SavedModel format. import tempfile _, keras_file = tempfile.mkstemp('.h5') print('Saving model to: ', keras_file) tf.keras.models.save_model(model_wm_10, keras_file, include_optimizer=False) Saving model el) _, clustered_keras_file = tempfile.mkstemp('.h5') print('Saving clustered model to: ', clustered_keras_file) tf.keras.models.save_model(final_model, clustered_keras_file, include_optimizer=False) clustered_tflite_file = '/tmp/clustered_speech.tflite' converter = tf.lite.TFLiteConverter.from_keras_model(final_model) tflite_clustered_model = converter.convert() with open(clustered_tflite_file, 'wb') as

media/File:Moving_From_unknown_to_known_feature_spaces_based_on_TS-ELM_with_random_kernels_and_connections.tif https://commons.wikimedia.org/wiki/Category:Machine_learning_algorithms#/media/File:Movin nd_connections.tif https://commons.wikimedia.org/wiki/Category:Machine_learning_algorithms#/media/File:OPTICS.svg May be re-distributed in accordance with the terms of the CC-SA 4.0 license https://creativecommons L.P. All rights reserved. Computer Vision Tasks Modified from https://commons.wikimedia.org/wiki/File:Cats_Petunia_and_Mimosa_2004.jpg May be re-distributed in accordance with the terms of the CC-SA 4

huggingface-cli（首先需要通过命令 pip install huggingface_hub 安装它）： huggingface-cli download file> --local-dir --local-dir- �→use-symlinks False 比如： huggingface-cli download Qwen/Qwen1 slightly bad static_groups=False, sym=True, true_sequential=True, model_name_or_path=None, model_file_base_name="model" ) max_len = 8192 # Load your tokenizer and model with AutoGPTQ # To learn about modules for LoRA. By default we tune all linear layers; • lora_weight_path: the path to the weight file for LoRA; • lora_bias: the bias for LoRA; • q_lora: whether to use Q-LoRA. def maybe_zero_3(param):

normalize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 20.6 get_file . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 20.7 print_summary io_utils 中的 HDF5Matrix 类。有关详细信息，请参阅 HDF5Matrix 文档。 你也可以直接使用 HDF5 数据集： import h5py with h5py.File('input/file.hdf5', 'r') as f: x_data = f['x_data'] model.predict(x_data) 快速开始 36 3.3.19 Keras 配置文件保存在哪里？ 用于防止在某些操作中被零除的 epsilon 模糊因子。 • 默认浮点数据类型。 • 默认后端。详见 backend 文档。 同 样， 缓 存 的 数 据 集 文 件 （如 使 用 get_file() 下 载 的 文 件） 默 认 存 储 在 $HOME/.keras/datasets/ 中。 3.3.20 如何在 Keras 开发过程中获取可复现的结果？ 在模型的开发过程中，能够在

the below command: pip install TensorFlow Once we execute keras, we could see the configuration file is located at your home directory inside and go to .keras/keras.json. keras.json { "image_data_format": or float64 using set_floatx() method.  backend denotes the current backend. Suppose, if the file is not created then move to the location and create using the below steps: > cd home > mkdir Remember, you should specify .keras as its folder name and add the above configuration inside keras.json file. We can perform some pre-defined operations to know backend functions. 3. Keras ― Backend Configuration

Prerequisites ● We assume you are already familiar with… 1. Python3 ■ if-else, loop, function, file IO, class, ... ■ refs: link1, link2, link3 2. Deep Learning Basics ■ Prof. Lee’s 1st & 2nd lecture expected values ● Dataloader: groups data in batches, enables multiprocessing ● dataset = MyDataset(file) ● dataloader = DataLoader(dataset, batch_size, shuffle=True) More info about batches and shuffling from torch.utils.data import Dataset, DataLoader class MyDataset(Dataset): def __init__(self, file): self.data = ... def __getitem__(self, index): return self.data[index] def

2 + ?3?3 + ⋯ + ???? + ? 上述计算逻辑可以通过图 2.2(b)直观地展现。 ① 素材来自 https://commons.wikimedia.org/wiki/File:Neuron_Hand-tuned.svg 预览版202112 第 2 章 回归问题 2 ?1 ?2 ?3 ?? ?? ? ? ?1 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- mpg.data") 可以通过如下可视化代码绘制数据集的分布，如图 7.14 所示。 # 绘制数据集的分布，X 为 2D 坐标，y 为数据点的标签 def make_plot(X, y, plot_name, file_name=None, XX=None, YY=None, preds=None, dark=False): if (dark): plt.style.use('dark_background')

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: f.write('NumRooms,Alley,Price\n') # 列名 f.write('NA 如果没有安装pandas，只需取消对以下行的注释来安装pandas # !pip install pandas import pandas as pd data = pd.read_csv(data_file) print(data) NumRooms Alley Price 0 NaN Pave 127500 1 2.0 NaN 106000 2 4.0 NaN 178100 3 NaN 'Uniform', 'VonMises', 'Weibull', 'Wishart', '__all__', '__ �→builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', �→ '__spec__', 'bernoulli', 'beta', 'biject_to'

simple case where n = 1. Download data1.zip, and extract the files (ex1x.dat and ex1y.dat) from the zip file. The files contain some example measurements of heights for various boys between the ages of two and mul- tiple features. Download data1.zip, and extract the files (ex2x.dat and ex2y.dat) from the zip file. This is a training set of housing prices in Portland, Oregon, where the outputs y’s are the prices

The tflite_model_eval() function starts by creating a tflite interpreter, which consumes the model file content. The model_content variable holds the contents of the model that we created earlier. Then of TFLiteConverter. A call to the convert() method on the converter object generates a tflite model file content string. We referred to this string as model_content earlier. The converter object also supports