{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "wJcYs_ERTnnI" }, "source": [ "##### Copyright 2021 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2022-12-14T20:28:45.309234Z", "iopub.status.busy": "2022-12-14T20:28:45.308637Z", "iopub.status.idle": "2022-12-14T20:28:45.312538Z", "shell.execute_reply": "2022-12-14T20:28:45.312033Z" }, "id": "HMUDt0CiUJk9" }, "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": "77z2OchJTk0l" }, "source": [ "# 迁移 TensorBoard：TensorFlow 的呈现工具包\n", "\n", "\n", " \n", " \n", " \n", " \n", "
在 TensorFlow.org 上查看 在 Google Colab 运行\n", " 在 Github 上查看源代码\n", " 下载笔记本
\n" ] }, { "cell_type": "markdown", "metadata": { "id": "hIo_p2FWFIRx" }, "source": [ "[TensorBoard](https://tensorflow.google.cn/tensorboard) 是一个内置工具，用于在 TensorFlow 中提供测量和呈现。可以在 TensorBoard 中跟踪和显示准确率和损失等常见的机器学习实验指标。TensorBoard 与 TensorFlow 1 和 2 代码兼容。\n", "\n", "在 TensorFlow 1 中，`tf.estimator.Estimator` 默认为 TensorBoard 保存摘要。相比之下，在 TensorFlow 2 中，可以使用 `tf.keras.callbacks.TensorBoard` 回调保存摘要。\n", "\n", "本指南首先演示了如何在 TensorFlow 1 中将 TensorBoard 与 Estimator 一起使用，然后演示了如何在 TensorFlow 2 中执行等效的过程。" ] }, { "cell_type": "markdown", "metadata": { "id": "f55c103999de" }, "source": [ "### 安装" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T20:28:45.316158Z", "iopub.status.busy": "2022-12-14T20:28:45.315671Z", "iopub.status.idle": "2022-12-14T20:28:47.246834Z", "shell.execute_reply": "2022-12-14T20:28:47.246160Z" }, "id": "X74yjOb-e18w" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-12-14 20:28:46.264152: 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 20:28:46.264255: 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 20:28:46.264266: 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.compat.v1 as tf1\n", "import tensorflow as tf\n", "import tempfile\n", "import numpy as np\n", "import datetime\n", "%load_ext tensorboard" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T20:28:47.251060Z", "iopub.status.busy": "2022-12-14T20:28:47.250360Z", "iopub.status.idle": "2022-12-14T20:28:47.696848Z", "shell.execute_reply": "2022-12-14T20:28:47.696101Z" }, "id": "2r8r4d8FfMny" }, "outputs": [], "source": [ "mnist = tf.keras.datasets.mnist # The MNIST dataset.\n", "\n", "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n", "x_train, x_test = x_train / 255.0, x_test / 255.0" ] }, { "cell_type": "markdown", "metadata": { "id": "wrqBkG4RFLP_" }, "source": [ "### TensorFlow 1：TensorBoard 与 tf.estimator 一起使用\n", "\n", "在此 TensorFlow 1 示例中，您将实例化 `tf.estimator.DNNClassifier`，在 MNIST 数据集上对其进行训练和评估，并使用 TensorBoard 显示指标：" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T20:28:47.701211Z", "iopub.status.busy": "2022-12-14T20:28:47.700639Z", "iopub.status.idle": "2022-12-14T20:28:59.004629Z", "shell.execute_reply": "2022-12-14T20:28:59.003944Z" }, "id": "upA8nuf3FEq5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmp6t925gz_', '_tf_random_seed': None, '_save_summary_steps': 1, '_save_checkpoints_steps': 1, '_save_checkpoints_secs': None, '_session_config': allow_soft_placement: true\n", "graph_options {\n", " rewrite_options {\n", " meta_optimizer_iterations: ONE\n", " }\n", "}\n", ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/tmp/ipykernel_31731/2752664473.py:20: The name tf.estimator.inputs is deprecated. Please use tf.compat.v1.estimator.inputs instead.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/tmp/ipykernel_31731/2752664473.py:20: The name tf.estimator.inputs.numpy_input_fn is deprecated. Please use tf.compat.v1.estimator.inputs.numpy_input_fn instead.\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Not using Distribute Coordinator.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Running training and evaluation locally (non-distributed).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 1 or save_checkpoints_secs None.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/training_util.py:396: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_queue_runner.py:60: QueueRunner.__init__ (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "To construct input pipelines, use the `tf.data` module.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/inputs/queues/feeding_functions.py:491: add_queue_runner (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "To construct input pipelines, use the `tf.data` module.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Create CheckpointSaverHook.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Graph was finalized.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:910: start_queue_runners (from tensorflow.python.training.queue_runner_impl) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "To construct input pipelines, use the `tf.data` module.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-12-14 20:28:52.170464: W tensorflow/core/common_runtime/type_inference.cc:339] Type inference failed. This indicates an invalid graph that escaped type checking. Error message: INVALID_ARGUMENT: expected compatible input types, but input 1:\n", "type_id: TFT_OPTIONAL\n", "args {\n", " type_id: TFT_PRODUCT\n", " args {\n", " type_id: TFT_TENSOR\n", " args {\n", " type_id: TFT_INT64\n", " }\n", " }\n", "}\n", " is neither a subtype nor a supertype of the combined inputs preceding it:\n", "type_id: TFT_OPTIONAL\n", "args {\n", " type_id: TFT_PRODUCT\n", " args {\n", " type_id: TFT_TENSOR\n", " args {\n", " type_id: TFT_INT32\n", " }\n", " }\n", "}\n", "\n", "\twhile inferring type of node 'dnn/zero_fraction/cond/output/_18'\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmp6t925gz_/model.ckpt.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 1...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving checkpoints for 1 into /tmpfs/tmp/tmp6t925gz_/model.ckpt.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 1...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Starting evaluation at 2022-12-14T20:28:53\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Graph was finalized.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmp6t925gz_/model.ckpt-1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [1/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [2/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [3/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [4/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [5/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [6/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [7/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [8/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [9/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [10/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Inference Time : 0.29496s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Finished evaluation at 2022-12-14-20:28:53\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving dict for global step 1: accuracy = 0.159375, average_loss = 2.2843251, global_step = 1, loss = 292.39362\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 1: /tmpfs/tmp/tmp6t925gz_/model.ckpt-1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:loss = 118.0285, step = 0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 2...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving checkpoints for 2 into /tmpfs/tmp/tmp6t925gz_/model.ckpt.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 2...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Starting evaluation at 2022-12-14T20:28:53\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Graph was finalized.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmp6t925gz_/model.ckpt-2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [1/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [2/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [3/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [4/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [5/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [6/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [7/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [8/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [9/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [10/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Inference Time : 0.29514s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Finished evaluation at 2022-12-14-20:28:54\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving dict for global step 2: accuracy = 0.2359375, average_loss = 2.2260919, global_step = 2, loss = 284.93976\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 2: /tmpfs/tmp/tmp6t925gz_/model.ckpt-2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 3...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving checkpoints for 3 into /tmpfs/tmp/tmp6t925gz_/model.ckpt.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 3...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Starting evaluation at 2022-12-14T20:28:54\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Graph was finalized.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmp6t925gz_/model.ckpt-3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [1/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [2/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [3/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [4/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [5/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [6/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [7/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [8/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [9/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [10/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Inference Time : 0.28853s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Finished evaluation at 2022-12-14-20:28:54\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving dict for global step 3: accuracy = 0.31796876, average_loss = 2.1714652, global_step = 3, loss = 277.94754\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3: /tmpfs/tmp/tmp6t925gz_/model.ckpt-3\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 4...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving checkpoints for 4 into /tmpfs/tmp/tmp6t925gz_/model.ckpt.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 4...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Starting evaluation at 2022-12-14T20:28:55\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Graph was finalized.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmp6t925gz_/model.ckpt-4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [1/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [2/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [3/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [4/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [5/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [6/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [7/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [8/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [9/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [10/10]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Inference Time : 0.28964s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Finished evaluation at 2022-12-14-20:28:55\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving dict for global step 4: accuracy = 0.371875, average_loss = 2.122491, global_step = 4, loss = 271.67883\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 4: /tmpfs/tmp/tmp6t925gz_/model.ckpt-4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving checkpoints for 5 into /tmpfs/tmp/tmp6t925gz_/model.ckpt.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/saver.py:1064: remove_checkpoint (from tensorflow.python.checkpoint.checkpoint_management) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use standard file APIs to delete files with this prefix.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Starting evaluation at 2022-12-14T20:28:55\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Graph was finalized.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmp6t925gz_/model.ckpt-5\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Done running local_init_op.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Evaluation [1/10]\n" ] }, { "name": "stdout", "output_type": 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"INFO:tensorflow:Finished evaluation at 2022-12-14-20:28:58\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving dict for global step 10: accuracy = 0.5859375, average_loss = 1.7800614, global_step = 10, loss = 227.84785\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmpfs/tmp/tmp6t925gz_/model.ckpt-10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Loss for final step: 92.82808.\n" ] }, { "data": { "text/plain": [ "({'accuracy': 0.5859375,\n", " 'average_loss': 1.7800614,\n", " 'loss': 227.84785,\n", " 'global_step': 10},\n", " [])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%reload_ext tensorboard\n", "\n", "feature_columns = [tf1.feature_column.numeric_column(\"x\", shape=[28, 28])]\n", "\n", "config = tf1.estimator.RunConfig(save_summary_steps=1,\n", " save_checkpoints_steps=1)\n", "\n", "path = tempfile.mkdtemp()\n", "\n", "classifier = tf1.estimator.DNNClassifier(\n", " feature_columns=feature_columns,\n", " hidden_units=[256, 32],\n", " optimizer=tf1.train.AdamOptimizer(0.001),\n", " n_classes=10,\n", " dropout=0.1,\n", " model_dir=path,\n", " config = config\n", ")\n", "\n", "train_input_fn = tf1.estimator.inputs.numpy_input_fn(\n", " x={\"x\": x_train},\n", " y=y_train.astype(np.int32),\n", " num_epochs=10,\n", " batch_size=50,\n", " shuffle=True,\n", ")\n", "\n", "test_input_fn = tf1.estimator.inputs.numpy_input_fn(\n", " x={\"x\": x_test},\n", " y=y_test.astype(np.int32),\n", " num_epochs=10,\n", " shuffle=False\n", ")\n", "\n", "train_spec = tf1.estimator.TrainSpec(input_fn=train_input_fn, max_steps=10)\n", "eval_spec = tf1.estimator.EvalSpec(input_fn=test_input_fn,\n", " steps=10,\n", " throttle_secs=0)\n", "\n", "tf1.estimator.train_and_evaluate(estimator=classifier,\n", " train_spec=train_spec,\n", " eval_spec=eval_spec)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EBqO7JbR8bh2" }, "outputs": [], "source": [ "%tensorboard --logdir {classifier.model_dir}" ] }, { "cell_type": "markdown", "metadata": { "id": "GK8TK1CU88ns" }, "source": [ "" ] }, { "cell_type": "markdown", "metadata": { "id": "QvE_uxDJFUX-" }, "source": [ "### TensorFlow 2: TensorBoard 与 Keras 回调和 Model.fit 一起使用\n", "\n", "在此 TensorFlow 2 示例中，您将使用 `tf.keras.callbacks.TensorBoard` 回调创建和存储日志并训练模型。回调跟踪每个周期的准确率和损失。它会被传递给 `callbacks` 列表中的 `Model.fit`。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T20:28:59.008149Z", "iopub.status.busy": "2022-12-14T20:28:59.007892Z", "iopub.status.idle": "2022-12-14T20:29:25.768946Z", "shell.execute_reply": "2022-12-14T20:29:25.768196Z" }, "id": "9FLBhT2BFX2H" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 10/1875 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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", "1860/1875 [============================>.] - ETA: 0s - loss: 0.0979 - accuracy: 0.9701" ] }, { "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\r", "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0978 - accuracy: 0.9702 - val_loss: 0.0777 - val_accuracy: 0.9752\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 3/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 10/1875 [..............................] - ETA: 2s - loss: 0.0347 - accuracy: 0.9875" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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", "1860/1875 [============================>.] - ETA: 0s - loss: 0.0670 - 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\b\b\r", "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0670 - accuracy: 0.9789 - val_loss: 0.0740 - val_accuracy: 0.9776\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 4/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 10/1875 [..............................] - ETA: 2s - loss: 0.0870 - accuracy: 0.9688" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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", "1860/1875 [============================>.] - ETA: 0s - loss: 0.0530 - 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\b\b\b\b\r", "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0531 - accuracy: 0.9830 - val_loss: 0.0686 - val_accuracy: 0.9787\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 5/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 10/1875 [..............................] - ETA: 2s - loss: 0.0550 - accuracy: 0.9844" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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", "1860/1875 [============================>.] - ETA: 0s - loss: 0.0433 - accuracy: 0.9855" ] }, { "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\r", "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0434 - accuracy: 0.9855 - val_loss: 0.0655 - val_accuracy: 0.9810\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 6/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 10/1875 [..............................] - ETA: 2s - loss: 0.0087 - accuracy: 1.0000" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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", "1860/1875 [============================>.] - ETA: 0s - loss: 0.0360 - accuracy: 0.9884" ] }, { "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\r", "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0360 - accuracy: 0.9884 - val_loss: 0.0607 - val_accuracy: 0.9818\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 7/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 10/1875 [..............................] - ETA: 2s - loss: 0.0231 - accuracy: 0.9906" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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", "1860/1875 [============================>.] - ETA: 0s - loss: 0.0316 - accuracy: 0.9893" ] }, { "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\r", "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0316 - accuracy: 0.9893 - val_loss: 0.0712 - val_accuracy: 0.9802\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 8/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 10/1875 [..............................] - ETA: 2s - loss: 0.0319 - accuracy: 0.9844" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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", "1860/1875 [============================>.] - ETA: 0s - loss: 0.0263 - accuracy: 0.9913" ] }, { "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\r", "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0263 - accuracy: 0.9912 - val_loss: 0.0714 - val_accuracy: 0.9804\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 9/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 10/1875 [..............................] - ETA: 2s - loss: 0.0229 - accuracy: 0.9969" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\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", "1860/1875 [============================>.] - ETA: 0s - loss: 0.0231 - accuracy: 0.9921" ] }, { "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\r", "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0233 - accuracy: 0.9919 - val_loss: 0.0853 - val_accuracy: 0.9801\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 10/10\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 10/1875 [..............................] - ETA: 2s - loss: 0.0097 - accuracy: 0.9969" ] }, { "name": "stdout", "output_type": "stream", "text": [ 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tf.keras.layers.Dropout(0.2),\n", " tf.keras.layers.Dense(10, activation='softmax')\n", " ])\n", "\n", "model = create_model()\n", "model.compile(optimizer='adam',\n", " loss='sparse_categorical_crossentropy',\n", " metrics=['accuracy'],\n", " steps_per_execution=10)\n", "\n", "log_dir = tempfile.mkdtemp()\n", "tensorboard_callback = tf.keras.callbacks.TensorBoard(\n", " log_dir=log_dir,\n", " histogram_freq=1) # Enable histogram computation with each epoch.\n", "\n", "model.fit(x=x_train,\n", " y=y_train,\n", " epochs=10,\n", " validation_data=(x_test, y_test),\n", " callbacks=[tensorboard_callback])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ovPoLrCJ8t-R" }, "outputs": [], "source": [ "%tensorboard --logdir {tensorboard_callback.log_dir}" ] }, { "cell_type": "markdown", "metadata": { "id": "Ip-IMGt_8xx9" }, "source": [ "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "rQUS8nO9FZlH" }, "source": [ "# 后续步骤\n", "\n", "- 在[使用入门](https://tensorflow.google.cn/tensorboard/get_started)指南中详细了解 TensorBoard。\n", "- 对于较低级别的 API，请参阅 [tf.summary 迁移到 TensorFlow 2](https://tensorflow.google.cn/tensorboard/migrate) 指南。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "tensorboard.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 }