transformations applied separately result in a dataset 3x the original size. Can we apply N transformations to create a dataset Nx the size? What are the constraining factors? An image transformation recomputes the import layers, optimizers, metrics DROPOUT_RATE = 0.2 LEARNING_RATE = 0.0002 NUM_CLASSES = 102 def create_model(): # Initialize the core model core_args = dict(input_shape=(IMG_SIZE, IMG_SIZE, 3), include_top=False) include_top=False) core = apps.resnet50.ResNet50(**core_args) core.trainable = False # Create the full model with input, preprocessing, core and softmax layers. model = tf.keras.Sequential([ layers.Input([IMG_SIZE

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7.1. create 4.7.2. replace 4.7.3. process 4.7.4. run 4.7.5. patch 4.7.6. policy 4.7.7. secrets 4.7.8. autoscale 6.2. create-kubeconfig 5.6.3. create-api-client-config 5.7. 高级 CLI 操作 5.7.1. create-bootstrap-project-template 5.7.2. create-bootstrap-policy-file 5.7.3. create-login-template 5.7.4. create-node-config com (openshift) Password: ****** Login successful. 3 You don't have any projects. You can try to create a new project, by running $ oc new-project 4 Welcome to OpenShift! See 'oc help'

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？

(openshift) Username: user1 Password: 3 Login successful. You don't have any projects. You can try to create a new project, by running oc new-project Welcome! See 'oc help' to get started help OpenShift Container Platform 4.10 CLI 工具 工具 12 使用--help标志获取有关特定CLI命令的帮助信息： 示例： 示例：获 获取 取oc create命令的帮助信息 命令的帮助信息 输 输出示例 出示例 使用oc explain命令查看特定资源的描述信息和项信息： 示例： 示例：查 查看 看 Pod 资 资源的文档 源的文档 输 输出示例 new project new-app Create a new application ... $ oc create --help Create a resource by filename or stdin JSON and YAML formats are accepted. Usage: oc create -f FILENAME [flags] ... $

(openshift) Username: user1 Password: 3 Login successful. You don't have any projects. You can try to create a new project, by running oc new-project Welcome! See 'oc help' to get started help OpenShift Container Platform 4.8 CLI 工具 工具 12 使用--help标志获取有关特定CLI命令的帮助信息： 示例： 示例：获 获取 取oc create命令的帮助信息 命令的帮助信息 输 输出示例 出示例 使用oc explain命令查看特定资源的描述信息和项信息： 示例： 示例：查 查看 看 Pod 资 资源的文档 源的文档 输 输出示例 new project new-app Create a new application ... $ oc create --help Create a resource by filename or stdin JSON and YAML formats are accepted. Usage: oc create -f FILENAME [flags] ... $

range [-10, 10], incrementing by 2.5 each time and find the quantized values for b = 3. First, let’s create our x. # Construct the array that we wish to quantize. # We slightly exceed 10.0 to include 10 using quantization? You can leverage the np.random.uniform() function (from the numpy package) to create dummy inputs (X), weights (W) and bias (b) tensors. Using these three tensors, compute the layer 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 tensor of shape [10, 3], where 10 is the batch

在模型端，需要创建写入监控数据的 Summary 类，并在需要的时候写入监控数据。首 先通过 tf.summary.create_file_writer 创建监控对象类实例，并指定监控数据的写入目录，代 码如下： # 创建监控类，监控数据将写入 log_dir 目录 summary_writer = tf.summary.create_file_writer(log_dir) 我们以监控误差数据和可视化图片数据为例，介绍如何写入监控数据。在前向计算完 在模型端，需要创建写入监控数据的 Summary 类，并在需要的时候写入监控数据。首 先通过 tf.summary.create_file_writer 创建监控对象类实例，并指定监控数据的写入目录，代 码如下： # 创建监控类，监控数据将写入 log_dir 目录 summary_writer = tf.summary.create_file_writer(log_dir) 我们以监控误差数据和可视化图片数据为例，介绍如何写入监控数据。在前向计算完 GAN 的网络结构和算法原理。 13.2 GAN 原理 现在我们来正式介绍生成对抗网络的网络结构和训练方法。 13.2.1 网络结构 生成对抗网络包含了两个子网络：生成网络(Generator，简称 G)和判别网络 (Discriminator，简称 D)，其中生成网络 G 负责学习样本的真实分布，判别网络 D 负责将 生成网络采样的样本与真实样本区分开来。 生成网络G(?)

Critical Security Parameter CVL Component Validation List DRBG Deterministic Random Number Generator DTR Derived Test Requirements ECDSA Elliptic Curve Digital Signature Algorithm EC DH Elliptic Derivation Function KTS Key Transport Scheme KW Key Wrap NDRNG Non-Deterministic Random Number Generator NIST National Institute of Standards and Technology OE Operating Environment OS Operating as specified in Section 5 of [SP 800-133 r2]. The module employs a [SP 800-90A r1] random bit generator for creation of the seed for asymmetric key generation. The module requests a minimum number of

usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool. • pandas is a dependency of statsmodels, making it an important part argument must specified to True. Google BigQuery Enhancements • Added ability to automatically create a table/dataset using the pandas.io.gbq.to_gbq() function if the destination table/dataset does not df.B.cat.categories Out[4]: Index([u'c', u'a', u'b'], dtype='object') setting the index, will create create a CategoricalIndex In [5]: df2 = df.set_index('B') In [6]: df2.index Out[6]: CategoricalIndex([u'a'