The total number of epochs for training was 200. The learning rate for each architecture was kept constant at 0.0001 with the RMSprop31 (root mean square propagation) algorithm, which was used for gradient descent optimization. Though we experimented with different batch sizes (8/16/24/ 32), a batch size of 16 was used for both training and validat- ing the deep convolutional architecture, because it gave the best result. Leaky ReLU (alpha ¼ 0.01), where negative values are occasionally allowed to propagate, was applied in convolutional layers 1 and 2. This provided nonlinearity on the output of the convolutional layers. As all our architec- tures were shallow, to prevent overfitting, both dropout32 and L2 regularization33 were applied before the classification layer. 解释

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衰变：＃Copyright 2021 by YoungWoon Cho，Danny HongThe Cooper Union for the Science and Art＃ECE471 Machine Learning Architecture

这段话主要描述了一个深度卷积神经网络的训练细节。其中，训练的总迭代次数为200次，使用了RMSprop算法作为梯度下降优化器，并设置了学习率为0.0001。在不同的batch size中，选择了16作为训练和验证的批次大小，因为它提供了最好的结果。在前两个卷积层中应用了Leaky ReLU激活函数，这使得负值有时也可以传播，提供了非线性。为了防止过拟合，模型中应用了dropout和L2正则化。
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train with base lr in the first 100 epochs # and half the lr in the last 100 epochs To train with a base learning rate for the first 100 epochs and half the learning rate for the last 100 epochs, you can use a learning rate scheduler in PyTorch. Here's an example of how you can modify the training loop in your code: import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import MultiStepLR # Define your model, criterion, and optimizer model = YourModel() criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01) # Define the number of epochs and the milestone epochs num_epochs = 200 milestones = [100] # Create a learning rate scheduler scheduler = MultiStepLR(optimizer, milestones=milestones, gamma=0.5) # Train the model for epoch in range(num_epochs): # Train with base lr for the first 100 epochs, and half the lr for the last 100 epochs if epoch >= milestones[0]: scheduler.step() for inputs, labels in train_loader: # Forward pass outputs = model(inputs) loss = criterion(outputs, labels) # Backward pass and optimization optimizer.zero_grad() loss.backward() optimizer.step() # Perform validation or testing after each epoch with torch.no_grad(): # Validation or testing code # Print training information print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}, LR: {scheduler.get_last_lr()[0]}") # Save the model or perform other operations after training In this code snippet, we create a MultiStepLR scheduler and specify the milestones as [100] and gamma as 0.5. The learning rate is halved at the specified milestone epochs. Inside the training loop, we check if the current epoch is greater than or equal to the milestone epoch, and if so, we call scheduler.step() to update the learning rate. Remember to adjust the num_epochs and other hyperparameters according to your specific requirements. 翻译成中文

为了在前100个epochs中使用基本学习率训练，并在最后100个epochs中将学习率减半，你可以在PyTorch中使用学习率调度器。这是一个示例，展示了如何修改代码中的训练循环： python import torch import torch.nn as...

DLR. A total number of 512 deep features are extracted (256 from PET images and 256 from CT images) as a byproduct of a multimodal neural network. The network is trained on CT and PET simultaneously [39], with two identical and parallel convolutional branches merged in a fully connected layer (see Fig. 4 for the details on the architecture). An internal transfer learning procedure is applied by first training the whole network on the T-stage dataset, then predicting LR by fine-tuning, i.e. retraining only the linear blocks (final blue box in Fig. 4). Fixed hyper-parameters are used to regulate the training process with Adam [43] optimizer (batch size: 32, epochs: 500, learning rate 10−3). Data augmentation procedures were used to improve the performance and reduce overfitting: i.e., minimal rotations, translations and Gaussian noise. The transformed images were resized to cubes of 64 × 64 × 64 to better fit the GPU memory size. 解释

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As shown in Figure 1, the proposed network contains four strided convolutional layers and four strided deconvo- lutional layers. The Leaky Rectified Linear Unit (LReLU) with a negative slope of 0.2 is used after each convolutional and deconvolutional layer. The residual group [38] consists of three residual blocks, and 18 residual blocks are used in GRes. The filter size is set as 11×11 pixels in the first convo- lutional layer in the encoder module and 3 × 3 in all the other convolutional and deconvolutional layers. We jointly train the MSBDN and DFF module and use the Mean Squared Error (MSE) as the loss function to constrain the network output and ground truth. The entire training process con- tains 100 epochs optimized by the ADAM solver [28 ] with β1 = 0.9 and β2 = 0.999 with a batch size of 16. The initial learning rate is set as 10−4 with a decay rate of 0.75 after every 10 epochs. All the experiments are conducted on an NVIDIA 2080Ti GPU. The source code and trained models are availabe at https://github.com/BookerDeWitt/MSBDN- DFF 翻译

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Task: - Download the UCM dataset from the official source or any reputable repository. - Preprocess the dataset by splitting it into training, validation, and test sets. Please use an 80-10-10 split, respectively. - Choose a suitable deep-learning algorithm for scene classification. You can use popular classifiers such as Convolutional Neural Networks (CNNs). - Implement the chosen algorithm in Python, using appropriate libraries. - Train the classifier using the training set and tune the hyperparameters using the validation set. - Evaluate the trained classifier on the test set using the following metrics: Accuracy: The overall classification accuracy. Precision: The ability to correctly classify positive samples. Recall: The ability to correctly identify positive samples. F1-score: The harmonic mean of precision and recall. Confusion Matrix: A table showing the number of correct and incorrect predictions for each class. Report: Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Submit your Python code along with the one-page report summarizing the results. Make sure to organize your code and report in a clear and readable manner.

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config = { 'n_epochs': 3000, # maximum number of epochs 'batch_size': 270, # mini-batch size for dataloader 'optimizer': 'SGD', # optimization algorithm (optimizer in torch.optim) 'optim_hparas': { # hyper-parameters for the optimizer (depends on which optimizer you are using) 'lr': 0.001, # learning rate of SGD 可换成adam试试 'momentum': 0.9 # momentum for SGD }, 'early_stop': 200, # early stopping epochs (the number epochs since your model's last improvement) 'save_path': 'models/model.pth' # your model will be saved here }

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2. 循环执行指定的epochs次数。 3. 在每个epoch开始之前，随机打乱训练数据。 4. 将训练数据划分成mini_batches，每个mini_batch包含指定大小的样本。 5. 对于每个mini_batch，将输入数据x和标签y合并，并使用梯度...

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It calculates the total number of steps based on the number of epochs and the batch size, and then uses the formula for epsilon with basic composition to compute the privacy budget for each epoch. ...

with torch.no_grad(): correct = 0 total = 0 for images, labels in test_loader: outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum().item() print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))其中1) batchsize=32, learning_rate = 1e-2,epochs=100, 中间层神经元依次为400,300,200,100. 2) 隐层含有ReLU()和批标准化函数。

这段代码是用来测试模型在测试集上的准确率的。首先使用了torch.no_grad()，这是一个上下文管理器，用于禁用梯度计算，可以减少显存占用和加速计算。然后在测试集上循环，将每个批次的图像输入模型中进行前向传播...

To do multiple steps, we implement a function the_loop that iterates over a dataloader. It should do a training step per batch for epochs. After one epoch, the loss on the validation set should be calculated.帮我根据这个要求补充完整下面代码中。。。的部分。 def the_loop(net, optimizer, train_loader, val_loader=None, epochs=None, swa_model=None, swa_start=5): if epochs is None: raise Exception("a training duration must be given: set epochs") log_iterval = 1 running_mean = 0. loss = Tensor([0.]).cuda() losses = [] val_losses = [] states = [] i, j = 0, 0 pbar = tqdm(train_loader, desc=f"epoch {i}", postfix={"loss": loss.item(), "step": j}) for i in range(epochs): running_mean = 0. j = 0 pbar.set_description(f"epoch {i}") pbar.refresh() pbar.reset() for j, batch in enumerate(train_loader): # implement training step by # - appending the current states to states # - doing a training_step # - appending the current loss to the losses list # - update the running_mean for logging ... if j % log_iterval == 0 and j != 0: pbar.set_postfix({"loss": running_mean.item(), "step": j}) running_mean = 0. pbar.update() if i > swa_start and swa_model is not None: swa_model.update_parameters(net) if val_loader is not None: # evaluate the current net on the validation data loader and # collect all losses in the ´val_loss´ list ... pbar.refresh() if val_loader is not None: return losses, states, val_losses return losses, states

Here is the completed code: python def the_loop(net, optimizer, train_loader, val_loader=None, epochs=None, swa_model=None, swa_start=5): if epochs is None: raise Exception("a training duration ...

opt = keras.optimizers.Adam(learning_rate=0.001) model.compile(loss = 'binary_crossentropy', optimizer = opt,metrics=METRICS) # 编译模型。由于我们做的是二元分类，所以我们指定损失函数为binary_crossentropy，以及模式为binary # 另外常见的损失函数还有mean_squared_error、categorical_crossentropy等，请阅读帮助文件。 # 求解方法我们指定用adam，还有sgd、rmsprop等可选 history_NN=model.fit(x_train.values, y_train.values, epochs = 200, batch_size = 128,validation_split=0.1)

首先指定优化器为Adam，学习率为0.001，然后指定损失函数为binary_crossentropy，并且使用模型的accuracy评估模型的性能。接着通过调用fit方法对模型进行训练，训练数据为x_train和y_train，训练轮数为200，每个...

for k in range(epochs):#k在第几次的循环中 i = np.random.randint(X.shape[0]) a = [X[i]] for l in range(len(self.weights)): #going forward network, for each layer a.append(self.activation(np.dot(a[l], self.weights[l]))) #Computer the node value for each layer (O_i) using activation function error = y[i] - a[-1] #Computer the error at the top layer deltas = [error * self.activation_deriv(a[-1])] #For output layer, Err calculation (delta is updated error) #Staring backprobagation for l in range(len(a) - 2, 0, -1): # we need to begin at the second to last layer #Compute the updated error (i,e, deltas) for each node going from top layer to input layer deltas.append(deltas[-1].dot(self.weights[l].T)*self.activation_deriv(a[l])) deltas.reverse() for i in range(len(self.weights)): layer = np.atleast_2d(a[i]) delta = np.atleast_2d(deltas[i]) self.weights[i] += learning_rate * layer.T.dot(delta) 代码含义

在Python中，"for k in range(epochs):" 意味着进行一个循环，循环变量 k 的值将在范围 [0, epochs) 中变化，每次循环都会执行其后面的语句。在这里，epochs 是一个变量或常量，表示循环的次数。

We have seen the cars2018.csv data in the 'Example: Fuel Consumption' Lecture Task 1: Install {keras} (and Tenserflow) on your computer. Make sure its linkage with Python libary is also established. (It is one of the key steps for this assignment. It may take some time if you have not get the right library installed. Here is a reference link. https://keras.rstudio.com/reference/install_keras.html) Task 2: Divide the data randomly into 80% training and 20% test, fit a neural network model on fuel consumption (mpg) with one hidden dense layer. Choose the number of hidden units, and select the right activation function and modelling parameters. Task 3: Fit another neural network with 2 hidden dense layers, i.e. stack another hidden layer on top of the pervious neural net. Choose the number of the hidden units for the second layer. Compare the results with output from Task 2. Task 4: Compare the model performance with the model discussed in the lecture, i.e. the lasso, rpart, random forest, and xgboost models. Task 5: Change the response variable from mpg to Liters/KM (i.e. 235.15/mpg), and re-fit a neural network model. Compare performance as in Task 4.请你提供这个题目的task4的代码

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代码time_start = time.time() results = list() iterations = 2001 lr = 1e-2 model = func_critic_model(input_shape=(None, train_img.shape[1]), act_func='relu') loss_func = tf.keras.losses.MeanSquaredError() alg = "gd" # alg = "gd" for kk in range(iterations): with tf.GradientTape() as tape: predict_label = model(train_img) loss_val = loss_func(predict_label, train_lbl) grads = tape.gradient(loss_val, model.trainable_variables) overall_grad = tf.concat([tf.reshape(grad, -1) for grad in grads], 0) overall_model = tf.concat([tf.reshape(weight, -1) for weight in model.weights], 0) overall_grad = overall_grad + 0.001 * overall_model ## adding a regularization term results.append(loss_val.numpy()) if alg == 'gd': overall_model -= lr * overall_grad ### gradient descent elif alg == 'gdn': ## gradient descent with nestrov's momentum overall_vv_new = overall_model - lr * overall_grad overall_model = (1 + gamma) * oerall_vv_new - gamma * overall_vv overall_vv = overall_new pass model_start = 0 for idx, weight in enumerate(model.weights): model_end = model_start + tf.size(weight) weight.assign(tf.reshape()) for grad, ww in zip(grads, model.weights): ww.assign(ww - lr * grad) if kk % 100 == 0: print(f"Iter: {kk}, loss: {loss_val:.3f}, Duration: {time.time() - time_start:.3f} sec...") input_shape = train_img.shape[1] - 1 model = tf.keras.Sequential([ tf.keras.layers.Input(shape=(input_shape,)), tf.keras.layers.Dense(30, activation="relu"), tf.keras.layers.Dense(20, activation="relu"), tf.keras.layers.Dense(1) ]) n_epochs = 20 batch_size = 100 learning_rate = 0.01 momentum = 0.9 sgd_optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=momentum) model.compile(loss="mean_squared_error", optimizer=sgd_optimizer) history = model.fit(train_img, train_lbl, epochs=n_epochs, batch_size=batch_size, validation_data=(test_img, test_lbl)) nag_optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate, momentum=momentum, nesterov=True) model.compile(loss="mean_squared_error", optimizer=nag_optimizer) history = model.fit(train_img, train_lbl, epochs=n_epochs, batch_size=batch_size, validation_data=(test_img, test_lbl))运行后报错TypeError: Missing required positional argument，如何改正

这个错误信息并没有指明是哪个函数缺少了必要的位置参数，因此需要仔细检查代码。根据代码的结构，可能是在使用tf.reshape()函数时缺少了必要的参数。 具体来说，下面这行代码中出现了这个错误： ...

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