{ "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": "2022-12-14T22:13:31.657927Z", "iopub.status.busy": "2022-12-14T22:13:31.657323Z", "iopub.status.idle": "2022-12-14T22:13:31.660959Z", "shell.execute_reply": "2022-12-14T22:13:31.660426Z" }, "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": [ "# 指標とオプティマイザを移行する\n", "\n", "\n", " \n", " \n", " \n", " \n", "
TensorFlow.org で表示 Google Colab で実行 GitHub でソースを表示ノートブックをダウンロード
" ] }, { "cell_type": "markdown", "metadata": { "id": "meUTrR4I6m1C" }, "source": [ "TF1 では、`tf.metrics` はすべての指標関数の API 名前空間です。各指標は、`label` と `prediction` を入力パラメータとして取り、対応する指標テンソルを結果として返す関数です。TF2 では、`tf.keras.metrics` にすべての指標関数とオブジェクトが含まれています。`Metric` オブジェクトを `tf.keras.Model` および `tf.keras.layers.layer` で使用して、指標値を計算できます。" ] }, { "cell_type": "markdown", "metadata": { "id": "YdZSoIXEbhg-" }, "source": [ "## セットアップ\n", "\n", "いくつかの必要な TensorFlow インポートから始めましょう。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T22:13:31.664578Z", "iopub.status.busy": "2022-12-14T22:13:31.664047Z", "iopub.status.idle": "2022-12-14T22:13:33.592148Z", "shell.execute_reply": "2022-12-14T22:13:33.591473Z" }, "id": "iE0vSfMXumKI" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-12-14 22:13:32.624361: 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 22:13:32.624482: 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 22:13:32.624493: 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", "import tensorflow.compat.v1 as tf1" ] }, { "cell_type": "markdown", "metadata": { "id": "Jsm9Rxx7s1OZ" }, "source": [ "デモ用にいくつかの簡単なデータを準備します。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T22:13:33.596819Z", "iopub.status.busy": "2022-12-14T22:13:33.596006Z", "iopub.status.idle": "2022-12-14T22:13:33.600184Z", "shell.execute_reply": "2022-12-14T22:13:33.599585Z" }, "id": "m7rnGxsXtDkV" }, "outputs": [], "source": [ "features = [[1., 1.5], [2., 2.5], [3., 3.5]]\n", "labels = [0, 0, 1]\n", "eval_features = [[4., 4.5], [5., 5.5], [6., 6.5]]\n", "eval_labels = [0, 1, 1]" ] }, { "cell_type": "markdown", "metadata": { "id": "xswk0d4xrFaQ" }, "source": [ "## TF1: Estimator を使用した tf.compat.v1.metrics\n", "\n", "TF1 では、指標は `eval_metric_ops` として `EstimatorSpec` に追加でき、演算は　`tf.metrics`　で定義されたすべての指標関数を介して生成されます。例に従って、`tf.metrics.accuracy` の使用方法を確認できます。" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T22:13:33.603352Z", "iopub.status.busy": "2022-12-14T22:13:33.602901Z", "iopub.status.idle": "2022-12-14T22:13:37.942142Z", "shell.execute_reply": "2022-12-14T22:13:37.941487Z" }, "id": "lqe9obf7suIj" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using default config.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmp23fn1_x6\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmp23fn1_x6', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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/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": [ "INFO:tensorflow:Calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/adagrad.py:138: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Call initializer instance with the dtype argument instead of passing it to the constructor\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": [ "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/tmp23fn1_x6/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:loss = 2.6736233, step = 0\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/tmp23fn1_x6/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:Loss for final step: 0.008751018.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def _input_fn():\n", " return tf1.data.Dataset.from_tensor_slices((features, labels)).batch(1)\n", "\n", "def _eval_input_fn():\n", " return tf1.data.Dataset.from_tensor_slices(\n", " (eval_features, eval_labels)).batch(1)\n", "\n", "def _model_fn(features, labels, mode):\n", " logits = tf1.layers.Dense(2)(features)\n", " predictions = tf.math.argmax(input=logits, axis=1)\n", " loss = tf1.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)\n", " optimizer = tf1.train.AdagradOptimizer(0.05)\n", " train_op = optimizer.minimize(loss, global_step=tf1.train.get_global_step())\n", " accuracy = tf1.metrics.accuracy(labels=labels, predictions=predictions)\n", " return tf1.estimator.EstimatorSpec(mode, \n", " predictions=predictions,\n", " loss=loss, \n", " train_op=train_op,\n", " eval_metric_ops={'accuracy': accuracy})\n", "\n", "estimator = tf1.estimator.Estimator(model_fn=_model_fn)\n", "estimator.train(_input_fn)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T22:13:37.945765Z", "iopub.status.busy": "2022-12-14T22:13:37.945019Z", "iopub.status.idle": "2022-12-14T22:13:38.316471Z", "shell.execute_reply": "2022-12-14T22:13:38.315817Z" }, "id": "HsOpjW5plH9Q" }, "outputs": [ { "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 2022-12-14T22:13:38\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/tmp23fn1_x6/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:Inference Time : 0.25721s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Finished evaluation at 2022-12-14-22:13:38\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving dict for global step 3: accuracy = 0.6666667, global_step = 3, loss = 2.0419586\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3: /tmpfs/tmp/tmp23fn1_x6/model.ckpt-3\n" ] }, { "data": { "text/plain": [ "{'accuracy': 0.6666667, 'loss': 2.0419586, 'global_step': 3}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "estimator.evaluate(_eval_input_fn)" ] }, { "cell_type": "markdown", "metadata": { "id": "Wk4C6qA_OaQx" }, "source": [ "また、指標は `tf.estimator.add_metrics()` を介してエスティメータに直接追加できます。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T22:13:38.320076Z", "iopub.status.busy": "2022-12-14T22:13:38.319560Z", "iopub.status.idle": "2022-12-14T22:13:38.592375Z", "shell.execute_reply": "2022-12-14T22:13:38.591775Z" }, "id": "B2lpLOh9Owma" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmp23fn1_x6', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_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": [ "INFO:tensorflow:Calling model_fn.\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:Done calling model_fn.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Starting evaluation at 2022-12-14T22:13:38\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/tmp23fn1_x6/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:Inference Time : 0.16179s\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Finished evaluation at 2022-12-14-22:13:38\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving dict for global step 3: accuracy = 0.6666667, global_step = 3, loss = 2.0419586, mean_squared_error = 0.33333334\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Saving 'checkpoint_path' summary for global step 3: /tmpfs/tmp/tmp23fn1_x6/model.ckpt-3\n" ] }, { "data": { "text/plain": [ "{'accuracy': 0.6666667,\n", " 'loss': 2.0419586,\n", " 'mean_squared_error': 0.33333334,\n", " 'global_step': 3}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def mean_squared_error(labels, predictions):\n", " labels = tf.cast(labels, predictions.dtype)\n", " return {\"mean_squared_error\": \n", " tf1.metrics.mean_squared_error(labels=labels, predictions=predictions)}\n", "\n", "estimator = tf1.estimator.add_metrics(estimator, mean_squared_error)\n", "estimator.evaluate(_eval_input_fn)" ] }, { "cell_type": "markdown", "metadata": { "id": "KEmzBjfnsxwT" }, "source": [ "## TF2: tf.keras.Model を使用した Keras メトリクス API\n", "\n", "TF2 では、`tf.keras.metrics` にすべての指標クラスと関数が含まれています。これらは OOP スタイルで設計されており、他の `tf.keras` API と密接に統合されています。すべての指標は `tf.keras.metrics` 名前空間で見つけることができ、通常は `tf.compat.v1.metrics` と `tf.keras.metrics` の間に直接マッピングがあります。\n", "\n", "次の例では、指標が `model.compile()` メソッドに追加されています。ユーザーは、ラベルと予測テンソルを指定せずに、指標インスタンスを作成するだけで済みます。Keras モデルは、モデルの出力とラベルを指標オブジェクトにルーティングします。" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T22:13:38.596142Z", "iopub.status.busy": "2022-12-14T22:13:38.595500Z", "iopub.status.idle": "2022-12-14T22:13:38.651963Z", "shell.execute_reply": "2022-12-14T22:13:38.651351Z" }, "id": "atVciNgPs0fw" }, "outputs": [], "source": [ "dataset = tf.data.Dataset.from_tensor_slices((features, labels)).batch(1)\n", "eval_dataset = tf.data.Dataset.from_tensor_slices(\n", " (eval_features, eval_labels)).batch(1)\n", "\n", "inputs = tf.keras.Input((2,))\n", "logits = tf.keras.layers.Dense(2)(inputs)\n", "predictions = tf.math.argmax(input=logits, axis=1)\n", "model = tf.keras.models.Model(inputs, predictions)\n", "optimizer = tf.keras.optimizers.Adagrad(learning_rate=0.05)\n", "\n", "model.compile(optimizer, loss='mse', metrics=[tf.keras.metrics.Accuracy()])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T22:13:38.655633Z", "iopub.status.busy": "2022-12-14T22:13:38.655114Z", "iopub.status.idle": "2022-12-14T22:13:38.784404Z", "shell.execute_reply": "2022-12-14T22:13:38.783809Z" }, "id": "Kip65sYBlKiu" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/3 [=========>....................] - ETA: 0s - loss: 1.0000 - accuracy: 0.0000e+00" ] }, { "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", "3/3 [==============================] - 0s 4ms/step - loss: 0.3333 - accuracy: 0.6667\n" ] }, { "data": { "text/plain": [ "{'loss': 0.3333333432674408, 'accuracy': 0.6666666865348816}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(eval_dataset, return_dict=True)" ] }, { "cell_type": "markdown", "metadata": { "id": "_mcGoCm_X1V0" }, "source": [ "Eager execution を有効にすると、`tf.keras.metrics.Metric` インスタンスを直接使用して、numpy データまたは Eager テンソルを評価できます。`tf.keras.metrics.Metric` オブジェクトはステートフルコンテナーです。指標値は `metric.update_state(y_true, y_pred)` で更新でき、結果は `metrics.result() `で取得できます。\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T22:13:38.787561Z", "iopub.status.busy": "2022-12-14T22:13:38.787316Z", "iopub.status.idle": "2022-12-14T22:13:38.802645Z", "shell.execute_reply": "2022-12-14T22:13:38.801945Z" }, "id": "TVGn5_IhYhtG" }, "outputs": [ { "data": { "text/plain": [ "0.75" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy = tf.keras.metrics.Accuracy()\n", "\n", "accuracy.update_state(y_true=[0, 0, 1, 1], y_pred=[0, 0, 0, 1])\n", "accuracy.result().numpy()\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2022-12-14T22:13:38.805760Z", "iopub.status.busy": "2022-12-14T22:13:38.805202Z", "iopub.status.idle": "2022-12-14T22:13:38.814960Z", "shell.execute_reply": "2022-12-14T22:13:38.814314Z" }, "id": "wQEV2hHtY_su" }, "outputs": [ { "data": { "text/plain": [ "0.41666666" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy.update_state(y_true=[0, 0, 1, 1], y_pred=[0, 0, 0, 0])\n", "accuracy.update_state(y_true=[0, 0, 1, 1], y_pred=[1, 1, 0, 0])\n", "accuracy.result().numpy()" ] }, { "cell_type": "markdown", "metadata": { "id": "E3F3ElcyadW-" }, "source": [ "`tf.keras.metrics.Metric` の詳細については、`tf.keras.metrics.Metric` の API ドキュメントと[移行ガイド](https://www.tensorflow.org/guide/effective_tf2#new-style_metrics_and_losses)を参照してください。" ] }, { "cell_type": "markdown", "metadata": { "id": "eXKY9HEulxQC" }, "source": [ "## TF1.x オプティマイザの Keras オプティマイザへの移行\n", "\n", "Adam オプティマイザや[勾配降下オプティマイザ](https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/AdamOptimizer)などの tf.compat.v1.train 内のオプティマイザは、`tf.keras.optimizers` 内に同等のものをもちます。\n", "\n", "以下の表は、これらのレガシーオプティマイザを Keras の同等のものに変換する方法をまとめたものです。追加の手順（[デフォルトの学習率の更新](../../guide/effective_tf2.ipynb#optimizer_defaults)など）が必要でない限り、TF1.x バージョンを TF2 バージョンに直接置き換えることができます。\n", "\n", "オプティマイザを変換すると、[古いチェックポイントの互換性が失われる可能性があること](./migrating_checkpoints.ipynb)に注意してください。\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TF1.xTF2追加の手順
`tf.v1.train.GradientDescentOptimizer``tf.keras.optimizers.SGD`なし
`tf.v1.train.MomentumOptimizer``tf.keras.optimizers.SGD``momentum` 引数を含む
`tf.v1.train.AdamOptimizer``tf.keras.optimizers.Adam``beta1` および `beta2` 引数の名前を `beta_1` および `beta_2` に変更する
`tf.v1.train.RMSPropOptimizer``tf.keras.optimizers.RMSprop``decay` 引数の名前を `rho` に変更する
`tf.v1.train.AdadeltaOptimizer``tf.keras.optimizers.Adadelta`なし
`tf.v1.train.AdagradOptimizer``tf.keras.optimizers.Adagrad`なし
`tf.v1.train.FtrlOptimizer``tf.keras.optimizers.Ftrl``accum_name` および `linear_name` 引数を削除する
`tf.contrib.AdamaxOptimizer``tf.keras.optimizers.Adamax``beta1` および `beta2` 引数の名前を `beta_1` および `beta_2` に変更する
`tf.contrib.Nadam``tf.keras.optimizers.Nadam``beta1` および `beta2` 引数の名前を `beta_1` および `beta_2` に変更する
\n", "\n", "注意: TF2 では、すべてのイプシロン（数値安定定数）のデフォルトが `1e-8` ではなく `1e-7` になりました。ほとんどの場合、この違いは無視できます。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "metrics_optimizers.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 }