircase=False)), metrics=['mse']) checkpoint_save_path= "C:\\Users\\Root\\Desktop\\bysj\\model_all.ckpt" if os.path.exists(checkpoint_save_path + '.index'): print('------------------load model all---------------------') model_all.load_weights(checkpoint_save_path) cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path, save_weights_only=True,save_best_only=True)

这段代码主要是实现了一个包含两个子模型的神经网络，其中每个子模型都是由 ResNet18 构成的。此外，还定义了三个损失函数 phsical_loss、angle_loss 和 amp_loss，分别用于计算物理约束、角度约束和幅度约束。其中，物理约束的损失函数 phsical_loss 是由子模型输出的实数和虚数部分的均方误差以及模长的均方误差之和。角度约束的损失函数 angle_loss 是由子模型输出的实数和虚数部分的反正切值的平均绝对误差。幅度约束的损失函数 amp_loss 是由子模型输出的模长的均方误差。最后，使用 Adam 优化器和学习率的反比时间衰减策略来编译模型，并且设置了 ModelCheckpoint 回调函数用于保存最优模型。