{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "Tce3stUlHN0L" }, "outputs": [], "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "tuOe1ymfHZPu", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "qFdPvlXBOdUN" }, "source": [ "# 高效的 TensorFlow 2" ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "\n", " \n", " \n", " \n", " \n", "
在 TensorFlow.org 上查看 在 Google Colab 中运行 在 GitHub 上查看源代码 下载笔记本
" ] }, { "cell_type": "markdown", "metadata": { "id": "xHxb-dlhMIzW" }, "source": [ "## 概述\n", "\n", "本指南提供了使用 TensorFlow 2 (TF2) 编写代码的最佳做法列表，此列表专为最近从 TensorFlow 1 (TF1) 切换过来的用户编写。有关将 TF1 代码迁移到 TF2 的更多信息，请参阅[指南的迁移部分](https://tensorflow.org/guide/migrate)。" ] }, { "cell_type": "markdown", "metadata": { "id": "MUXex9ctTuDB" }, "source": [ "## 设置\n", "\n", "为本指南中的示例导入 TensorFlow 和其他依赖项。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "IqR2PQG4ZaZ0", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "import tensorflow as tf\n", "import tensorflow_datasets as tfds" ] }, { "cell_type": "markdown", "metadata": { "id": "Ngds9zateIY8" }, "source": [ "## 惯用 TensorFlow 2 的建议" ] }, { "cell_type": "markdown", "metadata": { "id": "B3RdHaroMAi4" }, "source": [ "### 将代码重构为更小的模块\n", "\n", "一种良好做法是将代码重构为根据需要调用的更小函数。为了获得最佳性能，您应当尝试在 `tf.function` 中装饰最大的计算块（请注意，由 `tf.function` 调用的嵌套 Python 函数不需要自己单独的装饰，除非您想为 `tf.function` 使用不同的 `jit_compile` 设置）。根据您的用例，这可能是多个训练步骤，甚至是整个训练循环。对于推断用例，它可能是单个模型前向传递。" ] }, { "cell_type": "markdown", "metadata": { "id": "Rua1l8et3Evd" }, "source": [ "### 调整某些 `tf.keras.optimizer` 的默认学习率\n", "\n", "\n", "\n", "在 TF2 中，某些 Keras 优化器具有不同的学习率。如果您发现模型的收敛行为发生变化，请检查默认学习率。\n", "\n", "`optimizers.SGD`、`optimizers.Adam` 或 `optimizers.RMSprop` 没有任何变更。\n", "\n", "以下优化器的默认学习率已更改：\n", "\n", "- `optimizers.Adagrad` 从 `0.01` 更改为 `0.001`\n", "- `optimizers.Adadelta` 从 `1.0` 更改为 `0.001`\n", "- `optimizers.Adamax` 从 `0.002` 更改为 `0.001`\n", "- `optimizers.Nadam` 从 `0.002` 更改为 `0.001`" ] }, { "cell_type": "markdown", "metadata": { "id": "z6LfkpsEldEV" }, "source": [ "### 使用 `tf.Module` 和 Keras 层管理变量\n", "\n", "`tf.Module` 和 `tf.keras.layers.Layer` 提供了方便的 `variables` 和 `trainable_variables` 属性，它们以递归方式收集所有因变量。这样便可轻松在使用变量的地方对它们进行本地管理。" ] }, { "cell_type": "markdown", "metadata": { "id": "WQ2U0rj1oBlc" }, "source": [ "Keras 层/模型继承自 `tf.train.Checkpointable` 并与 `@tf.function` 集成，这样便有可能从 Keras 对象直接导出 SavedModel 或为其添加检查点。您不必使用 Keras的 `Model.fit` API 来利用这些集成。\n", "\n", "阅读 Keras 指南中有关[迁移学习和微调](https://tensorflow.google.cn/guide/keras/transfer_learning#transfer_learning_fine-tuning_with_a_custom_training_loop)的部分，了解如何使用 Keras 收集相关变量的子集。" ] }, { "cell_type": "markdown", "metadata": { "id": "j34MsfxWodG6" }, "source": [ "### 结合 `tf.data.Dataset` 和 `tf.function`\n", "\n", "[TensorFlow Datasets](https://tensorflow.org/datasets) 软件包 (tfds) 包含用于将预定义数据集作为 `tf.data.Dataset` 对象加载的的实用工具。对于此示例，您可以使用 `tfds` 加载 MNIST 数据集：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "BMgxaLH74_s-", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n", "mnist_train, mnist_test = datasets['train'], datasets['test']" ] }, { "cell_type": "markdown", "metadata": { "id": "hPJhEuvj5VfR" }, "source": [ "然后，准备用于训练的数据：\n", "\n", "- 重新缩放每个图像；\n", "- 重排样本顺序。\n", "- 收集图像和标签批次。\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "StBRHtJM2S7o", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "BUFFER_SIZE = 10 # Use a much larger value for real code\n", "BATCH_SIZE = 64\n", "NUM_EPOCHS = 5\n", "\n", "\n", "def scale(image, label):\n", " image = tf.cast(image, tf.float32)\n", " image /= 255\n", "\n", " return image, label" ] }, { "cell_type": "markdown", "metadata": { "id": "SKq14zKKFAdv" }, "source": [ "为了使样本简短，将数据集修剪为仅返回 5 个批次：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_J-o4YjG2mkM", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "train_data = mnist_train.map(scale).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n", "test_data = mnist_test.map(scale).batch(BATCH_SIZE)\n", "\n", "STEPS_PER_EPOCH = 5\n", "\n", "train_data = train_data.take(STEPS_PER_EPOCH)\n", "test_data = test_data.take(STEPS_PER_EPOCH)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "XEqdkH54VM6c", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "image_batch, label_batch = next(iter(train_data))" ] }, { "cell_type": "markdown", "metadata": { "id": "loTPH2Pz4_Oj" }, "source": [ "使用常规 Python 迭代来迭代适合装入内存的训练数据。除此之外，`tf.data.Dataset` 是从磁盘流式传输训练数据的最佳方式。数据集是[可迭代对象（但不是迭代器）](https://docs.python.org/3/glossary.html#term-iterable)，就像其他 Eager Execution 中的 Python 可迭代对象一样。您可以通过将代码封装在 `tf.function` 中来充分利用数据集异步预提取/流式传输功能，此代码将 Python 迭代替换为使用 AutoGraph 的等效计算图运算。\n", "\n", "```python\n", "@tf.function\n", "def train(model, dataset, optimizer):\n", " for x, y in dataset:\n", " with tf.GradientTape() as tape:\n", " # training=True is only needed if there are layers with different\n", " # behavior during training versus inference (e.g. Dropout).\n", " prediction = model(x, training=True)\n", " loss = loss_fn(prediction, y)\n", " gradients = tape.gradient(loss, model.trainable_variables)\n", " optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n", "```\n", "\n", "如果您使用 Keras `Model.fit` API，则不必担心数据集迭代。\n", "\n", "```python\n", "model.compile(optimizer=optimizer, loss=loss_fn)\n", "model.fit(dataset)\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "mSev7vZC5GJB" }, "source": [ "\n", "\n", "### 使用 Keras 训练循环\n", "\n", "如果您不需要对训练过程进行低级控制，建议使用 Keras 的内置 `fit`、`evaluate` 和 `predict` 方法。无论实现方式（顺序、函数或子类化）如何，这些方法都能提供统一的接口来训练模型。\n", "\n", "这些方法的优点包括：\n", "\n", "- 接受 Numpy 数组、Python 生成器和 `tf.data.Datasets`。\n", "- 自动应用正则化和激活损失。\n", "- 支持 `tf.distribute`，[无论硬件配置如何](distributed_training.ipynb)，训练代码都保持不变。\n", "- 支持将任意可调用对象作为损失和指标。\n", "- 支持 `tf.keras.callbacks.TensorBoard` 之类的回调以及自定义回调。\n", "- 性能出色，可以自动使用 TensorFlow 计算图。\n", "\n", "下面是使用 `Dataset` 训练模型的示例。要详细了解工作原理，请参阅[教程](https://tensorflow.org/tutorials)。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uzHFCzd45Rae", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "model = tf.keras.Sequential([\n", " tf.keras.layers.Conv2D(32, 3, activation='relu',\n", " kernel_regularizer=tf.keras.regularizers.l2(0.02),\n", " input_shape=(28, 28, 1)),\n", " tf.keras.layers.MaxPooling2D(),\n", " tf.keras.layers.Flatten(),\n", " tf.keras.layers.Dropout(0.1),\n", " tf.keras.layers.Dense(64, activation='relu'),\n", " tf.keras.layers.BatchNormalization(),\n", " tf.keras.layers.Dense(10)\n", "])\n", "\n", "# Model is the full model w/o custom layers\n", "model.compile(optimizer='adam',\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics=['accuracy'])\n", "\n", "model.fit(train_data, epochs=NUM_EPOCHS)\n", "loss, acc = model.evaluate(test_data)\n", "\n", "print(\"Loss {}, Accuracy {}\".format(loss, acc))" ] }, { "cell_type": "markdown", "metadata": { "id": "LQTaHTuK5S5A" }, "source": [ "\n", "\n", "### 自定义训练并编写自己的循环\n", "\n", "如果 Keras 模型适合您，但您需要更大的灵活性和对训练步骤或外层训练循环的控制，您可以实现自己的训练步骤甚至整个训练循环。如需了解详情，请参阅有关[自定义 `fit`](https://tensorflow.google.cn/guide/keras/customizing_what_happens_in_fit) 的 Keras 指南。\n", "\n", "此外 ，您还可以将许多内容作为 `tf.keras.callbacks.Callback` 实现。\n", "\n", "这种方法具有[前面提到](#keras_training_loops)的许多优点，但可以让您控制训练步骤甚至外层循环。\n", "\n", "标准训练循环分为三个步骤：\n", "\n", "1. 迭代 Python 生成器或 `tf.data.Dataset` 来获得样本批次。\n", "2. 使用 `tf.GradientTape` 收集梯度。\n", "3. 使用 `tf.keras.optimizers` 之一将权重更新应用于模型的变量。\n", "\n", "请记住：\n", "\n", "- 始终在子类化层和模型的 `call` 方法上包含一个 `training` 参数。\n", "- 确保在 `training` 参数正确设置的情况下调用模型。\n", "- 根据用法，在对一批数据运行模型之前，模型变量可能不存在。\n", "- 您需要手动处理模型的正则化损失这类问题。\n", "\n", "无需运行变量初始值设定项或添加手动控制依赖项。`tf.function` 会在创建时为您处理自动控制依赖项和变量初始化。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gQooejfYlQeF", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "model = tf.keras.Sequential([\n", " tf.keras.layers.Conv2D(32, 3, activation='relu',\n", " kernel_regularizer=tf.keras.regularizers.l2(0.02),\n", " input_shape=(28, 28, 1)),\n", " tf.keras.layers.MaxPooling2D(),\n", " tf.keras.layers.Flatten(),\n", " tf.keras.layers.Dropout(0.1),\n", " tf.keras.layers.Dense(64, activation='relu'),\n", " tf.keras.layers.BatchNormalization(),\n", " tf.keras.layers.Dense(10)\n", "])\n", "\n", "optimizer = tf.keras.optimizers.Adam(0.001)\n", "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n", "\n", "@tf.function\n", "def train_step(inputs, labels):\n", " with tf.GradientTape() as tape:\n", " predictions = model(inputs, training=True)\n", " regularization_loss=tf.math.add_n(model.losses)\n", " pred_loss=loss_fn(labels, predictions)\n", " total_loss=pred_loss + regularization_loss\n", "\n", " gradients = tape.gradient(total_loss, model.trainable_variables)\n", " optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n", "\n", "for epoch in range(NUM_EPOCHS):\n", " for inputs, labels in train_data:\n", " train_step(inputs, labels)\n", " print(\"Finished epoch\", epoch)\n" ] }, { "cell_type": "markdown", "metadata": { "id": "WikxMFGgo3oZ" }, "source": [ "### 通过 Python 控制流充分利用 `tf.function`\n", "\n", "`tf.function` 提供了一种将依赖于数据的控制流转换为计算图模式等效项（如 `tf.cond` 和 `tf.while_loop`）的方法。\n", "\n", "数据依赖控制流出现的一个常见位置是序列模型。`tf.keras.layers.RNN` 封装一个 RNN 单元，允许您以静态或动态方式展开递归。例如，您可以按照下文所述重新实现动态展开。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "n5UebfChRu4T", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "class DynamicRNN(tf.keras.Model):\n", "\n", " def __init__(self, rnn_cell):\n", " super(DynamicRNN, self).__init__(self)\n", " self.cell = rnn_cell\n", "\n", " @tf.function(input_signature=[tf.TensorSpec(dtype=tf.float32, shape=[None, None, 3])])\n", " def call(self, input_data):\n", "\n", " # [batch, time, features] -> [time, batch, features]\n", " input_data = tf.transpose(input_data, [1, 0, 2])\n", " timesteps = tf.shape(input_data)[0]\n", " batch_size = tf.shape(input_data)[1]\n", " outputs = tf.TensorArray(tf.float32, timesteps)\n", " state = self.cell.get_initial_state(batch_size = batch_size, dtype=tf.float32)\n", " for i in tf.range(timesteps):\n", " output, state = self.cell(input_data[i], state)\n", " outputs = outputs.write(i, output)\n", " return tf.transpose(outputs.stack(), [1, 0, 2]), state" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NhBI_SGKQVIB", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "lstm_cell = tf.keras.layers.LSTMCell(units = 13)\n", "\n", "my_rnn = DynamicRNN(lstm_cell)\n", "outputs, state = my_rnn(tf.random.normal(shape=[10,20,3]))\n", "print(outputs.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "du7bn3NX7iIr" }, "source": [ "阅读 [`tf.function` 指南](https://tensorflow.google.cn/guide/function)以了解更多信息。" ] }, { "cell_type": "markdown", "metadata": { "id": "SUAYhgL_NomT" }, "source": [ "### 新型指标和损失\n", "\n", "指标和损失均为对象，两者都在 Eager 模式下工作，且都位于 `tf.function` 中。\n", "\n", "损失对象是可调用对象，并使用 (`y_true`, `y_pred`) 作为参数：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Pf5gcwMzNs8F", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "cce = tf.keras.losses.CategoricalCrossentropy(from_logits=True)\n", "cce([[1, 0]], [[-1.0,3.0]]).numpy()" ] }, { "cell_type": "markdown", "metadata": { "id": "a89m-wRfxyfV" }, "source": [ "#### 使用指标收集和显示数据\n", "\n", "您可以使用 `tf.metrics` 聚合数据，使用 `tf.summary` 记录摘要并使用上下文管理器将其重定向到编写器。摘要会直接发送到编写器，这意味着您必须在调用点提供 `step` 值。\n", "\n", "```python\n", "summary_writer = tf.summary.create_file_writer('/tmp/summaries')\n", "with summary_writer.as_default():\n", " tf.summary.scalar('loss', 0.1, step=42)\n", "```\n", "\n", "要在将数据记录为摘要之前对其进行聚合，请使用 `tf.metrics`。指标是有状态的；它们积累值并在您调用 `result` 方法（例如 `Mean.result`）时返回累积结果。可以使用 `Model.reset_states` 清除累积值。\n", "\n", "```python\n", "def train(model, optimizer, dataset, log_freq=10):\n", " avg_loss = tf.keras.metrics.Mean(name='loss', dtype=tf.float32)\n", " for images, labels in dataset:\n", " loss = train_step(model, optimizer, images, labels)\n", " avg_loss.update_state(loss)\n", " if tf.equal(optimizer.iterations % log_freq, 0):\n", " tf.summary.scalar('loss', avg_loss.result(), step=optimizer.iterations)\n", " avg_loss.reset_states()\n", "\n", "def test(model, test_x, test_y, step_num):\n", " # training=False is only needed if there are layers with different\n", " # behavior during training versus inference (e.g. Dropout).\n", " loss = loss_fn(model(test_x, training=False), test_y)\n", " tf.summary.scalar('loss', loss, step=step_num)\n", "\n", "train_summary_writer = tf.summary.create_file_writer('/tmp/summaries/train')\n", "test_summary_writer = tf.summary.create_file_writer('/tmp/summaries/test')\n", "\n", "with train_summary_writer.as_default():\n", " train(model, optimizer, dataset)\n", "\n", "with test_summary_writer.as_default():\n", " test(model, test_x, test_y, optimizer.iterations)\n", "```\n", "\n", "通过将 TensorBoard 指向摘要日志目录来呈现生成的摘要：\n", "\n", "```shell\n", "tensorboard --logdir /tmp/summaries\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "0tx7FyM_RHwJ" }, "source": [ "使用 `tf.summary` API 编写要在 TensorBoard 中呈现的摘要数据。有关更多信息，请阅读 `tf.summary` 指南。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HAbA0fKW58CH", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "# Create the metrics\n", "loss_metric = tf.keras.metrics.Mean(name='train_loss')\n", "accuracy_metric = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n", "\n", "@tf.function\n", "def train_step(inputs, labels):\n", " with tf.GradientTape() as tape:\n", " predictions = model(inputs, training=True)\n", " regularization_loss=tf.math.add_n(model.losses)\n", " pred_loss=loss_fn(labels, predictions)\n", " total_loss=pred_loss + regularization_loss\n", "\n", " gradients = tape.gradient(total_loss, model.trainable_variables)\n", " optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n", " # Update the metrics\n", " loss_metric.update_state(total_loss)\n", " accuracy_metric.update_state(labels, predictions)\n", "\n", "\n", "for epoch in range(NUM_EPOCHS):\n", " # Reset the metrics\n", " loss_metric.reset_states()\n", " accuracy_metric.reset_states()\n", "\n", " for inputs, labels in train_data:\n", " train_step(inputs, labels)\n", " # Get the metric results\n", " mean_loss=loss_metric.result()\n", " mean_accuracy = accuracy_metric.result()\n", "\n", " print('Epoch: ', epoch)\n", " print(' loss: {:.3f}'.format(mean_loss))\n", " print(' accuracy: {:.3f}'.format(mean_accuracy))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "bG9AaMzih3eh" }, "source": [ "#### Keras 指标名称\n", "\n", "\n", "\n", "Keras 模型以一致方式处理指标名称。当您在指标列表中传递字符串时，该*确切*字符串会用作指标的 `name`。这些名称在 `model.fit` 返回的历史对象中可见，而在传递给 `keras.callbacks` 的日志中，它们被设置为您在指标列表中传递的字符串。 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1iODIsGDgyYd", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "model.compile(\n", " optimizer = tf.keras.optimizers.Adam(0.001),\n", " loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " metrics = ['acc', 'accuracy', tf.keras.metrics.SparseCategoricalAccuracy(name=\"my_accuracy\")])\n", "history = model.fit(train_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8oGzs_TlisKJ", "vscode": { "languageId": "python" } }, "outputs": [], "source": [ "history.history.keys()" ] }, { "cell_type": "markdown", "metadata": { "id": "JaB2z2XIyhcr" }, "source": [ "### 调试\n", "\n", "使用 Eager Execution 可以分步运行代码来检查形状、数据类型和值。某些 API（如 `tf.function`、`tf.keras` 等）设计为使用计算图执行来提高性能和可移植性。调试时，使用 `tf.config.run_functions_eagerly(True)` 可以在此代码内使用 Eager Execution。\n", "\n", "例如：\n", "\n", "```python\n", "@tf.function\n", "def f(x):\n", " if x > 0:\n", " import pdb\n", " pdb.set_trace()\n", " x = x + 1\n", " return x\n", "\n", "tf.config.run_functions_eagerly(True)\n", "f(tf.constant(1))\n", "```\n", "\n", "```\n", ">>> f()\n", "-> x = x + 1\n", "(Pdb) l\n", " 6 @tf.function\n", " 7 def f(x):\n", " 8 if x > 0:\n", " 9 import pdb\n", " 10 pdb.set_trace()\n", " 11 -> x = x + 1\n", " 12 return x\n", " 13\n", " 14 tf.config.run_functions_eagerly(True)\n", " 15 f(tf.constant(1))\n", "[EOF]\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "gdvGF2FvbBXZ" }, "source": [ "这也可以在 Keras 模型和其他支持 Eager Execution 的 API 中使用：\n", "\n", "```python\n", "class CustomModel(tf.keras.models.Model):\n", "\n", " @tf.function\n", " def call(self, input_data):\n", " if tf.reduce_mean(input_data) > 0:\n", " return input_data\n", " else:\n", " import pdb\n", " pdb.set_trace()\n", " return input_data // 2\n", "\n", "\n", "tf.config.run_functions_eagerly(True)\n", "model = CustomModel()\n", "model(tf.constant([-2, -4]))\n", "```\n", "\n", "```\n", ">>> call()\n", "-> return input_data // 2\n", "(Pdb) l\n", " 10 if tf.reduce_mean(input_data) > 0:\n", " 11 return input_data\n", " 12 else:\n", " 13 import pdb\n", " 14 pdb.set_trace()\n", " 15 -> return input_data // 2\n", " 16\n", " 17\n", " 18 tf.config.run_functions_eagerly(True)\n", " 19 model = CustomModel()\n", " 20 model(tf.constant([-2, -4]))\n", "```" ] }, { "cell_type": "markdown", "metadata": { "id": "S0-F-bvJXKD8" }, "source": [ "注释：\n", "\n", "- `tf.keras.Model` 方法（例如 `fit`、`evaluate` 和 `predict`）作为[计算图](https://tensorflow.google.cn/guide/intro_to_graphs)执行，并且 `tf.function` 位于底层。\n", "\n", "- 使用 `tf.keras.Model.compile`时，设置 `run_eagerly = True` 以禁止 `Model` 逻辑被封装在 `tf.function` 中。\n", "\n", "- 使用 `tf.data.experimental.enable_debug_mode` 为 `tf.data` 启用调试模式。阅读 [API 文档](https://tensorflow.google.cn/api_docs/python/tf/data/experimental/enable_debug_mode)，了解详细信息。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "wxa5yKK7bym0" }, "source": [ "### 不要在您的对象中保留 `tf.Tensors`\n", "\n", "这些张量对象可能会在 `tf.function` 或 Eager 上下文中创建，并且这些张量的行为有所不同。始终仅将 `tf.Tensor` 用于中间值。\n", "\n", "要跟踪状态，请使用 `tf.Variable`，因为它们始终可用于两种上下文。阅读 `tf.Variable` 指南以了解更多信息。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "FdXLLYa2uAyx" }, "source": [ "## 资源和延伸阅读\n", "\n", "- 阅读 TF2 [指南](https://tensorflow.org/guide)和[教程](https://tensorflow.org/tutorials)以详细了解如何使用 TF2。\n", "\n", "- 如果您以前使用过 TF1.x，强烈建议您将代码迁移到 TF2。阅读[迁移指南](https://tensorflow.org/guide/migrate)以了解更多信息。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "effective_tf2.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }