assign unique strings of bits (codes) to the symbols based on their frequency in the data. More frequent symbols are assigned smaller codes, and less frequent symbols are assigned longer codes. This is achieved it. This allows us to encode the given data in as few bits as possible, since the most frequent symbols will take the least number of bits to represent. In aggregate, this would be better than encoding Source When decoding the encoded data, we look up the code from the lookup table to retrieve the symbols back. Since the codes are unique for each symbol (in fact, they are prefix codes: no code is a prefix

sparsity, total = get_conv_block_sparsity(block) print('Block: {} Sparsity: {}% Total Weights: {}'.format(block.name, sparsity, total)) Output Block: conv_block_0 Sparsity: 0.0% Total Weights: 864 Block: up with a variable-length code, where a smaller length code is assigned to frequently occurring symbols such that the total number of bits required to encode a typical message can be minimized. Let’s model seems to have trained to a reasonable accuracy. We can now persist it to disk in the SavedModel format. import tempfile _, keras_file = tempfile.mkstemp('.h5') print('Saving model to: ', keras_file)

在下 面，我们将说明字节对编码是如何工作的。 首先，我们将符号词表初始化为所有英文小写字符、特殊的词尾符号'_'和特殊的未知符号'[UNK]'。 import collections symbols = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's' items(): symbols = token.split() for i in range(len(symbols) - 1): # “pairs”的键是两个连续符号的元组 (continues on next page) 14.6. 子词嵌入 675 (continued from previous page) pairs[symbols[i], symbols[i + 1]] += get) # 具有最大值的“pairs”键 作为基于连续符号频率的贪心方法，字节对编码将使用以下merge_symbols函数来合并最频繁的连续符号对 以产生新符号。 def merge_symbols(max_freq_pair, token_freqs, symbols): symbols.append(''.join(max_freq_pair)) new_token_freqs = dict()

into your x matrix. Before beginning Newton’s Method, we will first plot the data using different symbols to represent the two classes. In Matlab/Octave, you can separate the positive class and the negative

/tmp/.keras/ 作为备份。 Keras 配置文件是存储在 $HOME/.keras/keras.json 中的 JSON 文件。默认的配置文件如 下所示： { "image_data_format": "channels_last", "epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow" } 它包含以下字段： Conv2D [source] keras.layers.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform' 当使用该层作为模型第一层时，需要提供 input_shape 参数（整数元组，不包含 样本表示的轴） ，例如，input_shape=(128, 128, 3) 表示 128x128 RGB 图像，在 data_format="channels_last" 时。 参数 • filters: 整数，输出空间的维度（即卷积中滤波器的输出数量）。 • kernel_size: 一个整数，或者 2 个整数表示的元组或列表，指明

{ "image_data_format": "channels_last", "epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow" } Here,  image_data_format represent the data format.  epsilon represents the backend = theano in keras.json file. It is described below: keras.json { "image_data_format": "channels_last", "epsilon": 1e-07, "floatx": "float32", "backend": "theano" } information as specified below: >>> k.backend() 'tensorflow' >>> k.epsilon() 1e-07 >>> k.image_data_format() 'channels_last' >>> k.floatx() 'float32' Let us understand some of the significant backend

chat(), we directly use model.generate() # But you need to use tokenizer.apply_chat_template() to format your inputs as shown␣ �→below prompt = "Give me a short introduction to large language model." messages chat(), we directly use model.generate() # But you need to use tokenizer.apply_chat_template() to format your inputs as shown␣ �→below prompt = "Give me a short introduction to large language model." messages model named Qwen..."} ] 然后只需通过一行代码运行校准过程： 1.8. GPTQ 17 Qwen import logging logging.basicConfig( format="%(asctime)s %(levelname)s [%(name)s] %(message)s", level=logging.INFO,␣ �→datefmt="%Y-%m-%d %H:%M:%S"

search_results.append(min_loss) fmt = 'Trial: {} learning_rate: {} layer_size: {} loss: {}' print(fmt.format(trial_id, learning_rate, layer_size, min_loss)) best_trial_id = np.argmin(search_results) best_loss min(search_results) print('\n=============== Search Summary ===============') print('Best Trial: {} Loss: {}'.format(best_trial_id, best_loss)) Trial: 0 learning_rate: 0.01 layer_size: 5 loss: 0.15629929304122925 reward, accuracy = child_manager.get_rewards(config) print( 'Episode: {} Reward: {} Accuracy: {}'.format( episode, reward, accuracy ) ) # Store predicted child and its rewards controller.save_trial(predictions

enhancements. ‣ PyTorch container image version 22.06 is based on 1.13.0a0+340c412. ‣ The TF32 numerical format is enabled by default for cuBLAS and cuDNN operations starting with the 22.06 release. If you encounter the PyTorch container does not build Caffe2 anymore. If scripted models were exported in the legacy format (using our 19.09 or previous NGC containers corresponding to PyTorch 1.2.0 or previous releases) and we recommend to disable cuDNN via torch.backends.cudnn.enabled = False. ‣ Channels-last memory format is experimental in the 20.07 container. Potential convergence issues for ResNet variants are being

them. When working with deep learning models and inputs such as text, which are not in numerical format, having an algorithmic way to meaningfully represent these inputs using a small number of numerical 'NaturalPlace', 'Village', 'Animal', 'Plant', 'Album', 'Film', 'WrittenWork'] The data is in CSV format with columns: class-id, title and description. The class id is 1-indexed, and the other two fields