{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2019 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2023-11-07T23:34:17.985526Z", "iopub.status.busy": "2023-11-07T23:34:17.984940Z", "iopub.status.idle": "2023-11-07T23:34:17.988851Z", "shell.execute_reply": "2023-11-07T23:34:17.988259Z" }, "id": "tuOe1ymfHZPu" }, "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": "MfBg1C5NB3X0" }, "source": [ "# Keras 的分布式训练" ] }, { "cell_type": "markdown", "metadata": { "id": "r6P32iYYV27b" }, "source": [ "\n", " \n", " \n", " \n", " \n", "
在 TensorFlow.org 上查看 在 Google Colab 上运行 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", "`tf.distribute.Strategy` API 提供了一个抽象，用于跨多个处理单元进行分布式训练。它允许您使用现有模型和训练代码，只需要很少的修改，就可以执行分布式训练。\n", "\n", "本教程演示了如何使用 `tf.distribute.MirroredStrategy` 在*单台机器的多个 GPU 上通过同步训练*进行计算图内复制。该策略本质上是将所有模型变量复制到每个处理器。 然后，通过使用[全归约](http://mpitutorial.com/tutorials/mpi-reduce-and-allreduce/)来组合所有处理器的梯度，并将组合后的值应用于模型的所有副本。\n", "\n", "您将使用 `tf.keras` API 构建模型并使用 `Model.fit` 对其进行训练。（要了解使用自定义训练循环和 `MirroredStrategy` 的分布式训练，请查看[此教程](custom_training.ipynb)。）\n", "\n", "`MirroredStrategy` 在单台机器上的多个 GPU 上训练您的模型。要*在多个工作进程的多个 GPU 上进行同步训练*，请通过 [Keras Model.fit](multi_worker_with_keras.ipynb) 或[自定义训练循环](multi_worker_with_ctl.ipynb)使用 `tf.distribute.MultiWorkerMirroredStrategy`。有关其他选项，请参阅[分布式训练指南](../../guide/distributed_training.ipynb)。\n", "\n", "要了解其他各种策略，请参阅[使用 TensorFlow 进行分布式训练](../../guide/distributed_training.ipynb)指南。" ] }, { "cell_type": "markdown", "metadata": { "id": "Dney9v7BsJij" }, "source": [ "## 安装" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:17.992455Z", "iopub.status.busy": "2023-11-07T23:34:17.992208Z", "iopub.status.idle": "2023-11-07T23:34:20.796231Z", "shell.execute_reply": "2023-11-07T23:34:20.795436Z" }, "id": "r8S3ublR7Ay8" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-11-07 23:34:18.893069: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "2023-11-07 23:34:18.893120: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "2023-11-07 23:34:18.894855: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" ] } ], "source": [ "import tensorflow_datasets as tfds\n", "import tensorflow as tf\n", "\n", "import os\n", "\n", "# Load the TensorBoard notebook extension.\n", "%load_ext tensorboard" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:20.800584Z", "iopub.status.busy": "2023-11-07T23:34:20.800144Z", "iopub.status.idle": "2023-11-07T23:34:20.804184Z", "shell.execute_reply": "2023-11-07T23:34:20.803526Z" }, "id": "SkocY8tgRd3H" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.15.0-rc1\n" ] } ], "source": [ "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": { "id": "hXhefksNKk2I" }, "source": [ "## 下载数据集" ] }, { "cell_type": "markdown", "metadata": { "id": "OtnnUwvmB3X5" }, "source": [ "从 [TensorFlow Datasets](https://tensorflow.google.cn/datasets) 加载 MNIST 数据集。这将返回 `tf.data` 格式的数据集。\n", "\n", "将 `with_info` 参数设置为 `True` 会包含整个数据集的元数据，这些元数据将被保存到 `info` 中。此外，该元数据对象还包括训练样本和测试样本的数量。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:20.807936Z", "iopub.status.busy": "2023-11-07T23:34:20.807405Z", "iopub.status.idle": "2023-11-07T23:34:23.869087Z", "shell.execute_reply": "2023-11-07T23:34:23.868343Z" }, "id": "iXMJ3G9NB3X6" }, "outputs": [], "source": [ "datasets, info = tfds.load(name='mnist', with_info=True, as_supervised=True)\n", "\n", "mnist_train, mnist_test = datasets['train'], datasets['test']" ] }, { "cell_type": "markdown", "metadata": { "id": "GrjVhv-eKuHD" }, "source": [ "## 定义分布式策略" ] }, { "cell_type": "markdown", "metadata": { "id": "TlH8vx6BB3X9" }, "source": [ "创建 `MirroredStrategy` 对象。这将处理分布，并提供一个上下文管理器 (`MirroredStrategy.scope`) 在内部构建模型。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:23.873402Z", "iopub.status.busy": "2023-11-07T23:34:23.873133Z", "iopub.status.idle": "2023-11-07T23:34:24.862483Z", "shell.execute_reply": "2023-11-07T23:34:24.861521Z" }, "id": "4j0tdf4YB3X9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0', '/job:localhost/replica:0/task:0/device:GPU:1', '/job:localhost/replica:0/task:0/device:GPU:2', '/job:localhost/replica:0/task:0/device:GPU:3')\n" ] } ], "source": [ "strategy = tf.distribute.MirroredStrategy()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:24.866881Z", "iopub.status.busy": "2023-11-07T23:34:24.866607Z", "iopub.status.idle": "2023-11-07T23:34:24.870859Z", "shell.execute_reply": "2023-11-07T23:34:24.870166Z" }, "id": "cY3KA_h2iVfN" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of devices: 4\n" ] } ], "source": [ "print('Number of devices: {}'.format(strategy.num_replicas_in_sync))" ] }, { "cell_type": "markdown", "metadata": { "id": "lNbPv0yAleW8" }, "source": [ "## 设置输入流水线" ] }, { "cell_type": "markdown", "metadata": { "id": "psozqcuptXhK" }, "source": [ "当使用多个 GPU 训练模型时，可以通过增加批次大小来有效利用额外的计算能力。通常，应使用适合 GPU 内存的最大批次大小，并相应地调整学习率。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:24.874501Z", "iopub.status.busy": "2023-11-07T23:34:24.874247Z", "iopub.status.idle": "2023-11-07T23:34:24.878206Z", "shell.execute_reply": "2023-11-07T23:34:24.877475Z" }, "id": "p1xWxKcnhar9" }, "outputs": [], "source": [ "# You can also do info.splits.total_num_examples to get the total\n", "# number of examples in the dataset.\n", "\n", "num_train_examples = info.splits['train'].num_examples\n", "num_test_examples = info.splits['test'].num_examples\n", "\n", "BUFFER_SIZE = 10000\n", "\n", "BATCH_SIZE_PER_REPLICA = 64\n", "BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync" ] }, { "cell_type": "markdown", "metadata": { "id": "0Wm5rsL2KoDF" }, "source": [ "定义一个函数，将图像像素值从 `[0, 255]` 范围归一化到 `[0, 1]` 范围（[特征缩放](https://en.wikipedia.org/wiki/Feature_scaling)）：" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:24.881651Z", "iopub.status.busy": "2023-11-07T23:34:24.881387Z", "iopub.status.idle": "2023-11-07T23:34:24.884907Z", "shell.execute_reply": "2023-11-07T23:34:24.884280Z" }, "id": "Eo9a46ZeJCkm" }, "outputs": [], "source": [ "def scale(image, label):\n", " image = tf.cast(image, tf.float32)\n", " image /= 255\n", "\n", " return image, label" ] }, { "cell_type": "markdown", "metadata": { "id": "WZCa5RLc5A91" }, "source": [ "将此 `scale` 函数应用于训练数据和测试数据，使用 `tf.data.Dataset` API 对训练数据进行乱序 (`Dataset.shuffle`)，然后进行分批 (`Dataset.batch`)。请注意，您还保留了训练数据的内存缓存以提高性能 (`Dataset.cache`).。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:24.888341Z", "iopub.status.busy": "2023-11-07T23:34:24.888094Z", "iopub.status.idle": "2023-11-07T23:34:24.942133Z", "shell.execute_reply": "2023-11-07T23:34:24.941416Z" }, "id": "gRZu2maChwdT" }, "outputs": [], "source": [ "train_dataset = mnist_train.map(scale).cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n", "eval_dataset = mnist_test.map(scale).batch(BATCH_SIZE)" ] }, { "cell_type": "markdown", "metadata": { "id": "4xsComp8Kz5H" }, "source": [ "## 生成模型" ] }, { "cell_type": "markdown", "metadata": { "id": "1BnQYQTpB3YA" }, "source": [ "在 `Strategy.scope` 的上下文中，使用 Keras API 创建和编译模型：" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:24.946208Z", "iopub.status.busy": "2023-11-07T23:34:24.945629Z", "iopub.status.idle": "2023-11-07T23:34:25.135649Z", "shell.execute_reply": "2023-11-07T23:34:25.134841Z" }, "id": "IexhL_vIB3YA" }, "outputs": [], "source": [ "with strategy.scope():\n", " model = tf.keras.Sequential([\n", " tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),\n", " tf.keras.layers.MaxPooling2D(),\n", " tf.keras.layers.Flatten(),\n", " tf.keras.layers.Dense(64, activation='relu'),\n", " tf.keras.layers.Dense(10)\n", " ])\n", "\n", " model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " optimizer=tf.keras.optimizers.Adam(),\n", " metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": { "id": "8i6OU5W9Vy2u" }, "source": [ "## 定义回调（callback）\n" ] }, { "cell_type": "markdown", "metadata": { "id": "YOXO5nvvK3US" }, "source": [ "定义以下 [Keras 回调](https://tensorflow.google.cn/guide/keras/train_and_evaluate)：\n", "\n", "- `tf.keras.callbacks.TensorBoard`：为 TensorBoard 编写日志，以便呈现计算图。\n", "- `tf.keras.callbacks.ModelCheckpoint`：以特定频率保存模型，例如在每个周期之后。\n", "- `tf.keras.callbacks.BackupAndRestore`：通过备份模型和当前周期号来提供容错功能。在[使用 Keras 进行多工作进程训练](multi_worker_with_keras.ipynb)教程的*容错*部分了解详情。\n", "- `tf.keras.callbacks.LearningRateScheduler`: schedules the learning rate to change after, for example, every epoch/batch.\n", "\n", "出于说明目的，添加名为 PrintLR 的回调以在笔记本中显示学习率。\n", "\n", "**注：** 使用 `BackupAndRestore` 回调而不是 `ModelCheckpoint` 作为从作业失败重新启动时还原训练状态的主要机制。由于 `BackupAndRestore` 仅支持 Eager 模式，在计算图模式下考虑使用 `ModelCheckpoint`。" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:25.140619Z", "iopub.status.busy": "2023-11-07T23:34:25.139962Z", "iopub.status.idle": "2023-11-07T23:34:25.143647Z", "shell.execute_reply": "2023-11-07T23:34:25.143048Z" }, "id": "A9bwLCcXzSgy" }, "outputs": [], "source": [ "# Define the checkpoint directory to store the checkpoints.\n", "checkpoint_dir = './training_checkpoints'\n", "# Define the name of the checkpoint files.\n", "checkpoint_prefix = os.path.join(checkpoint_dir, \"ckpt_{epoch}\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:25.147329Z", "iopub.status.busy": "2023-11-07T23:34:25.146728Z", "iopub.status.idle": "2023-11-07T23:34:25.150530Z", "shell.execute_reply": "2023-11-07T23:34:25.149851Z" }, "id": "wpU-BEdzJDbK" }, "outputs": [], "source": [ "# Define a function for decaying the learning rate.\n", "# You can define any decay function you need.\n", "def decay(epoch):\n", " if epoch < 3:\n", " return 1e-3\n", " elif epoch >= 3 and epoch < 7:\n", " return 1e-4\n", " else:\n", " return 1e-5" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:25.154262Z", "iopub.status.busy": "2023-11-07T23:34:25.153620Z", "iopub.status.idle": "2023-11-07T23:34:25.157636Z", "shell.execute_reply": "2023-11-07T23:34:25.156912Z" }, "id": "jKhiMgXtKq2w" }, "outputs": [], "source": [ "# Define a callback for printing the learning rate at the end of each epoch.\n", "class PrintLR(tf.keras.callbacks.Callback):\n", " def on_epoch_end(self, epoch, logs=None):\n", " print('\\nLearning rate for epoch {} is {}'.format( epoch + 1, model.optimizer.lr.numpy()))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:25.161102Z", "iopub.status.busy": "2023-11-07T23:34:25.160505Z", "iopub.status.idle": "2023-11-07T23:34:25.165152Z", "shell.execute_reply": "2023-11-07T23:34:25.164551Z" }, "id": "YVqAbR6YyNQh" }, "outputs": [], "source": [ "# Put all the callbacks together.\n", "callbacks = [\n", " tf.keras.callbacks.TensorBoard(log_dir='./logs'),\n", " tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_prefix,\n", " save_weights_only=True),\n", " tf.keras.callbacks.LearningRateScheduler(decay),\n", " PrintLR()\n", "]" ] }, { "cell_type": "markdown", "metadata": { "id": "70HXgDQmK46q" }, "source": [ "## 训练和评估" ] }, { "cell_type": "markdown", "metadata": { "id": "6EophnOAB3YD" }, "source": [ "现在，以普通方式训练模型，在模型上调用 Keras `Model.fit` 并传入在教程开始时创建的数据集。无论您是否分布训练，此步骤相同。" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:25.168864Z", "iopub.status.busy": "2023-11-07T23:34:25.168392Z", "iopub.status.idle": "2023-11-07T23:34:57.646466Z", "shell.execute_reply": "2023-11-07T23:34:57.645724Z" }, "id": "7MVw_6CqB3YD" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-11-07 23:34:25.196020: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:553] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Collective all_reduce tensors: 6 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Collective all_reduce tensors: 6 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Collective all_reduce tensors: 6 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Collective all_reduce tensors: 6 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "I0000 00:00:1699400072.026138 550348 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 25:55 - loss: 2.3030 - accuracy: 0.1133" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0075s vs `on_train_batch_end` time: 0.0133s). Check your callbacks.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:Callback method `on_train_batch_end` is slow compared to the batch time (batch time: 0.0075s vs `on_train_batch_end` time: 0.0133s). Check your callbacks.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 7/235 [..............................] - ETA: 2s - loss: 1.9262 - accuracy: 0.4821 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 14/235 [>.............................] - ETA: 1s - loss: 1.5330 - accuracy: 0.6116" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 21/235 [=>............................] - ETA: 1s - loss: 1.2522 - accuracy: 0.6786" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 28/235 [==>...........................] - 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9s 9ms/step - loss: 0.3254 - accuracy: 0.9083 - lr: 0.0010\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 8s - loss: 0.2046 - accuracy: 0.9492" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/235 [>.............................] - ETA: 1s - loss: 0.1175 - accuracy: 0.9712" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 16/235 [=>............................] - ETA: 1s - loss: 0.1231 - accuracy: 0.9668" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 24/235 [==>...........................] - 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2s 7ms/step - loss: 0.1000 - accuracy: 0.9708 - lr: 0.0010\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 9s - loss: 0.1038 - accuracy: 0.9844" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/235 [>.............................] - ETA: 1s - loss: 0.0750 - accuracy: 0.9795" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 15/235 [>.............................] - ETA: 1s - loss: 0.0824 - accuracy: 0.9789" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 22/235 [=>............................] - 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2s 7ms/step - loss: 0.0470 - accuracy: 0.9876 - lr: 1.0000e-04\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 9s - loss: 0.0453 - accuracy: 0.9883" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/235 [>.............................] - ETA: 1s - loss: 0.0635 - accuracy: 0.9834" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 15/235 [>.............................] - ETA: 1s - loss: 0.0583 - accuracy: 0.9823" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 23/235 [=>............................] - 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2s 7ms/step - loss: 0.0431 - accuracy: 0.9884 - lr: 1.0000e-04\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 7/12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 9s - loss: 0.0409 - accuracy: 0.9805" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/235 [>.............................] - ETA: 1s - loss: 0.0507 - accuracy: 0.9849" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 15/235 [>.............................] - ETA: 1s - loss: 0.0436 - accuracy: 0.9872" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 22/235 [=>............................] - 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2s 7ms/step - loss: 0.0396 - accuracy: 0.9897 - lr: 1.0000e-05\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 9/12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 9s - loss: 0.0420 - accuracy: 0.9883" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/235 [>.............................] - ETA: 1s - loss: 0.0454 - accuracy: 0.9854" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 15/235 [>.............................] - ETA: 1s - loss: 0.0484 - accuracy: 0.9870" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 22/235 [=>............................] - 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ETA: 0s - loss: 0.0394 - accuracy: 0.9896" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Learning rate for epoch 9 is 9.999999747378752e-06\n", "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "235/235 [==============================] - 2s 7ms/step - loss: 0.0393 - accuracy: 0.9897 - lr: 1.0000e-05\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 10/12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 9s - loss: 0.0343 - accuracy: 0.9844" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/235 [>.............................] - ETA: 1s - loss: 0.0444 - accuracy: 0.9883" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 15/235 [>.............................] - 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2s 7ms/step - loss: 0.0391 - accuracy: 0.9898 - lr: 1.0000e-05\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 11/12\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 8s - loss: 0.0479 - accuracy: 0.9883" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 8/235 [>.............................] - ETA: 1s - loss: 0.0440 - accuracy: 0.9873" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 15/235 [>.............................] - ETA: 1s - loss: 0.0463 - accuracy: 0.9872" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 22/235 [=>............................] - 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2s 7ms/step - loss: 0.0388 - accuracy: 0.9900 - lr: 1.0000e-05\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "EPOCHS = 12\n", "\n", "model.fit(train_dataset, epochs=EPOCHS, callbacks=callbacks)" ] }, { "cell_type": "markdown", "metadata": { "id": "NUcWAUUupIvG" }, "source": [ "查看保存的检查点：" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:57.650291Z", "iopub.status.busy": "2023-11-07T23:34:57.649811Z", "iopub.status.idle": "2023-11-07T23:34:58.271610Z", "shell.execute_reply": "2023-11-07T23:34:58.270641Z" }, "id": "JQ4zeSTxKEhB" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "checkpoint\t\t ckpt_4.data-00000-of-00001\r\n", "ckpt_1.data-00000-of-00001 ckpt_4.index\r\n", "ckpt_1.index\t\t ckpt_5.data-00000-of-00001\r\n", "ckpt_10.data-00000-of-00001 ckpt_5.index\r\n", "ckpt_10.index\t\t ckpt_6.data-00000-of-00001\r\n", "ckpt_11.data-00000-of-00001 ckpt_6.index\r\n", "ckpt_11.index\t\t ckpt_7.data-00000-of-00001\r\n", "ckpt_12.data-00000-of-00001 ckpt_7.index\r\n", "ckpt_12.index\t\t ckpt_8.data-00000-of-00001\r\n", "ckpt_2.data-00000-of-00001 ckpt_8.index\r\n", "ckpt_2.index\t\t ckpt_9.data-00000-of-00001\r\n", "ckpt_3.data-00000-of-00001 ckpt_9.index\r\n", "ckpt_3.index\r\n" ] } ], "source": [ "# Check the checkpoint directory.\n", "!ls {checkpoint_dir}" ] }, { "cell_type": "markdown", "metadata": { "id": "qor53h7FpMke" }, "source": [ "要查看模型的执行情况，请加载最新的检查点，并在测试数据上调用 `Model.evaluate`：" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:34:58.275967Z", "iopub.status.busy": "2023-11-07T23:34:58.275661Z", "iopub.status.idle": "2023-11-07T23:35:00.196097Z", "shell.execute_reply": "2023-11-07T23:35:00.195229Z" }, "id": "JtEwxiTgpQoP" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-11-07 23:34:58.383301: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:553] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/40 [..............................] - ETA: 57s - loss: 0.0627 - accuracy: 0.9805" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9/40 [=====>........................] - ETA: 0s - loss: 0.0467 - accuracy: 0.9848 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "17/40 [===========>..................] - ETA: 0s - loss: 0.0523 - accuracy: 0.9823" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "25/40 [=================>............] - ETA: 0s - loss: 0.0507 - accuracy: 0.9827" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "33/40 [=======================>......] - ETA: 0s - loss: 0.0506 - accuracy: 0.9827" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "40/40 [==============================] - ETA: 0s - loss: 0.0501 - accuracy: 0.9831" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "40/40 [==============================] - 2s 8ms/step - loss: 0.0501 - accuracy: 0.9831\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Eval loss: 0.05011461675167084, Eval accuracy: 0.9830999970436096\n" ] } ], "source": [ "model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))\n", "\n", "eval_loss, eval_acc = model.evaluate(eval_dataset)\n", "\n", "print('Eval loss: {}, Eval accuracy: {}'.format(eval_loss, eval_acc))" ] }, { "cell_type": "markdown", "metadata": { "id": "IIeF2RWfYu4N" }, "source": [ "要可视化输出，请启动 TensorBoard 并查看日志：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vtyAZO0DoKu_" }, "outputs": [], "source": [ "%tensorboard --logdir=logs" ] }, { "cell_type": "markdown", "metadata": { "id": "a0a82d26d6bd" }, "source": [ "" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:35:00.200230Z", "iopub.status.busy": "2023-11-07T23:35:00.199479Z", "iopub.status.idle": "2023-11-07T23:35:00.829823Z", "shell.execute_reply": "2023-11-07T23:35:00.828793Z" }, "id": "LnyscOkvKKBR" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 4.0K\r\n", "4.0K train\r\n" ] } ], "source": [ "!ls -sh ./logs" ] }, { "cell_type": "markdown", "metadata": { "id": "kBLlogrDvMgg" }, "source": [ "## 保存模型" ] }, { "cell_type": "markdown", "metadata": { "id": "Xa87y_A0vRma" }, "source": [ "使用 `Model.save` 将模型保存到一个 `.keras` 压缩归档中。保存后，您可以使用或不使用 `Strategy.scope` 加载模型。" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:35:00.834815Z", "iopub.status.busy": "2023-11-07T23:35:00.834488Z", "iopub.status.idle": "2023-11-07T23:35:00.838601Z", "shell.execute_reply": "2023-11-07T23:35:00.837898Z" }, "id": "h8Q4MKOLwG7K" }, "outputs": [], "source": [ "path = 'my_model.keras'" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:35:00.841903Z", "iopub.status.busy": "2023-11-07T23:35:00.841423Z", "iopub.status.idle": "2023-11-07T23:35:00.920613Z", "shell.execute_reply": "2023-11-07T23:35:00.919836Z" }, "id": "4HvcDmVsvQoa" }, "outputs": [], "source": [ "model.save(path)" ] }, { "cell_type": "markdown", "metadata": { "id": "vKJT4w5JwVPI" }, "source": [ "现在，在没有 `Strategy.scope` 的情况下加载模型：" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:35:00.924997Z", "iopub.status.busy": "2023-11-07T23:35:00.924377Z", "iopub.status.idle": "2023-11-07T23:35:01.570441Z", "shell.execute_reply": "2023-11-07T23:35:01.569678Z" }, "id": "T_gT0RbRvQ3o" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/40 [..............................] - ETA: 12s - loss: 0.0627 - accuracy: 0.9805" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "17/40 [===========>..................] - ETA: 0s - loss: 0.0523 - accuracy: 0.9823 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "32/40 [=======================>......] - ETA: 0s - loss: 0.0508 - accuracy: 0.9825" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "40/40 [==============================] - 0s 3ms/step - loss: 0.0501 - accuracy: 0.9831\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Eval loss: 0.05011461302638054, Eval Accuracy: 0.9830999970436096\n" ] } ], "source": [ "unreplicated_model = tf.keras.models.load_model(path)\n", "\n", "unreplicated_model.compile(\n", " loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " optimizer=tf.keras.optimizers.Adam(),\n", " metrics=['accuracy'])\n", "\n", "eval_loss, eval_acc = unreplicated_model.evaluate(eval_dataset)\n", "\n", "print('Eval loss: {}, Eval Accuracy: {}'.format(eval_loss, eval_acc))" ] }, { "cell_type": "markdown", "metadata": { "id": "YBLzcRF0wbDe" }, "source": [ "使用 `Strategy.scope` 加载模型：" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "execution": { "iopub.execute_input": "2023-11-07T23:35:01.574093Z", "iopub.status.busy": "2023-11-07T23:35:01.573805Z", "iopub.status.idle": "2023-11-07T23:35:04.000622Z", "shell.execute_reply": "2023-11-07T23:35:03.999802Z" }, "id": "BBVo3WGGwd9a" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2023-11-07 23:35:01.980132: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:553] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/40 [..............................] - ETA: 1:10 - loss: 0.0627 - accuracy: 0.9805" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "11/40 [=======>......................] - ETA: 0s - loss: 0.0508 - accuracy: 0.9830 " ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "22/40 [===============>..............] - ETA: 0s - loss: 0.0500 - accuracy: 0.9833" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "33/40 [=======================>......] - ETA: 0s - loss: 0.0506 - accuracy: 0.9827" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "40/40 [==============================] - 2s 5ms/step - loss: 0.0501 - accuracy: 0.9831\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Eval loss: 0.05011461675167084, Eval Accuracy: 0.9830999970436096\n" ] } ], "source": [ "with strategy.scope():\n", " replicated_model = tf.keras.models.load_model(path)\n", " replicated_model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " optimizer=tf.keras.optimizers.Adam(),\n", " metrics=['accuracy'])\n", "\n", " eval_loss, eval_acc = replicated_model.evaluate(eval_dataset)\n", " print ('Eval loss: {}, Eval Accuracy: {}'.format(eval_loss, eval_acc))" ] }, { "cell_type": "markdown", "metadata": { "id": "MUZwaz4AKjtD" }, "source": [ "### 其他资源\n", "\n", "更多通过 Keras `Model.fit` API 使用不同分布策略的示例：\n", "\n", "1. [在 TPU 上使用 BERT 解决 GLUE 任务](https://tensorflow.google.cn/text/tutorials/bert_glue)教程使用 `tf.distribute.MirroredStrategy` 在 GPU 上进行训练，并使用 `tf.distribute.TPUStrategy` 在 TPU 上进行训练。\n", "2. [使用分布式策略保存和加载模型](save_and_load.ipynb)教程演示了如何将 SavedModel API 与 `tf.distribute.Strategy` 一起使用。\n", "3. [官方 TensorFlow 模型](https://github.com/tensorflow/models/tree/master/official)可以配置为运行多个分布式策略。\n", "\n", "要了解有关 TensorFlow 分布式策略的更多信息，请参阅以下资料：\n", "\n", "1. [使用 tf.distribute.Strategy 进行自定义训练](custom_training.ipynb)教程展示了如何使用 `tf.distribute.MirroredStrategy` 通过自定义训练循环进行单工作进程训练。\n", "2. [使用 Keras 进行多工作进程训练](multi_worker_with_keras.ipynb)教程展示了如何将 `MultiWorkerMirroredStrategy` 与 `Model.fit` 一起使用。\n", "3. [使用 Keras 和 MultiWorkerMirroredStrategy 的自定义训练循环](multi_worker_with_ctl.ipynb)教程展示了如何将 `MultiWorkerMirroredStrategy` 与 Keras 和自定义训练循环一起使用。\n", "4. [TensorFlow 中的分布式训练](https://tensorflow.google.cn/guide/distributed_training)指南概述了可用的分布式策略。\n", "5. [使用 tf.function 获得更佳性能](../../guide/function.ipynb)指南提供了有关其他策略和工具的信息，例如可用于优化 TensorFlow 模型性能的 [TensorFlow Profiler](../../guide/profiler.md)。\n", "\n", "注：`tf.distribute.Strategy` 正在积极开发中，TensorFlow 将在不久的将来添加更多示例和教程。请进行尝试。我们欢迎您通过 [GitHub 上的议题](https://github.com/tensorflow/tensorflow/issues/new)提交反馈。" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "keras.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": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 0 }