{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "ISubpr_SSsiM" }, "source": [ "##### Copyright 2020 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2022-12-14T21:20:28.385778Z", "iopub.status.busy": "2022-12-14T21:20:28.385348Z", "iopub.status.idle": "2022-12-14T21:20:28.389413Z", "shell.execute_reply": "2022-12-14T21:20:28.388790Z" }, "id": "3jTMb1dySr3V" }, "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": "6DWfyNThSziV" }, "source": [ "# 模块、层和模型简介\n", "\n", "\n", " \n", " \n", " \n", " \n", "
View on TensorFlow.org 在 Google Colab 中运行 在 Github 上查看源代码\n", " 下载笔记本
" ] }, { "cell_type": "markdown", "metadata": { "id": "v0DdlfacAdTZ" }, "source": [ "要进行 TensorFlow 机器学习，您可能需要定义、保存和恢复模型。\n", "\n", "抽象地说，模型是：\n", "\n", "- 一个在张量上进行某些计算的函数（**前向传递**）\n", "- 一些可以更新以响应训练的变量\n", "\n", "在本指南中，您将深入学习 Keras，了解如何定义 TensorFlow 模型。本文着眼于 TensorFlow 如何收集变量和模型，以及如何保存和恢复它们。\n", "\n", "注：如果您想立即开始使用 Keras，请参阅 [Keras 指南集合](./keras/)。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "VSa6ayJmfZxZ" }, "source": [ "## 设置" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:28.393010Z", "iopub.status.busy": "2022-12-14T21:20:28.392468Z", "iopub.status.idle": "2022-12-14T21:20:30.292109Z", "shell.execute_reply": "2022-12-14T21:20:30.291422Z" }, "id": "goZwOXp_xyQj" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-12-14 21:20:29.328765: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n", "2022-12-14 21:20:29.328861: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n", "2022-12-14 21:20:29.328870: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" ] } ], "source": [ "import tensorflow as tf\n", "from datetime import datetime\n", "\n", "%load_ext tensorboard" ] }, { "cell_type": "markdown", "metadata": { "id": "yt5HEbsYAbw1" }, "source": [ "## 在 TensorFlow 中定义模型和层\n", "\n", "大多数模型都由层组成。层是具有已知数学结构的函数，可以重复使用并具有可训练的变量。在 TensorFlow 中，层和模型的大多数高级实现（例如 Keras 或 [Sonnet](https://github.com/deepmind/sonnet)）都在以下同一个基础类上构建：`tf.Module`。\n", "\n", "下面是一个在标量张量上运行的非常简单的 `tf.Module` 示例：\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:30.296433Z", "iopub.status.busy": "2022-12-14T21:20:30.295781Z", "iopub.status.idle": "2022-12-14T21:20:33.647754Z", "shell.execute_reply": "2022-12-14T21:20:33.647074Z" }, "id": "alhYPVEtAiSy" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class SimpleModule(tf.Module):\n", " def __init__(self, name=None):\n", " super().__init__(name=name)\n", " self.a_variable = tf.Variable(5.0, name=\"train_me\")\n", " self.non_trainable_variable = tf.Variable(5.0, trainable=False, name=\"do_not_train_me\")\n", " def __call__(self, x):\n", " return self.a_variable * x + self.non_trainable_variable\n", "\n", "simple_module = SimpleModule(name=\"simple\")\n", "\n", "simple_module(tf.constant(5.0))" ] }, { "cell_type": "markdown", "metadata": { "id": "JwMc_zu5Ant8" }, "source": [ "模块和引申而来的层是“对象”的深度学习术语：它们具有内部状态以及使用该状态的方法。\n", "\n", "`__call__` 并无特殊之处，只是其行为与 [Python 可调用对象](https://stackoverflow.com/questions/111234/what-is-a-callable)类似；您可以使用任何函数来调用模型。\n", "\n", "您可以出于任何原因开启和关闭变量的可训练性，包括在微调过程中冻结层和变量。\n", "\n", "注：`tf.Module` 是 `tf.keras.layers.Layer` 和 `tf.keras.Model` 的基类，因此您在此处看到的一切内容也适用于 Keras。出于历史兼容性原因，Keras 层不会从模块收集变量，因此您的模型应仅使用模块或仅使用 Keras 层。不过，下面给出的用于检查变量的方法在这两种情况下相同。\n", "\n", "通过将 `tf.Module` 子类化，将自动收集分配给该对象属性的任何 `tf.Variable` 或 `tf.Module` 实例。这样，您可以保存和加载变量，还可以创建 `tf.Module` 的集合。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:33.651965Z", "iopub.status.busy": "2022-12-14T21:20:33.651347Z", "iopub.status.idle": "2022-12-14T21:20:33.659172Z", "shell.execute_reply": "2022-12-14T21:20:33.658426Z" }, "id": "CyzYy4A_CbVf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "trainable variables: (,)\n", "all variables: (, )\n" ] } ], "source": [ "# All trainable variables\n", "print(\"trainable variables:\", simple_module.trainable_variables)\n", "# Every variable\n", "print(\"all variables:\", simple_module.variables)" ] }, { "cell_type": "markdown", "metadata": { "id": "nuSFrRUNCaaW" }, "source": [ "下面是一个由模块组成的两层线性层模型的示例。\n", "\n", "首先是一个密集（线性）层：" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:33.662635Z", "iopub.status.busy": "2022-12-14T21:20:33.662099Z", "iopub.status.idle": "2022-12-14T21:20:33.666444Z", "shell.execute_reply": "2022-12-14T21:20:33.665891Z" }, "id": "Efb2p2bzAn-V" }, "outputs": [], "source": [ "class Dense(tf.Module):\n", " def __init__(self, in_features, out_features, name=None):\n", " super().__init__(name=name)\n", " self.w = tf.Variable(\n", " tf.random.normal([in_features, out_features]), name='w')\n", " self.b = tf.Variable(tf.zeros([out_features]), name='b')\n", " def __call__(self, x):\n", " y = tf.matmul(x, self.w) + self.b\n", " return tf.nn.relu(y)" ] }, { "cell_type": "markdown", "metadata": { "id": "bAhMuC-UpnhX" }, "source": [ "随后是完整的模型，此模型将创建并应用两个层实例：" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:33.669799Z", "iopub.status.busy": "2022-12-14T21:20:33.669260Z", "iopub.status.idle": "2022-12-14T21:20:34.007652Z", "shell.execute_reply": "2022-12-14T21:20:34.006958Z" }, "id": "QQ7qQf-DFw74" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model results: tf.Tensor([[0. 0.]], shape=(1, 2), dtype=float32)\n" ] } ], "source": [ "class SequentialModule(tf.Module):\n", " def __init__(self, name=None):\n", " super().__init__(name=name)\n", "\n", " self.dense_1 = Dense(in_features=3, out_features=3)\n", " self.dense_2 = Dense(in_features=3, out_features=2)\n", "\n", " def __call__(self, x):\n", " x = self.dense_1(x)\n", " return self.dense_2(x)\n", "\n", "# You have made a model!\n", "my_model = SequentialModule(name=\"the_model\")\n", "\n", "# Call it, with random results\n", "print(\"Model results:\", my_model(tf.constant([[2.0, 2.0, 2.0]])))" ] }, { "cell_type": "markdown", "metadata": { "id": "d1oUzasJHHXf" }, "source": [ "`tf.Module` 实例将以递归方式自动收集分配给它的任何 `tf.Variable` 或 `tf.Module` 实例。这样，您可以使用单个模型实例管理 `tf.Module` 的集合，并保存和加载整个模型。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.011157Z", "iopub.status.busy": "2022-12-14T21:20:34.010636Z", "iopub.status.idle": "2022-12-14T21:20:34.014700Z", "shell.execute_reply": "2022-12-14T21:20:34.014124Z" }, "id": "JLFA5_PEGb6C" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Submodules: (<__main__.Dense object at 0x7f109408f2e0>, <__main__.Dense object at 0x7f109408f4f0>)\n" ] } ], "source": [ "print(\"Submodules:\", my_model.submodules)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.018024Z", "iopub.status.busy": "2022-12-14T21:20:34.017455Z", "iopub.status.idle": "2022-12-14T21:20:34.023884Z", "shell.execute_reply": "2022-12-14T21:20:34.023201Z" }, "id": "6lzoB8pcRN12" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \n", "\n", " \n", "\n", " \n", "\n", " \n", "\n" ] } ], "source": [ "for var in my_model.variables:\n", " print(var, \"\\n\")" ] }, { "cell_type": "markdown", "metadata": { "id": "hoaxL3zzm0vK" }, "source": [ "### 等待创建变量\n", "\n", "您在这里可能已经注意到，必须定义层的输入和输出大小。这样，`w` 变量才会具有已知的形状并且可被分配。\n", "\n", "通过将变量创建推迟到第一次使用特定输入形状调用模块时，您将无需预先指定输入大小。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.027127Z", "iopub.status.busy": "2022-12-14T21:20:34.026629Z", "iopub.status.idle": "2022-12-14T21:20:34.031613Z", "shell.execute_reply": "2022-12-14T21:20:34.030985Z" }, "id": "XsGCLFXlnPum" }, "outputs": [], "source": [ "class FlexibleDenseModule(tf.Module):\n", " # Note: No need for `in_features`\n", " def __init__(self, out_features, name=None):\n", " super().__init__(name=name)\n", " self.is_built = False\n", " self.out_features = out_features\n", "\n", " def __call__(self, x):\n", " # Create variables on first call.\n", " if not self.is_built:\n", " self.w = tf.Variable(\n", " tf.random.normal([x.shape[-1], self.out_features]), name='w')\n", " self.b = tf.Variable(tf.zeros([self.out_features]), name='b')\n", " self.is_built = True\n", "\n", " y = tf.matmul(x, self.w) + self.b\n", " return tf.nn.relu(y)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.034779Z", "iopub.status.busy": "2022-12-14T21:20:34.034224Z", "iopub.status.idle": "2022-12-14T21:20:34.043718Z", "shell.execute_reply": "2022-12-14T21:20:34.043097Z" }, "id": "8bjOWax9LOkP" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model results: tf.Tensor([[0. 0.]], shape=(1, 2), dtype=float32)\n" ] } ], "source": [ "# Used in a module\n", "class MySequentialModule(tf.Module):\n", " def __init__(self, name=None):\n", " super().__init__(name=name)\n", "\n", " self.dense_1 = FlexibleDenseModule(out_features=3)\n", " self.dense_2 = FlexibleDenseModule(out_features=2)\n", "\n", " def __call__(self, x):\n", " x = self.dense_1(x)\n", " return self.dense_2(x)\n", "\n", "my_model = MySequentialModule(name=\"the_model\")\n", "print(\"Model results:\", my_model(tf.constant([[2.0, 2.0, 2.0]])))" ] }, { "cell_type": "markdown", "metadata": { "id": "49JfbhVrpOLH" }, "source": [ "这种灵活性是 TensorFlow 层通常仅需要指定其输出的形状（例如在 `tf.keras.layers.Dense` 中），而无需指定输入和输出大小的原因。" ] }, { "cell_type": "markdown", "metadata": { "id": "JOLVVBT8J_dl" }, "source": [ "## 保存权重\n", "\n", "您可以将 `tf.Module` 保存为[检查点](./checkpoint.ipynb)和 [SavedModel](./saved_model.ipynb)。\n", "\n", "检查点即是权重（即模块及其子模块内部的变量集的值）。" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.047091Z", "iopub.status.busy": "2022-12-14T21:20:34.046603Z", "iopub.status.idle": "2022-12-14T21:20:34.072159Z", "shell.execute_reply": "2022-12-14T21:20:34.071532Z" }, "id": "pHXKRDk7OLHA" }, "outputs": [ { "data": { "text/plain": [ "'my_checkpoint'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chkp_path = \"my_checkpoint\"\n", "checkpoint = tf.train.Checkpoint(model=my_model)\n", "checkpoint.write(chkp_path)" ] }, { "cell_type": "markdown", "metadata": { "id": "WXOPMBR4T4ZR" }, "source": [ "检查点由两种文件组成---数据本身以及元数据的索引文件。索引文件跟踪实际保存的内容和检查点的编号，而检查点数据包含变量值及其特性查找路径。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.075422Z", "iopub.status.busy": "2022-12-14T21:20:34.074814Z", "iopub.status.idle": "2022-12-14T21:20:34.243408Z", "shell.execute_reply": "2022-12-14T21:20:34.242577Z" }, "id": "jBV3fprlTWqJ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "my_checkpoint.data-00000-of-00001 my_checkpoint.index\r\n" ] } ], "source": [ "!ls my_checkpoint*" ] }, { "cell_type": "markdown", "metadata": { "id": "CowCuBTvXgUu" }, "source": [ "您可以查看检查点内部，以确保整个变量集合已由包含这些变量的 Python 对象保存并排序。" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.247378Z", "iopub.status.busy": "2022-12-14T21:20:34.246800Z", "iopub.status.idle": "2022-12-14T21:20:34.252637Z", "shell.execute_reply": "2022-12-14T21:20:34.252079Z" }, "id": "o2QAdfpvS8tB" }, "outputs": [ { "data": { "text/plain": [ "[('_CHECKPOINTABLE_OBJECT_GRAPH', []),\n", " ('model/dense_1/b/.ATTRIBUTES/VARIABLE_VALUE', [3]),\n", " ('model/dense_1/w/.ATTRIBUTES/VARIABLE_VALUE', [3, 3]),\n", " ('model/dense_2/b/.ATTRIBUTES/VARIABLE_VALUE', [2]),\n", " ('model/dense_2/w/.ATTRIBUTES/VARIABLE_VALUE', [3, 2])]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.train.list_variables(chkp_path)" ] }, { "cell_type": "markdown", "metadata": { "id": "4eGaNiQWcK4j" }, "source": [ "在分布式（多机）训练期间，可以将它们分片，这就是要对它们进行编号（例如 '00000-of-00001'）的原因。不过，在本例中，只有一个分片。\n", "\n", "重新加载模型时，将重写 Python 对象中的值。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.255926Z", "iopub.status.busy": "2022-12-14T21:20:34.255471Z", "iopub.status.idle": "2022-12-14T21:20:34.276608Z", "shell.execute_reply": "2022-12-14T21:20:34.276029Z" }, "id": "UV8rdDzcwVVg" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_model = MySequentialModule()\n", "new_checkpoint = tf.train.Checkpoint(model=new_model)\n", "new_checkpoint.restore(\"my_checkpoint\")\n", "\n", "# Should be the same result as above\n", "new_model(tf.constant([[2.0, 2.0, 2.0]]))" ] }, { "cell_type": "markdown", "metadata": { "id": "BnPwDRwamdfq" }, "source": [ "注：由于检查点处于长时间训练工作流的核心位置，因此 `tf.checkpoint.CheckpointManager` 是一个可使检查点管理变得更简单的辅助类。有关更多详细信息，请参阅[指南](./checkpoint.ipynb)。" ] }, { "cell_type": "markdown", "metadata": { "id": "pSZebVuWxDXu" }, "source": [ "## 保存函数\n", "\n", "TensorFlow 可以在不使用原始 Python 对象的情况下运行模型，如 [TensorFlow Serving](https://tensorflow.org/tfx) 和 [TensorFlow Lite](https://tensorflow.org/lite) 所示，甚至当您从 [TensorFlow Hub](https://tensorflow.org/hub) 下载经过训练的模型时也是如此。\n", "\n", "TensorFlow 需要了解如何执行 Python 中描述的计算，但**不需要原始代码**。为此，您可以创建一个**计算图**，如[计算图和函数简介指南](./intro_to_graphs.ipynb)中所述。\n", "\n", "此计算图中包含实现函数的*运算*。\n", "\n", "您可以通过添加 `@tf.function` 装饰器在上面的模型中定义计算图，以指示此代码应作为计算图运行。" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.280157Z", "iopub.status.busy": "2022-12-14T21:20:34.279474Z", "iopub.status.idle": "2022-12-14T21:20:34.287129Z", "shell.execute_reply": "2022-12-14T21:20:34.286554Z" }, "id": "WQTvkapUh7lk" }, "outputs": [], "source": [ "class MySequentialModule(tf.Module):\n", " def __init__(self, name=None):\n", " super().__init__(name=name)\n", "\n", " self.dense_1 = Dense(in_features=3, out_features=3)\n", " self.dense_2 = Dense(in_features=3, out_features=2)\n", "\n", " @tf.function\n", " def __call__(self, x):\n", " x = self.dense_1(x)\n", " return self.dense_2(x)\n", "\n", "# You have made a model with a graph!\n", "my_model = MySequentialModule(name=\"the_model\")" ] }, { "cell_type": "markdown", "metadata": { "id": "hW66YXBziLo9" }, "source": [ "您构建的模块的工作原理与之前完全相同。传递给函数的每个唯一签名都会创建一个单独的计算图。请参阅[计算图和函数简介指南](./intro_to_graphs.ipynb)以了解详情。" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.290476Z", "iopub.status.busy": "2022-12-14T21:20:34.289990Z", "iopub.status.idle": "2022-12-14T21:20:34.395480Z", "shell.execute_reply": "2022-12-14T21:20:34.394788Z" }, "id": "H5zUfti3iR52" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor([[0. 0.]], shape=(1, 2), dtype=float32)\n", "tf.Tensor(\n", "[[[0. 0.]\n", " [0. 0.]]], shape=(1, 2, 2), dtype=float32)\n" ] } ], "source": [ "print(my_model([[2.0, 2.0, 2.0]]))\n", "print(my_model([[[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]]))" ] }, { "cell_type": "markdown", "metadata": { "id": "lbGlU1kgyDo7" }, "source": [ "您可以通过在 TensorBoard 摘要中跟踪计算图来将其可视化。" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:34.399392Z", "iopub.status.busy": "2022-12-14T21:20:34.398754Z", "iopub.status.idle": "2022-12-14T21:20:35.188258Z", "shell.execute_reply": "2022-12-14T21:20:35.187524Z" }, "id": "zmy-T67zhp-S" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-12-14 21:20:34.407218: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcupti.so.11.2'; dlerror: libcupti.so.11.2: cannot open shared object file: No such file or directory\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor([[2.365068 0. ]], shape=(1, 2), dtype=float32)\n" ] } ], "source": [ "# Set up logging.\n", "stamp = datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n", "logdir = \"logs/func/%s\" % stamp\n", "writer = tf.summary.create_file_writer(logdir)\n", "\n", "# Create a new model to get a fresh trace\n", "# Otherwise the summary will not see the graph.\n", "new_model = MySequentialModule()\n", "\n", "# Bracket the function call with\n", "# tf.summary.trace_on() and tf.summary.trace_export().\n", "tf.summary.trace_on(graph=True)\n", "tf.profiler.experimental.start(logdir)\n", "# Call only one tf.function when tracing.\n", "z = print(new_model(tf.constant([[2.0, 2.0, 2.0]])))\n", "with writer.as_default():\n", " tf.summary.trace_export(\n", " name=\"my_func_trace\",\n", " step=0,\n", " profiler_outdir=logdir)" ] }, { "cell_type": "markdown", "metadata": { "id": "gz4lwNZ9hR79" }, "source": [ "启动 Tensorboard 以查看生成的跟踪：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "V4MXDbgBnkJu" }, "outputs": [], "source": [ "#docs_infra: no_execute\n", "%tensorboard --logdir logs/func" ] }, { "cell_type": "markdown", "metadata": { "id": "Gjattu0AhYUl" }, "source": [ "![A screenshot of the graph, in tensorboard](https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/guide/images/tensorboard_graph.png?raw=true)" ] }, { "cell_type": "markdown", "metadata": { "id": "SQu3TVZecmL7" }, "source": [ "### 创建 `SavedModel`\n", "\n", "共享经过完全训练的模型的推荐方式是使用 `SavedModel`。`SavedModel` 包含函数集合与权重集合。\n", "\n", "您可以按以下方式保存刚刚训练的模型：" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.192331Z", "iopub.status.busy": "2022-12-14T21:20:35.191693Z", "iopub.status.idle": "2022-12-14T21:20:35.278665Z", "shell.execute_reply": "2022-12-14T21:20:35.278009Z" }, "id": "Awv_Tw__WK7a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: the_saved_model/assets\n" ] } ], "source": [ "tf.saved_model.save(my_model, \"the_saved_model\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.282022Z", "iopub.status.busy": "2022-12-14T21:20:35.281711Z", "iopub.status.idle": "2022-12-14T21:20:35.465050Z", "shell.execute_reply": "2022-12-14T21:20:35.464215Z" }, "id": "SXv3mEKsefGj" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 28\r\n", "drwxr-sr-x 2 kbuilder kokoro 4096 Dec 14 21:20 assets\r\n", "-rw-rw-r-- 1 kbuilder kokoro 55 Dec 14 21:20 fingerprint.pb\r\n", "-rw-rw-r-- 1 kbuilder kokoro 14672 Dec 14 21:20 saved_model.pb\r\n", "drwxr-sr-x 2 kbuilder kokoro 4096 Dec 14 21:20 variables\r\n" ] } ], "source": [ "# Inspect the SavedModel in the directory\n", "!ls -l the_saved_model" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.468786Z", "iopub.status.busy": "2022-12-14T21:20:35.468240Z", "iopub.status.idle": "2022-12-14T21:20:35.648302Z", "shell.execute_reply": "2022-12-14T21:20:35.647475Z" }, "id": "vQQ3hEvHYdoR" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 8\r\n", "-rw-rw-r-- 1 kbuilder kokoro 490 Dec 14 21:20 variables.data-00000-of-00001\r\n", "-rw-rw-r-- 1 kbuilder kokoro 356 Dec 14 21:20 variables.index\r\n" ] } ], "source": [ "# The variables/ directory contains a checkpoint of the variables \n", "!ls -l the_saved_model/variables" ] }, { "cell_type": "markdown", "metadata": { "id": "xBqPop7ZesBU" }, "source": [ "`saved_model.pb` 文件是一个描述函数式 `tf.Graph` 的[协议缓冲区](https://developers.google.com/protocol-buffers)。\n", "\n", "可以从此表示加载模型和层，而无需实际构建创建该表示的类的实例。在您没有（或不需要）Python 解释器（例如大规模应用或在边缘设备上），或者在原始 Python 代码不可用或不实用的情况下，这样做十分理想。\n", "\n", "您可以将模型作为新对象加载：" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.652468Z", "iopub.status.busy": "2022-12-14T21:20:35.651813Z", "iopub.status.idle": "2022-12-14T21:20:35.689806Z", "shell.execute_reply": "2022-12-14T21:20:35.689153Z" }, "id": "zRFcA5wIefv4" }, "outputs": [], "source": [ "new_model = tf.saved_model.load(\"the_saved_model\")" ] }, { "cell_type": "markdown", "metadata": { "id": "-9EF3mT7i3qN" }, "source": [ "通过加载已保存模型创建的 `new_model` 是 TensorFlow 内部的用户对象，无需任何类知识。它不是 `SequentialModule` 类型的对象。" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.693168Z", "iopub.status.busy": "2022-12-14T21:20:35.692699Z", "iopub.status.idle": "2022-12-14T21:20:35.696843Z", "shell.execute_reply": "2022-12-14T21:20:35.696263Z" }, "id": "EC_eQj7yi54G" }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "isinstance(new_model, SequentialModule)" ] }, { "cell_type": "markdown", "metadata": { "id": "-OrOX1zxiyhR" }, "source": [ "此新模型​​适用于已定义的输入签名。您不能向以这种方式恢复的模型添加更多签名。" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.700021Z", "iopub.status.busy": "2022-12-14T21:20:35.699412Z", "iopub.status.idle": "2022-12-14T21:20:35.706740Z", "shell.execute_reply": "2022-12-14T21:20:35.706031Z" }, "id": "_23BYYBWfKnc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor([[0. 0.]], shape=(1, 2), dtype=float32)\n", "tf.Tensor(\n", "[[[0. 0.]\n", " [0. 0.]]], shape=(1, 2, 2), dtype=float32)\n" ] } ], "source": [ "print(my_model([[2.0, 2.0, 2.0]]))\n", "print(my_model([[[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]]))" ] }, { "cell_type": "markdown", "metadata": { "id": "qSFhoMtTjSR6" }, "source": [ "因此，利用 `SavedModel`，您可以使用 `tf.Module` 保存 TensorFlow 权重和计算图，随后再次加载它们。" ] }, { "cell_type": "markdown", "metadata": { "id": "Rb9IdN7hlUZK" }, "source": [ "## Keras 模型和层\n", "\n", "请注意，到目前为止，还没有提到 Keras。您可以在 `tf.Module` 上构建自己的高级 API，而我们已经拥有这些 API。\n", "\n", "在本部分中，您将研究 Keras 如何使用 `tf.Module`。可在 [Keras 指南](https://tensorflow.google.cn/guide/keras/sequential_model)中找到有关 Keras 模型的完整用户指南。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "uigsVGPreE-D" }, "source": [ "### Keras 层\n", "\n", "`tf.keras.layers.Layer` 是所有 Keras 层的基类，它继承自 `tf.Module`。\n", "\n", "您只需换出父项，然后将 `__call__` 更改为 `call` 即可将模块转换为 Keras 层：" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.710318Z", "iopub.status.busy": "2022-12-14T21:20:35.709696Z", "iopub.status.idle": "2022-12-14T21:20:35.719155Z", "shell.execute_reply": "2022-12-14T21:20:35.718576Z" }, "id": "88YOGquhnQRd" }, "outputs": [], "source": [ "class MyDense(tf.keras.layers.Layer):\n", " # Adding **kwargs to support base Keras layer arguments\n", " def __init__(self, in_features, out_features, **kwargs):\n", " super().__init__(**kwargs)\n", "\n", " # This will soon move to the build step; see below\n", " self.w = tf.Variable(\n", " tf.random.normal([in_features, out_features]), name='w')\n", " self.b = tf.Variable(tf.zeros([out_features]), name='b')\n", " def call(self, x):\n", " y = tf.matmul(x, self.w) + self.b\n", " return tf.nn.relu(y)\n", "\n", "simple_layer = MyDense(name=\"simple\", in_features=3, out_features=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "nYGmAsPrws--" }, "source": [ "Keras 层有自己的 `__call__`，它会进行下一部分中所述的某些簿记，然后调用 `call()`。您应当不会看到功能上的任何变化。" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.722577Z", "iopub.status.busy": "2022-12-14T21:20:35.722026Z", "iopub.status.idle": "2022-12-14T21:20:35.730566Z", "shell.execute_reply": "2022-12-14T21:20:35.729909Z" }, "id": "nIqE8wOznYKG" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "simple_layer([[2.0, 2.0, 2.0]])" ] }, { "cell_type": "markdown", "metadata": { "id": "tmN5vb1K18U1" }, "source": [ "### `build` 步骤\n", "\n", "如上所述，在您确定输入形状之前，等待创建变量在许多情况下十分方便。\n", "\n", "Keras 层具有额外的生命周期步骤，可让您在定义层时获得更高的灵活性。这是在 `build()` 函数中定义的。\n", "\n", "`build` 仅被调用一次，而且是使用输入的形状调用的。它通常用于创建变量（权重）。\n", "\n", "您可以根据输入的大小灵活地重写上面的 `MyDense` 层:\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.733991Z", "iopub.status.busy": "2022-12-14T21:20:35.733384Z", "iopub.status.idle": "2022-12-14T21:20:35.738785Z", "shell.execute_reply": "2022-12-14T21:20:35.738204Z" }, "id": "4YTfrlgdsURp" }, "outputs": [], "source": [ "class FlexibleDense(tf.keras.layers.Layer):\n", " # Note the added `**kwargs`, as Keras supports many arguments\n", " def __init__(self, out_features, **kwargs):\n", " super().__init__(**kwargs)\n", " self.out_features = out_features\n", "\n", " def build(self, input_shape): # Create the state of the layer (weights)\n", " self.w = tf.Variable(\n", " tf.random.normal([input_shape[-1], self.out_features]), name='w')\n", " self.b = tf.Variable(tf.zeros([self.out_features]), name='b')\n", "\n", " def call(self, inputs): # Defines the computation from inputs to outputs\n", " return tf.matmul(inputs, self.w) + self.b\n", "\n", "# Create the instance of the layer\n", "flexible_dense = FlexibleDense(out_features=3)" ] }, { "cell_type": "markdown", "metadata": { "id": "Koc_uSqt2PRh" }, "source": [ "此时，模型尚未构建，因此没有变量:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.742182Z", "iopub.status.busy": "2022-12-14T21:20:35.741723Z", "iopub.status.idle": "2022-12-14T21:20:35.745569Z", "shell.execute_reply": "2022-12-14T21:20:35.745002Z" }, "id": "DgyTyUD32Ln4" }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flexible_dense.variables" ] }, { "cell_type": "markdown", "metadata": { "id": "-KdamIVl2W8Y" }, "source": [ "调用该函数会分配大小适当的变量。" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.748639Z", "iopub.status.busy": "2022-12-14T21:20:35.748114Z", "iopub.status.idle": "2022-12-14T21:20:35.754849Z", "shell.execute_reply": "2022-12-14T21:20:35.754287Z" }, "id": "IkLyEx7uAoTK" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model results: tf.Tensor(\n", "[[ -4.0764046 -6.8253055 8.713049 ]\n", " [ -6.114606 -10.237958 13.069572 ]], shape=(2, 3), dtype=float32)\n" ] } ], "source": [ "# Call it, with predictably random results\n", "print(\"Model results:\", flexible_dense(tf.constant([[2.0, 2.0, 2.0], [3.0, 3.0, 3.0]])))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.757939Z", "iopub.status.busy": "2022-12-14T21:20:35.757377Z", "iopub.status.idle": "2022-12-14T21:20:35.762668Z", "shell.execute_reply": "2022-12-14T21:20:35.762122Z" }, "id": "Swofpkrd2YDd" }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flexible_dense.variables" ] }, { "cell_type": "markdown", "metadata": { "id": "7PuNUnf0OIpF" }, "source": [ "由于仅调用一次 `build`，因此如果输入形状与层的变量不兼容，输入将被拒绝。" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.766012Z", "iopub.status.busy": "2022-12-14T21:20:35.765455Z", "iopub.status.idle": "2022-12-14T21:20:35.770173Z", "shell.execute_reply": "2022-12-14T21:20:35.769567Z" }, "id": "caYWDrHSAy_j" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Failed: Exception encountered when calling layer 'flexible_dense' (type FlexibleDense).\n", "\n", "{{function_node __wrapped__MatMul_device_/job:localhost/replica:0/task:0/device:GPU:0}} Matrix size-incompatible: In[0]: [1,4], In[1]: [3,3] [Op:MatMul]\n", "\n", "Call arguments received by layer 'flexible_dense' (type FlexibleDense):\n", " • inputs=tf.Tensor(shape=(1, 4), dtype=float32)\n" ] } ], "source": [ "try:\n", " print(\"Model results:\", flexible_dense(tf.constant([[2.0, 2.0, 2.0, 2.0]])))\n", "except tf.errors.InvalidArgumentError as e:\n", " print(\"Failed:\", e)" ] }, { "cell_type": "markdown", "metadata": { "id": "YnporXiudF1I" }, "source": [ "Keras 层具有许多额外的功能，包括：\n", "\n", "- 可选损失\n", "- 对指标的支持\n", "- 对可选 `training` 参数的内置支持，用于区分训练和推断用途\n", "- `get_config` 和 `from_config` 方法，允许您准确存储配置以在 Python 中克隆模型\n", "\n", "在自定义层和模型的[完整指南](./keras/custom_layers_and_models.ipynb)中阅读关于它们的信息。" ] }, { "cell_type": "markdown", "metadata": { "id": "L2kds2IHw2KD" }, "source": [ "### Keras 模型\n", "\n", "您可以将模型定义为嵌套的 Keras 层。\n", "\n", "但是，Keras 还提供了称为 `tf.keras.Model` 的全功能模型类。它继承自 `tf.keras.layers.Layer`，因此 Keras 模型支持以同样的方式使用、嵌套和保存。Keras 模型还具有额外的功能，这使它们可以轻松训练、评估、加载、保存，甚至在多台机器上进行训练。\n", "\n", "您可以使用几乎相同的代码定义上面的 `SequentialModule`，再次将 `__call__` 转换为 `call()` 并更改父项。" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.773522Z", "iopub.status.busy": "2022-12-14T21:20:35.772913Z", "iopub.status.idle": "2022-12-14T21:20:35.789861Z", "shell.execute_reply": "2022-12-14T21:20:35.789208Z" }, "id": "Hqjo1DiyrHrn" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model results: tf.Tensor([[ -7.7209134 -11.065 ]], shape=(1, 2), dtype=float32)\n" ] } ], "source": [ "class MySequentialModel(tf.keras.Model):\n", " def __init__(self, name=None, **kwargs):\n", " super().__init__(**kwargs)\n", "\n", " self.dense_1 = FlexibleDense(out_features=3)\n", " self.dense_2 = FlexibleDense(out_features=2)\n", " def call(self, x):\n", " x = self.dense_1(x)\n", " return self.dense_2(x)\n", "\n", "# You have made a Keras model!\n", "my_sequential_model = MySequentialModel(name=\"the_model\")\n", "\n", "# Call it on a tensor, with random results\n", "print(\"Model results:\", my_sequential_model(tf.constant([[2.0, 2.0, 2.0]])))\n" ] }, { "cell_type": "markdown", "metadata": { "id": "8i-CR_h2xw3z" }, "source": [ "所有相同的功能都可用，包括跟踪变量和子模块。\n", "\n", "注：为了强调上面的注意事项，嵌套在 Keras 层或模型中的原始 `tf.Module` 将不会收集其变量以用于训练或保存。相反，它会在 Keras 层内嵌套 Keras 层。" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.792898Z", "iopub.status.busy": "2022-12-14T21:20:35.792440Z", "iopub.status.idle": "2022-12-14T21:20:35.798583Z", "shell.execute_reply": "2022-12-14T21:20:35.798004Z" }, "id": "hdLQFNdMsOz1" }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_sequential_model.variables" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.801878Z", "iopub.status.busy": "2022-12-14T21:20:35.801358Z", "iopub.status.idle": "2022-12-14T21:20:35.929601Z", "shell.execute_reply": "2022-12-14T21:20:35.928968Z" }, "id": "JjVAMrAJsQ7G" }, "outputs": [ { "data": { "text/plain": [ "(<__main__.FlexibleDense at 0x7f10356a2ac0>,\n", " <__main__.FlexibleDense at 0x7f0f840a6df0>)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_sequential_model.submodules" ] }, { "cell_type": "markdown", "metadata": { "id": "FhP8EItC4oac" }, "source": [ "重写 `tf.keras.Model` 是一种构建 TensorFlow 模型的极 Python 化方式。如果要从其他框架迁移模型，这可能非常简单。\n", "\n", "如果要构造的模型是现有层和输入的简单组合，则可以使用[函数式 API](./keras/functional.ipynb) 节省时间和空间，此 API 附带有关模型重构和架构的附加功能。\n", "\n", "下面是使用函数式 API 构造的相同模型：" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.933206Z", "iopub.status.busy": "2022-12-14T21:20:35.932652Z", "iopub.status.idle": "2022-12-14T21:20:35.984394Z", "shell.execute_reply": "2022-12-14T21:20:35.983815Z" }, "id": "jJiZZiJ0fyqQ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Layer (type) Output Shape Param # \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " input_1 (InputLayer) [(None, 3)] 0 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " flexible_dense_3 (FlexibleD (None, 3) 12 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " ense) \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " flexible_dense_4 (FlexibleD (None, 2) 8 \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " ense) \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " \n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "=================================================================\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Total params: 20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Trainable params: 20\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Non-trainable params: 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n" ] } ], "source": [ "inputs = tf.keras.Input(shape=[3,])\n", "\n", "x = FlexibleDense(3)(inputs)\n", "x = FlexibleDense(2)(x)\n", "\n", "my_functional_model = tf.keras.Model(inputs=inputs, outputs=x)\n", "\n", "my_functional_model.summary()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:35.991239Z", "iopub.status.busy": "2022-12-14T21:20:35.990651Z", "iopub.status.idle": "2022-12-14T21:20:35.997431Z", "shell.execute_reply": "2022-12-14T21:20:35.996864Z" }, "id": "kg-xAZw5gaG6" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "my_functional_model(tf.constant([[2.0, 2.0, 2.0]]))" ] }, { "cell_type": "markdown", "metadata": { "id": "s_BK9XH5q9cq" }, "source": [ "这里的主要区别在于，输入形状是作为函数构造过程的一部分预先指定的。在这种情况下，不必完全指定 `input_shape` 参数；您可以将某些维度保留为 `None`。\n", "\n", "注：您无需在子类化模型中指定 `input_shape` 或 `InputLayer`；这些参数和层将被忽略。" ] }, { "cell_type": "markdown", "metadata": { "id": "qI9aXLnaHEFF" }, "source": [ "## 保存 Keras 模型\n", "\n", "可以为 Keras 模型创建检查点，这看起来和 `tf.Module` 一样。\n", "\n", "Keras 模型也可以使用 `tf.saved_models.save()` 保存，因为它们是模块。但是，Keras 模型具有更方便的方法和其他功能：" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:36.000781Z", "iopub.status.busy": "2022-12-14T21:20:36.000258Z", "iopub.status.idle": "2022-12-14T21:20:36.309485Z", "shell.execute_reply": "2022-12-14T21:20:36.308860Z" }, "id": "SAz-KVZlzAJu" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: exname_of_file/assets\n" ] } ], "source": [ "my_sequential_model.save(\"exname_of_file\")" ] }, { "cell_type": "markdown", "metadata": { "id": "C2urAeR-omns" }, "source": [ "同样地，它们也可以轻松重新加载：" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:36.313402Z", "iopub.status.busy": "2022-12-14T21:20:36.312797Z", "iopub.status.idle": "2022-12-14T21:20:36.392778Z", "shell.execute_reply": "2022-12-14T21:20:36.392193Z" }, "id": "Wj5DW-LCopry" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n" ] } ], "source": [ "reconstructed_model = tf.keras.models.load_model(\"exname_of_file\")" ] }, { "cell_type": "markdown", "metadata": { "id": "EA7P_MNvpviZ" }, "source": [ "Keras `SavedModels` 还可以保存指标、损失和优化器状态。\n", "\n", "可以使用此重构模型，并且在相同数据上调用时会产生相同的结果：" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T21:20:36.396328Z", "iopub.status.busy": "2022-12-14T21:20:36.395858Z", "iopub.status.idle": "2022-12-14T21:20:36.418629Z", "shell.execute_reply": "2022-12-14T21:20:36.418072Z" }, "id": "P_wGfQo5pe6T" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reconstructed_model(tf.constant([[2.0, 2.0, 2.0]]))" ] }, { "cell_type": "markdown", "metadata": { "id": "xKyjlkceqjwD" }, "source": [ "有关保存和序列化 Keras 模型，包括为自定义层提供配置方法来为功能提供支持的更多信息，请参阅[保存和序列化指南](https://tensorflow.google.cn/guide/keras/save_and_serialize)。" ] }, { "cell_type": "markdown", "metadata": { "id": "kcdMMPYv7Krz" }, "source": [ "# 后续步骤\n", "\n", "如果您想了解有关 Keras 的更多详细信息，可以在[此处](./keras/)查看现有的 Keras 指南。\n", "\n", "在 `tf.module` 上构建的高级 API 的另一个示例是 DeepMind 的 Sonnet，[其网站](https://github.com/deepmind/sonnet)上有详细介绍。" ] } ], "metadata": { "colab": { "collapsed_sections": [ "ISubpr_SSsiM" ], "name": "intro_to_modules.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 0 }