Batch Norm
Batch Norm 主讲人:龙良曲 Intuitive explanation Intuitive explanation Feature scaling ▪ Image Normalization ▪ Batch Normalization Batch Norm https://medium.com/syncedreview/facebook-ai-proposes-group-normalization- p-normalization- alternative-to-batch-normalization-fb0699bffae7 Pipeline nn.BatchNorm2d Class variables Test Visualization Advantages ▪ Converge faster ▪ Better performance ▪ Robust ▪ stable0 码力 | 16 页 | 1.29 MB | 1 年前3《Efficient Deep Learning Book》[EDL] Chapter 5 - Advanced Compression Techniques
we can 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 [n, 147586 _________________________________________________________________ prune_low_magnitude_batch_no (None, 32, 32, 128) 513 _________________________________________________________________ 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 the0 码力 | 34 页 | 3.18 MB | 1 年前3《Efficient Deep Learning Book》[EDL] Chapter 3 - Learning Techniques
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) train(model, 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(tmpl, save_best_only=True, monitor="val_accuracy") history = model.fit( batch_tds, validation_data=batch_vds, epochs=epochs, callbacks=[checkpoints] ) return history Let’s run a baseline0 码力 | 56 页 | 18.93 MB | 1 年前3《Efficient Deep Learning Book》[EDL] Chapter 6 - Advanced Learning Techniques - Technical Review
import tensorflow_datasets as tfds with tf.device('/job:localhost'): ds = tfds.load('ag_news_subset', try_gcs=True, shuffle_files=True, batch_size=-1) train_dataset = tf.data.Dataset.from_tensor_slices(ds['train']) datasets. BATCH_SIZE = 256 batched_train = train_dataset.shuffle(train_dataset.cardinality()).batch(BATCH_SIZE) batched_test = test_dataset.shuffle(test_dataset.cardinality()).batch(BATCH_SIZE) We branch (the upper branch in the figure) we pass it through is a sequence of multiple convolutional, batch-norm, ReLU layers. The output of both the branches is added together, and passed through a non-linearity0 码力 | 31 页 | 4.03 MB | 1 年前3《Efficient Deep Learning Book》[EDL] Chapter 4 - Efficient Architectures
vectorization layer to build the vocabulary. train_text_ds = tf.data.Dataset.from_tensor_slices(x_train).batch(512) vectorization_layer.adapt(train_text_ds) Let’s checkout the top ten words in the vocabulary create an embedding layer initialized with the word2vec_embeddings tensor created previously. The job of the embedding layer, given a list of token indices, is to look up their embeddings in the word2vec_embeddings Let’s train this model! bow_model_w2v_history = bow_model_w2v.fit( x_train_vectorized, y_train, batch_size=64, epochs=10, validation_data=(x_test_vectorized, y_test)) Epoch 1/10 313/313 [===========0 码力 | 53 页 | 3.92 MB | 1 年前3Keras: 基于 Python 的深度学习库
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? . . . . . . . . . 5 train_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.2.3.6 test_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2.3.7 predict_on_batch . . . . . 5 train_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 4.3.3.6 test_on_batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.3.3.7 predict_on_batch . . . . .0 码力 | 257 页 | 1.19 MB | 1 年前3keras tutorial
dtype=float32) batch_dot It is used to perform the product of two data in batches. Input dimension must be 2 or higher. It is shown below: >>> a_batch = k.ones(shape=(2,3)) >>> b_batch = k.ones(shape=(3 ones(shape=(3,2)) >>> c_batch = k.batch_dot(a_batch,b_batch) >>> c_batchvariable It is used to initializes a variable. Let us perform simple transpose layer will have batch size as the first dimension and so, input shape will be represented by (None, 8) and the output shape as (None, 16). Currently, batch size is None as it is not set. Batch size is usually 0 码力 | 98 页 | 1.57 MB | 1 年前3动手学深度学习 v2.0
10) 公式 (3.1.10)中的w和x都是向量。在这里,更优雅的向量表示法比系数表示法(如w1, w2, . . . , wd)更具可读 性。|B|表示每个小批量中的样本数,这也称为批量大小(batch size)。η表示学习率(learning rate)。批量 大小和学习率的值通常是手动预先指定,而不是通过模型训练得到的。这些可以调整但不在训练过程中更新 的参数称为超参数(hyperpa 本并以小批量 方式获取数据。 在下面的代码中,我们定义一个data_iter函数,该函数接收批量大小、特征矩阵和标签向量作为输入,生成 大小为batch_size的小批量。每个小批量包含一组特征和标签。 def data_iter(batch_size, features, labels): num_examples = len(features) indices = list(range(num_examples)) for i in range(0, num_examples, batch_size): batch_indices = torch.tensor( indices[i: min(i + batch_size, num_examples)]) yield features[batch_indices], labels[batch_indices] 通常,我们利用GPU并行运算的优势,处理合0 码力 | 797 页 | 29.45 MB | 1 年前3【PyTorch深度学习-龙龙老师】-测试版202112
2015 DQN AlphaGO 2016 2017 AlphaGO Zero 2019 OpenAI Five ResNet 2015 2014 VGG GooLeNet 2015 Batch Normalization 德州扑克 Pluribus 2019 机器翻译 BERT 2018 TensorFlow 发布 2015 PyTorch 0.1 发布 2017 2018 PyTorch import pyplot as plt # 绘图工具 from utils import plot_image, plot_curve, one_hot # 便捷绘图函数 batch_size = 512 # 批大小 # 训练数据集,自动从网络下载 MNIST 数据集,保存至 mnist_data 文件夹 train_db=torchvision.datasets.MNIST('mnist_data' ])) # 创建 Dataloader 对象,方便以批量形式训练,随机打乱顺序 train_loader=torch.utils.data.DataLoader(train_db, batch_size=batch_size, sh uffle=True) 通过调用 torchvision.datasets.MNIST 函数可以方便地读取 MNIST 数据集,通过 train=True 选择生成训练集还是测试机。其中训练集0 码力 | 439 页 | 29.91 MB | 1 年前3全连接神经网络实战. pytorch 版
存储样本以及它们的标签等信息,Dataset 可以使用预加载的数据集(例如 mnist), 也可以使用自定义的数据集;而 DataLoader 是把样本进行访问和索引的工具,它实现了迭代器 功能,也就是说它可以依次将 batch_size 数量的样本导出。 注意,前面已经导入过的 python 包我们就不再重复导入了。 from torch . u t i l s . data import Dataset from DataLoader 里面: # batch_size : 每 次 迭 代 取 出 的 数 据 量 # s h u f f l e : 洗 牌 的 意 思, 先 把 数 据 打 乱, 然 后 再 分 为 不 同 的 batch Chapter 1. 准备章节 9 train_dataloader = DataLoader ( training_data , batch_size =64, s h u DataLoader ( test_data , batch_size =64, s h u f f l e=True ) 我们写点程序检测一下 DataLoader: train_features , train_labels = next ( i t e r ( train_dataloader ) ) print ( f ” Feature ␣batch␣shape : ␣{ train_features0 码力 | 29 页 | 1.40 MB | 1 年前3
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