{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "1l8bWGmIJuQa" }, "source": [ "##### Copyright 2019 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "execution": { "iopub.execute_input": "2024-01-11T18:14:23.880639Z", "iopub.status.busy": "2024-01-11T18:14:23.880406Z", "iopub.status.idle": "2024-01-11T18:14:23.884189Z", "shell.execute_reply": "2024-01-11T18:14:23.883596Z" }, "id": "CPSnXS88KFEo" }, "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": "89xNCIO5hiCj" }, "source": [ "# 分散ストラテジーを使ってモデルを保存して読み込む" ] }, { "cell_type": "markdown", "metadata": { "id": "9Ejs4QVxIdAm" }, "source": [ "\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": "A0lG6qgThxAS" }, "source": [ "## 概要\n", "\n", "このチュートリアルでは、トレーニング中またはトレーニング後に `tf.distribute.Strategy` を使用して SavedModel 形式でモデルを保存して読み込む方法を説明します。Keras モデルの保存と読み込みには、高レベル（`tf.keras.Model.save` と `tf.keras.models.load_model`）と低レベル（`tf.saved_model.save` と `tf.saved_model.load`）の 2 種類の API があります。\n", "\n", "SavedModel とシリアル化の全般的な内容については、[SavedModel ガイド](../../guide/saved_model.ipynb)と [Keras モデルのシリアル化ガイド](https://www.tensorflow.org/guide/keras/save_and_serialize)をお読みください。では、単純な例から始めましょう。\n", "\n", "注意: TensorFlow モデルはコードであるため、信頼できないコードには注意する必要があります。詳細は、[TensorFlow を安全に使用する](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md)をご覧ください。\n" ] }, { "cell_type": "markdown", "metadata": { "id": "FITHltVKQ4eZ" }, "source": [ "依存関係をインポートします。" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:23.887664Z", "iopub.status.busy": "2024-01-11T18:14:23.887433Z", "iopub.status.idle": "2024-01-11T18:14:26.592124Z", "shell.execute_reply": "2024-01-11T18:14:26.591394Z" }, "id": "RWG5HchAiOrZ" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-01-11 18:14:24.741176: 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", "2024-01-11 18:14:24.741223: 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", "2024-01-11 18:14:24.742716: 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", "\n", "import tensorflow as tf\n" ] }, { "cell_type": "markdown", "metadata": { "id": "qqapWj98ptNV" }, "source": [ "TensorFlow Datasets と `tf.data` でデータを読み込んで準備し、`tf.distribute.MirroredStrategy` を使ってモデルを作成します。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:26.596321Z", "iopub.status.busy": "2024-01-11T18:14:26.595896Z", "iopub.status.idle": "2024-01-11T18:14:29.791838Z", "shell.execute_reply": "2024-01-11T18:14:29.791087Z" }, "id": "yrYiAf_ziRyw" }, "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" ] } ], "source": [ "mirrored_strategy = tf.distribute.MirroredStrategy()\n", "\n", "def get_data():\n", " datasets = tfds.load(name='mnist', as_supervised=True)\n", " mnist_train, mnist_test = datasets['train'], datasets['test']\n", "\n", " BUFFER_SIZE = 10000\n", "\n", " BATCH_SIZE_PER_REPLICA = 64\n", " BATCH_SIZE = BATCH_SIZE_PER_REPLICA * mirrored_strategy.num_replicas_in_sync\n", "\n", " def scale(image, label):\n", " image = tf.cast(image, tf.float32)\n", " image /= 255\n", "\n", " return image, label\n", "\n", " train_dataset = mnist_train.map(scale).cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)\n", " eval_dataset = mnist_test.map(scale).batch(BATCH_SIZE)\n", "\n", " return train_dataset, eval_dataset\n", "\n", "def get_model():\n", " with mirrored_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=[tf.metrics.SparseCategoricalAccuracy()])\n", " return model" ] }, { "cell_type": "markdown", "metadata": { "id": "qmU4Y3feS9Na" }, "source": [ "`tf.keras.Model.fit` を使用してモデルをトレーニングします。 " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:29.796047Z", "iopub.status.busy": "2024-01-11T18:14:29.795799Z", "iopub.status.idle": "2024-01-11T18:14:42.302658Z", "shell.execute_reply": "2024-01-11T18:14:42.301880Z" }, "id": "zmGurbJmS_vN" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2024-01-11 18:14:31.467356: 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 1/2\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 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8s 8ms/step - loss: 0.3291 - sparse_categorical_accuracy: 0.9094\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 8s - loss: 0.1046 - sparse_categorical_accuracy: 0.9766" ] }, { "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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 9/235 [>.............................] - ETA: 1s - loss: 0.1336 - sparse_categorical_accuracy: 0.9614" ] }, { "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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 17/235 [=>............................] - 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ETA: 0s - loss: 0.1072 - sparse_categorical_accuracy: 0.9684" ] }, { "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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "235/235 [==============================] - 2s 7ms/step - loss: 0.1068 - sparse_categorical_accuracy: 0.9685\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = get_model()\n", "train_dataset, eval_dataset = get_data()\n", "model.fit(train_dataset, epochs=2)" ] }, { "cell_type": "markdown", "metadata": { "id": "L01wjgvRizHS" }, "source": [ "## モデルを保存して読み込む\n", "\n", "作業に使用する単純なモデルを準備できたので、保存と読み込みに使用する API を見てみましょう。使用できる API には、以下の 2 種類があります。\n", "\n", "- 高レベル（Keras）: `Model.save` および `tf.keras.models.load_model`（`.keras` zip アーカイブ形式）\n", "- 低レベル: `tf.saved_model.save` および `tf.saved_model.load`（TF SavedModel 形式）\n" ] }, { "cell_type": "markdown", "metadata": { "id": "FX_IF2F1tvFs" }, "source": [ "### Keras API" ] }, { "cell_type": "markdown", "metadata": { "id": "O8xfceg4Z3H_" }, "source": [ "Keras API を使用したモデルの保存と読み込みの例を以下に示します。" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:42.306714Z", "iopub.status.busy": "2024-01-11T18:14:42.306471Z", "iopub.status.idle": "2024-01-11T18:14:42.372087Z", "shell.execute_reply": "2024-01-11T18:14:42.371426Z" }, "id": "LYOStjV5knTQ" }, "outputs": [], "source": [ "keras_model_path = '/tmp/keras_save.keras'\n", "model.save(keras_model_path)" ] }, { "cell_type": "markdown", "metadata": { "id": "yvQIdQp3zNMp" }, "source": [ "`tf.distribute.Strategy` を使用せずにモデルを復元します。" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:42.375805Z", "iopub.status.busy": "2024-01-11T18:14:42.375534Z", "iopub.status.idle": "2024-01-11T18:14:45.445350Z", "shell.execute_reply": "2024-01-11T18:14:45.444585Z" }, "id": "WrXAAVtrzRgv" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - 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3s 11ms/step - loss: 0.0531 - sparse_categorical_accuracy: 0.9844\n" ] } ], "source": [ "another_strategy = tf.distribute.OneDeviceStrategy('/cpu:0')\n", "with another_strategy.scope():\n", " restored_keras_model_ds = tf.keras.models.load_model(keras_model_path)\n", " restored_keras_model_ds.fit(train_dataset, epochs=2)" ] }, { "cell_type": "markdown", "metadata": { "id": "PdiiPmL5tQk5" }, "source": [ "`Model.fit` 出力からわかるように、`tf.distribute.Strategy` を使って期待どおり読み込まれました。ここで使用されるストラテジーは、保存前と同じストラテジーである必要はありません。 " ] }, { "cell_type": "markdown", "metadata": { "id": "3CrXIbmFt0f6" }, "source": [ "### `tf.saved_model` API" ] }, { "cell_type": "markdown", "metadata": { "id": "HtGzPp6et4Em" }, "source": [ "より低レベルの API を使用したモデルの保存方法は、Keras API を使う方法に似ています。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:51.697541Z", "iopub.status.busy": "2024-01-11T18:14:51.697228Z", "iopub.status.idle": "2024-01-11T18:14:52.257784Z", "shell.execute_reply": "2024-01-11T18:14:52.257138Z" }, "id": "4y6T31APuCqK" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] } ], "source": [ "model = get_model() # get a fresh model\n", "saved_model_path = '/tmp/tf_save'\n", "tf.saved_model.save(model, saved_model_path)" ] }, { "cell_type": "markdown", "metadata": { "id": "q1QNRYcwuRll" }, "source": [ "読み込みは、`tf.saved_model.load` を使用して行えますが、これは低レベル API（したがって、より幅広いユースケースのある API）であるため、Keras モデルを返しません。代わりに、推論を行うために使用できる関数を含むオブジェクトを返します。以下に例を示します。" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:52.261526Z", "iopub.status.busy": "2024-01-11T18:14:52.261263Z", "iopub.status.idle": "2024-01-11T18:14:52.387887Z", "shell.execute_reply": "2024-01-11T18:14:52.387233Z" }, "id": "aaEKqBSPwAuM" }, "outputs": [], "source": [ "DEFAULT_FUNCTION_KEY = 'serving_default'\n", "loaded = tf.saved_model.load(saved_model_path)\n", "inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]" ] }, { "cell_type": "markdown", "metadata": { "id": "x65l7AaHUZCA" }, "source": [ "読み込まれたオブジェクトには、それぞれにキーが関連付けられた複数の関数が含まれている可能性があります。`\"serving_default\"` キーは、保存された Keras モデルを使用した推論関数のデフォルトのキーです。この関数で推論するには、以下のようにします。 " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:52.391440Z", "iopub.status.busy": "2024-01-11T18:14:52.391141Z", "iopub.status.idle": "2024-01-11T18:14:52.709303Z", "shell.execute_reply": "2024-01-11T18:14:52.708505Z" }, "id": "5Ore5q8-UjW1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'dense_3': }\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-01-11 18:14:52.675006: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n" ] } ], "source": [ "predict_dataset = eval_dataset.map(lambda image, label: image)\n", "for batch in predict_dataset.take(1):\n", " print(inference_func(batch))" ] }, { "cell_type": "markdown", "metadata": { "id": "osB1LY8WwUJZ" }, "source": [ "また、分散方法で読み込んで推論を実行することもできます。" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:52.713525Z", "iopub.status.busy": "2024-01-11T18:14:52.713198Z", "iopub.status.idle": "2024-01-11T18:14:53.571847Z", "shell.execute_reply": "2024-01-11T18:14:53.570999Z" }, "id": "iDYvu12zYTmT" }, "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" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-01-11 18:14:52.889461: 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": [ "WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "{'dense_3': PerReplica:{\n", " 0: ,\n", " 1: ,\n", " 2: ,\n", " 3: \n", "}}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-01-11 18:14:53.554141: W tensorflow/core/kernels/data/cache_dataset_ops.cc:858] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.\n" ] } ], "source": [ "another_strategy = tf.distribute.MirroredStrategy()\n", "with another_strategy.scope():\n", " loaded = tf.saved_model.load(saved_model_path)\n", " inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]\n", "\n", " dist_predict_dataset = another_strategy.experimental_distribute_dataset(\n", " predict_dataset)\n", "\n", " # Calling the function in a distributed manner\n", " for batch in dist_predict_dataset:\n", " result = another_strategy.run(inference_func, args=(batch,))\n", " print(result)\n", " break" ] }, { "cell_type": "markdown", "metadata": { "id": "hWGSukoyw3fF" }, "source": [ "復元された関数の呼び出しは、保存されたモデル（`tf.keras.Model.predict`）に対するフォワードパスです。読み込まれた関数をトレーニングし続ける場合はどうでしょうか。または読み込まれた関数をより大きなモデルに埋め込むには？一般的には、この読み込まれたオブジェクトを Keras レイヤーにラップして行うことができます。幸いにも、[TF Hub](https://www.tensorflow.org/hub) には、以下に示すとおり、この目的に使用できる [`hub.KerasLayer`](https://github.com/tensorflow/hub/blob/master/tensorflow_hub/keras_layer.py) が用意されています。" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:14:53.576532Z", "iopub.status.busy": "2024-01-11T18:14:53.576251Z", "iopub.status.idle": "2024-01-11T18:15:01.268472Z", "shell.execute_reply": "2024-01-11T18:15:01.267722Z" }, "id": "clfk3hQoyKu6" }, "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" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2024-01-11 18:14:54.395157: 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 1/2\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: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": [ "\r", " 1/235 [..............................] - ETA: 11:14 - loss: 2.3140 - sparse_categorical_accuracy: 0.0586" ] }, { "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\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: 1.8867 - sparse_categorical_accuracy: 0.4834 " ] }, { "name": "stdout", "output_type": "stream", "text": [ 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[============================>.] - ETA: 0s - loss: 0.3450 - sparse_categorical_accuracy: 0.9019" ] }, { "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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "235/235 [==============================] - 4s 7ms/step - loss: 0.3436 - sparse_categorical_accuracy: 0.9023\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", " 1/235 [..............................] - ETA: 8s - loss: 0.1879 - sparse_categorical_accuracy: 0.9531" ] }, { "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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", " 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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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "233/235 [============================>.] - ETA: 0s - loss: 0.1129 - sparse_categorical_accuracy: 0.9682" ] }, { "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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r", "235/235 [==============================] - 2s 7ms/step - loss: 0.1126 - sparse_categorical_accuracy: 0.9682\n" ] } ], "source": [ "import tensorflow_hub as hub\n", "\n", "def build_model(loaded):\n", " x = tf.keras.layers.Input(shape=(28, 28, 1), name='input_x')\n", " # Wrap what's loaded to a KerasLayer\n", " keras_layer = hub.KerasLayer(loaded, trainable=True)(x)\n", " model = tf.keras.Model(x, keras_layer)\n", " return model\n", "\n", "another_strategy = tf.distribute.MirroredStrategy()\n", "with another_strategy.scope():\n", " loaded = tf.saved_model.load(saved_model_path)\n", " model = build_model(loaded)\n", "\n", " model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n", " optimizer=tf.keras.optimizers.Adam(),\n", " metrics=[tf.metrics.SparseCategoricalAccuracy()])\n", " model.fit(train_dataset, epochs=2)" ] }, { "cell_type": "markdown", "metadata": { "id": "Oe1z_OtSJlu2" }, "source": [ "上記の例では、`hub.KerasLayer` は `tf.saved_model.load()` から読み込まれた結果を、別のモデルの構築に使用できる Keras レイヤーにラップしています。転移学習を行う際に非常に便利な手法です。 " ] }, { "cell_type": "markdown", "metadata": { "id": "KFDOZpK5Wa3W" }, "source": [ "### どの API を使用すべきですか？" ] }, { "cell_type": "markdown", "metadata": { "id": "GC6GQ9HDLxD6" }, "source": [ "保存においては、Keras モデルを使用している場合は、低レベル API が実現できる追加の制御が必要でない限り、Keras の `Model.save` API を使用します。保存しているものが Keras モデルでない場合は、低レベル API の `tf.saved_model.save` しか使用できません。\n", "\n", "読み込みにおいては、使用する API はモデルの読み込みから得ようとしているものによって異なります。Keras モデルを使用できない場合（または使用したくない場合）は、`tf.saved_model.load` を使用し、使用できる場合は `tf.keras.models.load_model` を使用します。Keras モデルを保存した場合にのみ、Keras モデルを読み込めることに注意してください。\n", "\n", "API を混在させることも可能です。`model.save` で Keras モデルを保存し、低レベルの `tf.saved_model.load` API を使用して、非 Keras モデルを読み込むことができます。 " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:15:01.272770Z", "iopub.status.busy": "2024-01-11T18:15:01.272218Z", "iopub.status.idle": "2024-01-11T18:15:01.942640Z", "shell.execute_reply": "2024-01-11T18:15:01.941959Z" }, "id": "Ktwg2GwnXE8v" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] }, { "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": [ "model = get_model()\n", "\n", "# Saving the model using Keras `Model.save`\n", "model.save(saved_model_path)\n", "\n", "another_strategy = tf.distribute.MirroredStrategy()\n", "# Loading the model using the lower-level API\n", "with another_strategy.scope():\n", " loaded = tf.saved_model.load(saved_model_path)" ] }, { "cell_type": "markdown", "metadata": { "id": "0Z7lSj8nZiW5" }, "source": [ "### ローカルデバイスからの読み込みまたは保存" ] }, { "cell_type": "markdown", "metadata": { "id": "NVAjWcosZodw" }, "source": [ "ローカル I/O デバイスから読み込みと保存を行い、リモートデバイスでトレーニングする場合（Cloud TPU を使用する場合など）、`tf.saved_model.SaveOptions` と `tf.saved_model.LoadOptions` に `experimental_io_device` を使用して、I/O デバイスを `localhost` に設定する必要があります。以下に例を示します。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:15:01.946527Z", "iopub.status.busy": "2024-01-11T18:15:01.946280Z", "iopub.status.idle": "2024-01-11T18:15:02.734691Z", "shell.execute_reply": "2024-01-11T18:15:02.733996Z" }, "id": "jFcuzsI94bNA" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] }, { "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": [ "model = get_model()\n", "\n", "# Saving the model to a path on localhost.\n", "saved_model_path = '/tmp/tf_save'\n", "save_options = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')\n", "model.save(saved_model_path, options=save_options)\n", "\n", "# Loading the model from a path on localhost.\n", "another_strategy = tf.distribute.MirroredStrategy()\n", "with another_strategy.scope():\n", " load_options = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')\n", " loaded = tf.keras.models.load_model(saved_model_path, options=load_options)" ] }, { "cell_type": "markdown", "metadata": { "id": "hJTWOnC9iuA3" }, "source": [ "### 警告" ] }, { "cell_type": "markdown", "metadata": { "id": "2cCSZrD7VCxe" }, "source": [ "Keras モデルを特定の方法で作成してから、トレーニングする前に保存するという、以下のような特別なケースがあります。" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:15:02.739997Z", "iopub.status.busy": "2024-01-11T18:15:02.739514Z", "iopub.status.idle": "2024-01-11T18:15:02.749005Z", "shell.execute_reply": "2024-01-11T18:15:02.748430Z" }, "id": "gurSIbDFjOBc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.SubclassedModel object at 0x7f7f09dc29a0>, because it is not built.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.SubclassedModel object at 0x7f7f09dc29a0>, because it is not built.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ValueError: Model <__main__.SubclassedModel object at 0x7f7f09dc29a0> cannot be saved either because the input shape is not available or because the forward pass of the model is not defined.To define a forward pass, please override `Model.call()`. To specify an input shape, either call `build(input_shape)` directly, or call the model on actual data using `Model()`, `Model.fit()`, or `Model.predict()`. If you have a custom training step, please make sure to invoke the forward pass in train step through `Model.__call__`, i.e. `model(inputs)`, as opposed to `model.call()`.\n" ] } ], "source": [ "class SubclassedModel(tf.keras.Model):\n", " \"\"\"Example model defined by subclassing `tf.keras.Model`.\"\"\"\n", "\n", " output_name = 'output_layer'\n", "\n", " def __init__(self):\n", " super(SubclassedModel, self).__init__()\n", " self._dense_layer = tf.keras.layers.Dense(\n", " 5, dtype=tf.dtypes.float32, name=self.output_name)\n", "\n", " def call(self, inputs):\n", " return self._dense_layer(inputs)\n", "\n", "my_model = SubclassedModel()\n", "try:\n", " my_model.save(saved_model_path)\n", "except ValueError as e:\n", " print(f'{type(e).__name__}: ', *e.args)" ] }, { "cell_type": "markdown", "metadata": { "id": "D4qMyXFDSPDO" }, "source": [ "SavedModel は `tf.function` をトレースする際に生成される `tf.types.experimental.ConcreteFunction` オブジェクトを保存します（詳細は、[グラフと tf.function の基本](../../guide/intro_to_graphs.ipynb)ガイドの*関数はいつトレースしますか？* をご覧ください）。このような `ValueError` が発生した場合、`Model.save` がトレースされた `ConcreteFunction` を見つけられなかったか作成できなかったことが原因です。\n", "\n", "**注意:** 少なくとも 1 つの `ConcreteFunction` がない場合にモデルを保存しないことをお勧めします。そうでない場合、低レベル API は、`ConcreteFunction` シグネチャのない状態で SavedModel を生成してしまうためです（SavedModel 形式については、[こちら](../../guide/saved_model.ipynb)をご覧ください）。以下に例を示します。" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:15:02.752144Z", "iopub.status.busy": "2024-01-11T18:15:02.751605Z", "iopub.status.idle": "2024-01-11T18:15:02.805836Z", "shell.execute_reply": "2024-01-11T18:15:02.805216Z" }, "id": "064SE47mYDj8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.SubclassedModel object at 0x7f7f09dc29a0>, because it is not built.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.SubclassedModel object at 0x7f7f09dc29a0>, because it is not built.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Skipping full serialization of Keras layer , because it is not built.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "WARNING:tensorflow:Skipping full serialization of Keras layer , because it is not built.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] }, { "data": { "text/plain": [ "_SignatureMap({})" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.saved_model.save(my_model, saved_model_path)\n", "x = tf.saved_model.load(saved_model_path)\n", "x.signatures" ] }, { "cell_type": "markdown", "metadata": { "id": "LRTxlASJX-cY" }, "source": [ "通常、モデルのフォワードパス（`call` メソッド）は、モデルが Keras の `Model.fit` メソッドを通じて初めて呼び出されたときに、自動的にトレースされます。また、最初のレイヤーを `tf.keras.layers.InputLayer` などにして、`input_shape` キーワード引数に渡すことで入力形状を設定している場合、Keras の [Sequential](https://www.tensorflow.org/guide/keras/sequential_model) API と [Functional](https://www.tensorflow.org/guide/keras/functional) API によって `ConcreteFunction` が生成されることもあります。\n", "\n", "モデルにトレース済みの `ConcreteFunction` が存在するかを確認するには、`Model.save_spec` が `None` になっていることを確認します。" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:15:02.809189Z", "iopub.status.busy": "2024-01-11T18:15:02.808708Z", "iopub.status.idle": "2024-01-11T18:15:02.812456Z", "shell.execute_reply": "2024-01-11T18:15:02.811747Z" }, "id": "xAXise4eR0YJ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(my_model.save_spec() is None)" ] }, { "cell_type": "markdown", "metadata": { "id": "G2G_FQrWJAO3" }, "source": [ "`tf.keras.Model.fit` を使ってモデルをトレーニングし、`save_spec` が定義され、モデルの保存が機能するかを確認しましょう。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "execution": { "iopub.execute_input": "2024-01-11T18:15:02.815983Z", "iopub.status.busy": "2024-01-11T18:15:02.815354Z", "iopub.status.idle": "2024-01-11T18:15:03.930980Z", "shell.execute_reply": "2024-01-11T18:15:03.930334Z" }, "id": "cv5LTi0zDkKS" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/7 [===>..........................] - ETA: 5s - loss: 4.8873" ] }, { "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\r", "7/7 [==============================] - 1s 2ms/step - loss: 4.7761\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 2/2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\r", "1/7 [===>..........................] - ETA: 0s - loss: 4.5952" ] }, { "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\r", "7/7 [==============================] - 0s 2ms/step - loss: 4.4889\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "False\n", "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(5, 5), dtype=tf.float32, name='gradient'), , 140183601399632), {}).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(5, 5), dtype=tf.float32, name='gradient'), , 140183601399632), {}).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(5,), dtype=tf.float32, name='gradient'), , 140183601400208), {}).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(5,), dtype=tf.float32, name='gradient'), , 140183601400208), {}).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(5, 5), dtype=tf.float32, name='gradient'), , 140183601399632), {}).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(5, 5), dtype=tf.float32, name='gradient'), , 140183601399632), {}).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(5,), dtype=tf.float32, name='gradient'), , 140183601400208), {}).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Unsupported signature for serialization: ((TensorSpec(shape=(5,), dtype=tf.float32, name='gradient'), , 140183601400208), {}).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:tensorflow:Assets written to: /tmp/tf_save/assets\n" ] } ], "source": [ "BATCH_SIZE_PER_REPLICA = 4\n", "BATCH_SIZE = BATCH_SIZE_PER_REPLICA * mirrored_strategy.num_replicas_in_sync\n", "\n", "dataset_size = 100\n", "dataset = tf.data.Dataset.from_tensors(\n", " (tf.range(5, dtype=tf.float32), tf.range(5, dtype=tf.float32))\n", " ).repeat(dataset_size).batch(BATCH_SIZE)\n", "\n", "my_model.compile(optimizer='adam', loss='mean_squared_error')\n", "my_model.fit(dataset, epochs=2)\n", "\n", "print(my_model.save_spec() is None)\n", "my_model.save(saved_model_path)" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "save_and_load.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 }