support Supported planned post 2.0 Supported Custom training loop Experimental support Experimental support Support planned post 2.0 Support planned post 2.0 No support yet Supported

especially for regression, generative or higher-order models, or by using TF32 operations in pre- or post-processing steps, try to disable TF32 by setting `torch.set_float32_matmul_precision('highest')`. at /opt/pytorch/torch_tensorrt/ notebooks/vgg_qat.ipynb. ‣ The same field is also required for the post-training quantization sample to pass successfully. Please add truncate_long_and_double=True to the enhancements. ‣ PyTorch container image version 18.07 is based on PyTorch 0.4.0 upstream main branch post commit cca2476. ‣ Clip grads can be used on a single tensor directly. ‣ The precision of MSELoss

> 0.5, predict as 1 ▪ else predict as 0 ▪ minimize MSE ▪ confused? ▪ http://www.fharrell.com/post/classification/ Multi-class classification ▪ ?: ? → ? ? ? ▪ [? ? = 0 ? , ? ? = 1 ? , … , ? ? =

FLASK_ENV=development && flask run --host=0.0.0.0 打开浏览器访问测试 URL（http://localhost:5000/ping） 访问 验证码识别服务 $ curl -X POST -F image=@2140.png 'http://localhost:5000/predict' 2140.png 1459.png 6598.png “Hello TensorFlow”

复直到不能再分，这 样就可能对训练样本学习的“太好”了，把训练样本的一些特点当做所有数据 都具有的一般性质，从而导致过拟合。 剪枝的基本策略有“预剪枝”（prepruning）和“后剪枝”（post-pruning） 通过剪枝处理去掉一些分支来降低过拟合的风险。 20 C4.5的剪枝 预剪枝（prepruning） 预剪枝不仅可以降低过拟合的风险而且还可以 减少训练时间，但另一方面它是基于“贪心”

• maxlen: 整数，所有序列的最大长度。 • dtype: 输出序列的类型。 • padding: 字符串，‘pre’ 或 ‘post’ ，表示长度不足时是在序列的前端补齐还是在后端补齐。 • truncating: 字符串，‘pre’ 或 ‘post’ ，移除长度大于 maxlen 的序列的值，要么在序列前端 截断，要么在后端。 • value: 浮点数，表示用来补齐的值。 数据预处理 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 + '/publish/epoch/end/'。采用 HTTP POST ，其中的 data 参数是以 JSON 编码的事件数据字典。 参数 • root: 字符串；目标服务器的根地址。 • path: 字符串；相对于 root 的路径，事件数据被送达的地址。

if the resulting quality is within the acceptable parameters. For on-device models, TFLite offers post-training quantization as described in chapter 2. We could also incorporate compression techniques

maxlen=max_review_len,truncating='post',padding='post') x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen=max_review_len,truncating='post',padding='post') print(x_train.shape, x_test

language models and multimodal models are pretrained on large-scale multilingual and multimodal data and post-trained on quality data for aligning to human preferences. Qwen is capable of natural language understanding