3.8 fit_generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2.3.9 evaluate_generator . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2.3.10 predict_generator . . . . 3.8 fit_generator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.3.3.9 evaluate_generator . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.3.3.10 predict_generator . . . . 进行批量训练 与测试。请参阅 模型文档。 或 者， 你 可 以 编 写 一 个 生 成 批 处 理 训 练 数 据 的 生 成 器， 然 后 使 用 model.fit_generator(data_generator，steps_per_epoch，epochs) 方法。 你可以在 CIFAR10 example 中找到实践代码。 3.3.10 在验证集的误差不再下降时，如何中断训练？

achieves a higher accuracy with the same number of labeled training examples. Data Augmentation is a set of techniques which leverage the original training data to generate more training examples without workflows. We start with data augmentation in the next section. Data Augmentation Data Augmentation is a set of dataset manipulation techniques to improve sample and label efficiencies of deep learning models we can kick-off the training process. The train() is simple. It takes the model, training set and validation set as parameters. It also has two hyperparameters: batch_size and epochs. We use a small batch

预览版202112 第 3 章 分类问题 2 集共 70000 张图片。其中 60000 张图片作为训练集?train(Training Set)，用来训练模型，剩 下 10000 张图片作为测试集?test(Test Set)，用来预测或者测试，训练集和测试集共同组成 了整个 MNIST 数据集。 考虑到手写数字图片包含的信息比较简单，每张图片均被缩放到28 × 28的大小，同时 gca(projection='3d') # 设置 3D 坐标轴 ax.plot_surface(X, Y, Z) # 3D 曲面图 ax.view_init(60, -30) ax.set_xlabel('x') ax.set_ylabel('y') plt.show() 预览版202112 第 7 章 反向传播算法 20 图 7.11 Himmelblau 函数 3D style.use('dark_background') else: sns.set_style("whitegrid") plt.figure(figsize=(16,12)) axes = plt.gca() axes.set(xlabel="$x_1$", ylabel="$x_2$") plt.title(plot_name

data.Dataset 加载数据 使用 tf.data.Dataset.from_tensor_slices 加载 List 使用 tf.data.Dataset.from_generator 加载 Generator 使用 tf.data.TextLineDataset 加载文本 “Hello TensorFlow” Try it！ 使用 tf.keras.Model 管理模型 历史上的 digits, available from this page, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size- normalized

dequantized weights. How different are the two outputs? Solution: We will start with the random number generator with a fixed seed to get consistent results across multiple runs. Next, we will create an input and 5 is D2. Bias is of shape [5]. The shapes are arbitrarily chosen for illustration purposes. # Set the seed so that we get the same initialization. np.random.seed(10007) def get_random_matrix(shape): technologies like the fixed-point SIMD instructions which allows data parallelism, the SSE instruction set in x86 architecture, and similar support on ARM processors as well as on specialized DSPs like the

学习模型参数的最佳值。该数据集由一些为训练而收集的样本组成，称为训练数据集（training dataset，或 称为训练集（training set））。然而，在训练数据上表现良好的模型，并不一定在“新数据集”上有同样的性 能，这里的“新数据集”通常称为测试数据集（test dataset，或称为测试集（test set））。 综上所述，可用数据集通常可以分成两部分：训练数据集用于拟合模型参数，测试数据集用于评估拟合的模 型 """使用svg格式在Jupyter中显示绘图""" backend_inline.set_matplotlib_formats('svg') 我们定义set_figsize函数来设置图表大小。注意，这里可以直接使用d2l.plt，因为导入语句 from matplotlib import pyplot as plt已标记为保存到d2l包中。 def set_figsize(figsize=(3.5, 2.5)): #@save = figsize 下面的set_axes函数用于设置由matplotlib生成图表的轴的属性。 #@save def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend): """设置matplotlib的轴""" axes.set_xlabel(xlabel) axes.set_ylabel(ylabel)

paint After learned by 5 years After learned by 10 years Finally Put it down ▪ Painter or Generator: ▪ Critic or Discriminator https://towardsdatascience.com/generative-adversarial-networks-explained-

GAN的理论与实现模型 GAN的基本原理 GAN的学习方法 GAN的衍生模型 2. GAN的理论与实现模型 13 GAN 的核心思想来源于博弈论的纳什均衡。 它设定参与游戏双方分别为一个生成器 (Generator) 和一个判别器(Discriminator)，生成器的目的是尽 量去学习真实的数据分布，而判别器的目的是尽量 正确判别输入数据是来自真实数据还是来自生成器； 为了取得游戏胜利，这两个游戏参与者需要不断优

A Search Space for n parameters is a n-dimensional region such that a point in such a region is a set of well-defined values for each of those parameters. The parameters can take discrete or continuous hyperparameters to differentiate them from model parameters. The performance of deep learning relies on a set of good hyperparameters. Some of the commonly tuned hyperparameters are the learning rate and the momentum model. HPO performs trials with different sets of hyperparameters using the model as a blackbox. The set which performs the best is chosen for full training. In the next section, we'll discuss various approaches

floatx represent the default data type float32. You can also change it to float16 or float64 using set_floatx() method.  backend denotes the current backend. Suppose, if the file is not created then required information from the data.  Split data: Split the data into training and test data set. Test data will be used to evaluate the prediction of the algorithm / Model (once the machine learn)  Fit the model: The actual learning process will be done in this phase using the training data set.  Predict result for unknown value: Predict the output for the unknown input data (other than