## PyTorch ## Batch Norm 主讲人：龙良曲 ## I ntuitive explanation Activation Inputs Sigmoid Activation and Gradient  ## I ntuitive Feature scaling ## I mage Normalization ## ☀️ ☀️ ☁️ normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) Batch Normalization ## Batch Norm Layer Norm  Batch normalization $$ \tilde{z}^{i}=\frac{z^{i}-\mu}{\sigma} $$ $$ \hat{z}^{i}=\gamma\odot\tilde{z}^{i}+\beta $$ ![Image]

Threading Support • Faster • But Fat ## Threading CoreData Notification Realm Sync When Commit use Batch update ## Network ## Cache Solution NSURLConnection NSURLCache ETag HTTP ETag Field Cache-Control

satisfaction, and employee happiness 2. one of the best predictors of short lead times was small batch sizes of work b. Agile, Continuous Delivery, and the Three Ways 7 c. The First Way: The Principles context of global goals. ### ii. REDUCE BATCH SIZES 1. Another key component to creating smooth and fast flow is performing work in small batch sizes. 2. Large batch sizes result in skyrocketing levels of Development has worked on is released to production deployment. Like in manufacturing, this large batch release creates sudden, high levels of WIP and massive disruptions to all downstream work centers

ignore the first row in the weight matrix. If the input was of shape $ [n, 6] $ , where n is the batch size, and the weight matrix was of shape $ [6, 6] $ , we can now treat this problem to be of input Output Shape Param # prune_low_magnitude_conv2d_2 (None, 32, 32, 128) 147586 prune_low_magnitude_batch_no (None, 32, 32, 128) 513 prune_low_magnitude_re_lu_2 (None, 32, 32, 128) 1 Total params: 148,100 params: 73,988 Let's train our pruning enabled model and evaluate its performance. EPOCHS = 50 BATCH_SIZE = 16 # UpdatePruningStep() works in conjunction with the TFMOT pruning wrappers to update the

as parameters. It also has two hyperparameters: batch_size and epochs. We use a small batch size because our dataset has just 1020 samples. A large batch size, say 256, will result in a small number (5) tds, vds, batch_size=24, epochs=100): tds = tds.shuffle(1000, reshuffle_each_iteration=True) batch_tds = tds.batch(batch_size).prefetch(tf.data.AUTOTUNE) batch_vds = vds.batch(256).prefetch(tf ModelCheckpoint(tmp1, save_best_only=True, monitor="val_accuracy") history = model.fit( batch_tds, validation_data=batch_vds, epochs=epochs, callback=[checkpoints] ) return history Let’s run a

exercise. We use NumPy for this solution. It supports vector operations which operate on a vector (or a batch) of x variables (vectorized execution) instead of one variable at a time. Although it is possible dimension) of X as [batch size, $ D_{1} $ ], that of W as [D1, D2] and b is the bias vector with shape $ [D_{2}] $ . Hence, the shape of the result of the operation $ (XW + b) $ is [batch size, $ D_{2} across multiple runs. Next, we will create an input tensor of shape $ [10, 3] $ , where 10 is the batch size, and 3 is the input dimension ( $ D_{1} $ as stated earlier). The shape of the weights tensor

Issues A workaround for the WaveGlow training regression from our past containers is to use a fake batch dimension when calculating the log determinant via torch.logdet(W.unsqueeze(0).float()).squeeze() 6.0` A workaround for the WaveGlow training regression from our past containers is to use a fake batch dimension when calculating the log determinant via torch.logdet(W.unsqueeze(0).float()).squeeze() container. A workaround for the WaveGlow training regression from our past containers is to use a fake batch dimension when calculating the log determinant via torch.logdet(W.unsqueeze(0).float()).squeeze()

如何在多 GPU 上运行 Keras 模型? ..... 27 3.3.4.1 数据并行 ..... 27 3.3.4.2 设备并行 ..... 27 3.3.5 “sample”, “batch”, “epoch” 分别是什么? ..... 28 3.3.6 如何保存 Keras 模型? ..... 28 3.3.6.1 保存/加载整个模型（结构 + 权重 + 优化器状态） .. evaluate ..... 43 4.2.3.4 predict ..... 44 4.2.3.5 train_on_batch ..... 44 4.2.3.6 test_on_batch ..... 45 4.2.3.7 predict_on_batch ..... 45 4.2.3.8 fit_generator ..... 45 4.2.3.9 evaluate_generator evaluate ..... 51 4.3.3.4 predict ..... 52 4.3.3.5 train_on_batch ..... 52 4.3.3.6 test_on_batch ..... 53 4.3.3.7 predict_on_batch ..... 53 4.3.3.8 fit_generator ..... 54 4.3.3.9 evaluate_generator

### 1. 小批量梯度下降 ## 01 小批量梯度下降 02 优化算法 03 超参数调整和BatchNorm 04 Softmax ## 小批量梯度下降 ## 小批量梯度下降（Mini-Batch Gradient Descent） 梯度下降的每一步中，用到了一定批量的训练样本 每计算常数b次训练实例，便更新一次参数w 参数更新 $$ \begin{aligned}w_{j}:& （批量梯度下降,BGD） b=batch_size，通常是2的指数倍，常见有32,64,128等。 (小批量梯度下降,MBGD) ## 小批量梯度下降 Batch gradient descent  Mini-batch gradient descent [Image](/uploads/documents/b/5/6/7/b567ec9747c87c33d45000790224cffe/p11_1.jpg) 在第t次迭代中，该算法会照常计算当下mini-batch的微分dW，db，所以我会保留这个指数加权平均数，我们用到新符号 $ S_{dW} $ ，而不是 $ v_{dW} $ ，因此 $ S_{dW}=\beta S_{dW}+(1-\beta)dW^{2}

Parallelism 15 3.2 Flexible and Efficient Kernel Development with TileLang 16 3.3 High-Performance Batch-Invariant and Deterministic Kernel Libraries 18 3.4 FP4 Quantization-Aware Training 19 3.5 Training Language (DSL) to balance development productivity and runtime efficiency. Third, we provide efficient batch-invariant and deterministic kernel libraries to ensure bitwise reproducibility across training and positivity, getting $ M^{(0)}=\exp(\tilde{B}_{l}) $ , and then iteratively performs column and row normalization: $$ M ^ {(t)} = \mathcal {T} _ {r} \left(\mathcal {T} _ {c} \left(M ^ {(t - 1)}\right)\right)