vertically shifted by 50px as shown in the top middle image in figure 3-6. Such a shift has two side-effects. First, a part of the image “falls off” the top edge. That information will be lost. And the second 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, transform_opts) # Show the transformed image show_image(transformed_image.astype(int)) image_path = 'file:///whalefin.png' Now, let’s go through the various image transformations with code examples

AutoTokenizer # Specify paths and hyperparameters for quantization model_path = "your_model_path" quant_path = "your_quantized_model_path" quant_config = { "zero_point": True, "q_group_size": 128, "w_bit": and model with AutoAWQ tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoAWQForCausalLM.from_pretrained(model_path, device_map="auto",␣ �→safetensors=True) 接下来，您需要准备数据以进行校准。您需要做的就是将样本放入一个列表中，其中每个样本都是一段文 最后，保存量化模型： 14 Chapter 1. 文档 Qwen model.save_quantized(quant_path, safetensors=True, shard_size="4GB") tokenizer.save_pretrained(quant_path) 然后你就可以得到一个可以用于部署的 AWQ 量化模型。玩得开心！ 1.8 GPTQ GPTQ 是一种针对类 GPT

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： : optimizer . state_dict () , ’ epoch ’ : epoch} torch . save ( state , path ) # 恢 复 模 型 checkpoint = torch . load ( path ) model . load_state_dict ( checkpoint [ ’ model ’ ] ) optimizer . load_state_dict epochs = 10 fo r t in range ( epochs ) : print ( f ”Epoch␣{ t+1}\n−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−” ) path = ’ ./ model ’ + s t r ( t ) +’ . pth ’ train_loop ( train_dataloader , model , loss_function , optimizer

返回 一个 History 对象。 异常 • RuntimeError: 如果模型从未编译。 例 def generate_arrays_from_file(path): while 1: f = open(path) for line in f: # create Numpy arrays of input data # and labels, from each line in initial_epoch: 开始训练的轮次（有助于恢复之前的训练）。 返回 一个 History 对象。 例 def generate_arrays_from_file(path): while 1: f = open(path) for line in f: # 从文件中的每一行生成输入数据和标签的 numpy 数组， x1, x2, y = process_line(line) yield 自动从被监测的数据的名字中判断出来。 11.1.8 RemoteMonitor [source] keras.callbacks.RemoteMonitor(root='http://localhost:9000', path='/publish/epoch/end/', field='data', headers=None) 将事件数据流到服务器的回调函数。 需要 requests 库。事件被默认发送到 root

tion 网址进入 Anaconda 下载页面，选择 Python 最新版本的下载链接即可下载，下载完成后安 装即可进入安装程序。如图 1.22 所示，勾选”Add Anaconda to my PATH environment variable”一项，这样可以通过命令行方式调用 Anaconda 程序。如图 1.23 所示，安装程序 询问是否连带安装 VS Code 软件，选择 Skip 即可。整个安装流程约持续 CUDA 软件是否安装成功。打开 cmd 命令行，输入“nvcc - V”，即可打印当前 CUDA 的版本信息，如图 1.29 所示，如果命令无法识别，则说明安装 失败。同时也可以从系统环境变量 Path 中找到 CUDA 10.1 的路径配置，如图 1.28 所示。 图 1.27 CUDA 安装界面-3 图 1.28 CUDA 安装结果测试-1 图 1.29 CUDA tensorflow as tf from tensorflow import keras import pandas as pd # 在线下载汽车效能数据集 dataset_path = keras.utils.get_file("auto-mpg.data", "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-

以其他格式存储的数据也可以通过类似的方式进行处理。下面我们将数据集按行写入CSV文件中。 import os os.makedirs(os.path.join('..', 'data'), exist_ok=True) data_file = os.path.join('..', 'data', 'house_tiny.csv') with open(data_file, 'w') as f: '__ �→builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', �→ '__spec__', 'bernoulli', 'beta', 'biject_to', 'binomial', 'categorical', 'cauchy', 'chi2' edu/ml/machine‐learning‐databases/housing/housing.names 180 4. 多层感知机 def download(name, cache_dir=os.path.join('..', 'data')): #@save """下载一个DATA_HUB中的文件，返回本地文件名""" assert name in DATA_HUB, f"{name} 不存在于

only 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 state_dict(), 'loss': loss, ... }, PATH) • Load: • model = TheModelClass(*args, **kwargs) optimizer = TheOptimizerClass(*args, **kwargs) checkpoint = torch.load(PATH) model.load_state_dict(checkpoint

AudioCaptcha image = ImageCaptcha(fonts=['/path/A.ttf', '/path/B.ttf’]) data = image.generate('1234’) image.write('1234', 'out.png’) audio = AudioCaptcha(voicedir='/path/to/voices’) data = audio.generate('1234’)

packages from source. A workaround is to manually install a Conda package manager, and add the conda path to your PYTHONPATH for example, using export PYTHONPATH="/opt/conda/lib/python3.8/site-packages" if packages from source. A workaround is to manually install a Conda package manager, and add the conda path to your PYTHONPATH for example, using export PYTHONPATH="/opt/conda/lib/python3.8/site-packages" if packages from source. A workaround is to manually install a Conda package manager, and add the conda path to your PYTHONPATH for example, using export PYTHONPATH="/opt/conda/lib/python3.8/site-packages" if

contrib.learn 整个模块均已被废弃： 使用 Keras 加载 MNIST 数据集 tf.kera.datasets.mnist.load_data(path=‘mnist.npz’) Arguments: • path:本地缓存 MNIST 数据集(mnist.npz)的相对路径（~/.keras/datasets） Returns： Tuple of Numpy arrays: