reloaded at any time. Keras 41 config = layer_1.get_config() from_config Load the layer from the configuration object of the layer. config = layer_1.get_config() reload_layer Serialize the model Keras provides methods to serialize the model into object as well as json and load it again later. They are as follows:  get_config(): Returns the model as an object. config 10,000 test images. Below code can be used to load the dataset: from keras.datasets import mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() where  Line 1 imports minst from the

samples are provided to bridge the theory and practice gap. We have prepared a few helper functions: load_image(), show_image(), transform() and transform_and_show(), which will be used to transform the images preprocessing.image import ImageDataGenerator from urllib.request import urlopen IMG_SIZE = 224 def load_image(url): with urlopen(url) as request: img_array = np.asarray(bytearray(request.read()), dtype=np transform_opts) def transform_and_show(image_path, **transform_opts): # Load the image data # The data is formatted as (H, W, C) image = load_image(image_path) # Transformed Image transformed_image = transform(image

的实例： from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM 进行对话的示例： from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto # Now you do not need to add "trust_remote_code=True" model = AutoModelForCausalLM 5-7B-Chat-AWQ ： from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat-AWQ", # the quantized

& Dataloader ● Tensors ● torch.nn: Models, Loss Functions ● torch.optim: Optimization ● Save/load models Prerequisites ● We assume you are already familiar with… 1. Python3 ■ if-else, loop, function can be found here. Training & Testing Neural Networks - in Pytorch Validation Testing Training Load Data Step 1. torch.utils.data.Dataset & torch.utils.data.DataLoader Dataset & Dataloader Training: Network Loss Function Optimization Algorithm Training Validation Testing Step 2. torch.nn.Module Load Data torch.nn – Network Layers ● Linear Layer (Fully-connected Layer) nn.Linear(in_features, out_features)

息，我们可以用下面的代码来保存和恢复模型： # 保 存 模 型 torch . save ( model . state_dict () , path ) # 恢 复 模 型 model . load_state_dict ( torch . load ( path ) ) 其中，path 是保存模型的路径。有时候我们希望能同时保存模型的一些其他信息，比如 epoch 和优化器的类型，这时我们可以生成一个状态字典： . save ( state , path ) # 恢 复 模 型 checkpoint = torch . load ( path ) model . load_state_dict ( checkpoint [ ’ model ’ ] ) optimizer . load_state_dict ( checkpoint [ ’ optimizer ’ ] ) epoch = checkpoint (9) +’ . pth ’ checkpoint = torch . load ( path ) model2 = NeuralNetwork () . to ( device ) model2 . load_state_dict ( checkpoint [ ’ model ’ ] ) optimizer . load_state_dict ( checkpoint [ ’ optimizer

我们将features和labels作为API的参数传递，并通过数据迭代 器指定batch_size。此外，布尔值is_train表示是否希望数据迭代器对象在每个迭代周期内打乱数据。 def load_array(data_arrays, batch_size, is_train=True): #@save """构造一个PyTorch数据迭代器""" dataset = data.Ten ays) return data.DataLoader(dataset, batch_size, shuffle=is_train) batch_size = 10 data_iter = load_array((features, labels), batch_size) 使用data_iter的方式与我们在 3.2节中使用data_iter函数的方式相同。为了验证是否正常工作，让我们读 3.5. 图像分类数据集 113 3.5.3 整合所有组件 现在我们定义load_data_fashion_mnist函数，用于获取和读取Fashion‐MNIST数据集。这个函数返回训练集 和验证集的数据迭代器。此外，这个函数还接受一个可选参数resize，用来将图像大小调整为另一种形状。 def load_data_fashion_mnist(batch_size, resize=None):

computation graph) • Various other functions • loss (MSE,CE etc..) • optimizers Prepare Input Data •Load data •Iterate over examples Train Model •Train weights Evaluate Model •Visualise Tensor state_dict • Save: • torch.save(model.state_dict(), PATH) • Load: • model = TheModelClass(*args, **kwargs) • model.load_state_dict(torch.load(PATH)) • model.eval() • CONVENTION IS TO SAVE MODELS USING • Load: • model = TheModelClass(*args, **kwargs) optimizer = TheOptimizerClass(*args, **kwargs) checkpoint = torch.load(PATH) model.load_state_dict(checkpoint['model_state_dict']) optimizer.load_sta

优化器状态，允许准确地从你上次结束的地方继续训练。 你可以使用 keras.models.load_model(filepath) 重新实例化模型。load_model 还将负 责使用保存的训练配置项来编译模型（除非模型从未编译过）。 例子： from keras.models import load_model model.save('my_model.h5') # 创建 HDF5 文件 文件 'my_model.h5' del model # 删除现有模型 # 返回一个编译好的模型 # 与之前那个相同 model = load_model('my_model.h5') 3.3.6.2 只保存/加载模型的结构 如果您只需要保存模型的结构，而非其权重或训练配置项，则可以执行以下操作： # 保存为 JSON json_string = model.to_json() # 保存为 30 假设你有用于实例化模型的代码，则可以将保存的权重加载到具有相同结构的模型中： model.load_weights('my_model_weights.h5') 如果你需要将权重加载到不同的结构（有一些共同层）的模型中，例如微调或迁移学习，则 可以按层的名字来加载权重： model.load_weights('my_model_weights.h5', by_name=True) 例如：

embedding_len = 100 # 词向量长度 # 加载 IMDB 数据集 (x_train, y_train), (x_test, y_test) = keras.datasets.imdb.load_data(num_words=total_words) # 将句子填充或截断到相同长度，设置为末尾填充和末尾截断方式 x_train = keras.preprocessing.sequence tensorflow.keras import datasets # 导入经典数据集加载模块 # 加载 MNIST 数据集 (x, y), (x_test, y_test) = datasets.mnist.load_data() print('x:', x.shape, 'y:', y.shape, 'x test:', x_test.shape, 'y test:', y_test) Out [66]: 返回数组的形状 x: (60000, 28, 28) y: (60000,) x test: (10000, 28, 28) y test: [7 2 1 ... 4 5 6] 通过 load_data()函数会返回相应格式的数据，对于图片数据集 MNIST、CIFAR10 等，会返 回 2 个 tuple，第一个 tuple 保存了用于训练的数据 x 和 y 训练集对象；第 2 个 tuple

Before we start, the code is available as a Jupyter notebook here. Now let’s take a look at the load_data() function in the following code snippet. It uses the TF’s handily available MNIST dataset. We expand_dims(x, 3) return x def load_data(ds=tf.keras.datasets.mnist): """Returns the processed dataset.""" (train_images, train_labels), (test_images, test_labels) = ds.load_data() # Process the images (test_x, test_y) = load_data() # You can train on the Fashion MNIST dataset, which has the exact same format # as MNIST, and is slightly harder. # (train_x, train_y), (test_x, test_y) = load_data(ds=tf.keras