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警告: 新しいコードには Estimators は推奨されません。Estimators は
v1.Sessionスタイルのコードを実行しますが、これは正しく記述するのはより難しく、特に TF 2 コードと組み合わせると予期しない動作をする可能性があります。Estimators は、互換性保証の対象となりますが、セキュリティの脆弱性以外の修正は行われません。詳細については、移行ガイドを参照してください。
概要
TensorFlow Estimator は、TensorFlow でサポートされており、新規または既存の tf.keras モデルから作成することができます。このチュートリアルには、このプロセスの完全な最小限の例が含まれます。
注意: Keras モデルがある場合は、Estimator に変換せずに、直接 tf.distribute ストラテジーで使用することができます。したがって、model_to_estimator は推奨されなくなりました。
セットアップ
import tensorflow as tf
import numpy as np
import tensorflow_datasets as tfds
2022-12-15 02:45:18.105988: 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 2022-12-15 02:45:18.106099: 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 2022-12-15 02:45:18.106110: 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.
単純な Keras モデルを作成する。
Keras では、レイヤーを組み合わせてモデルを構築します。モデルは（通常）レイヤーのグラフです。最も一般的なモデルのタイプはレイヤーのスタックである tf.keras.Sequential モデルです。
単純で完全に接続されたネットワーク（多層パーセプトロン）を構築するには、以下を実行します。
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(16, activation='relu', input_shape=(4,)),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(3)
])
モデルをコンパイルして要約を取得します。
model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer='adam')
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 16) 80
dropout (Dropout) (None, 16) 0
dense_1 (Dense) (None, 3) 51
=================================================================
Total params: 131
Trainable params: 131
Non-trainable params: 0
_________________________________________________________________
入力関数を作成する
Datasets API を使用して、大規模なデータセットまたはマルチデバイストレーニングにスケーリングします。
Estimator には、いつどのように入力パイプラインが構築されるのかを制御する必要があります。これを行えるようにするには、"入力関数" または input_fn が必要です。Estimator は引数なしでこの関数を呼び出します。input_fn は、tf.data.Dataset を返す必要があります。
def input_fn():
split = tfds.Split.TRAIN
dataset = tfds.load('iris', split=split, as_supervised=True)
dataset = dataset.map(lambda features, labels: ({'dense_input':features}, labels))
dataset = dataset.batch(32).repeat()
return dataset
input_fn をテストします。
for features_batch, labels_batch in input_fn().take(1):
print(features_batch)
print(labels_batch)
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
{'dense_input': <tf.Tensor: shape=(32, 4), dtype=float32, numpy=
array([[5.1, 3.4, 1.5, 0.2],
[7.7, 3. , 6.1, 2.3],
[5.7, 2.8, 4.5, 1.3],
[6.8, 3.2, 5.9, 2.3],
[5.2, 3.4, 1.4, 0.2],
[5.6, 2.9, 3.6, 1.3],
[5.5, 2.6, 4.4, 1.2],
[5.5, 2.4, 3.7, 1. ],
[4.6, 3.4, 1.4, 0.3],
[7.7, 2.8, 6.7, 2. ],
[7. , 3.2, 4.7, 1.4],
[4.6, 3.2, 1.4, 0.2],
[6.5, 3. , 5.2, 2. ],
[5.5, 4.2, 1.4, 0.2],
[5.4, 3.9, 1.3, 0.4],
[5. , 3.5, 1.3, 0.3],
[5.1, 3.8, 1.5, 0.3],
[4.8, 3. , 1.4, 0.1],
[6.5, 3. , 5.8, 2.2],
[7.6, 3. , 6.6, 2.1],
[6.7, 3.3, 5.7, 2.1],
[7.9, 3.8, 6.4, 2. ],
[6.7, 3. , 5.2, 2.3],
[5.8, 4. , 1.2, 0.2],
[6.3, 2.5, 5. , 1.9],
[5. , 3. , 1.6, 0.2],
[6.9, 3.1, 5.1, 2.3],
[6.1, 3. , 4.6, 1.4],
[5.8, 2.7, 4.1, 1. ],
[5.2, 2.7, 3.9, 1.4],
[6.7, 3. , 5. , 1.7],
[5.7, 2.6, 3.5, 1. ]], dtype=float32)>}
tf.Tensor([0 2 1 2 0 1 1 1 0 2 1 0 2 0 0 0 0 0 2 2 2 2 2 0 2 0 2 1 1 1 1 1], shape=(32,), dtype=int64)
tf.keras モデルから Estimator を作成する。
tf.keras.Model は、tf.estimator API を使って、tf.keras.estimator.model_to_estimator を持つ tf.estimator.Estimator オブジェクトにモデルを変換することで、トレーニングすることができます。
import tempfile
model_dir = tempfile.mkdtemp()
keras_estimator = tf.keras.estimator.model_to_estimator(
keras_model=model, model_dir=model_dir)
INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
INFO:tensorflow:Using the Keras model provided.
INFO:tensorflow:Using the Keras model provided.
WARNING:absl:You are using `tf.keras.optimizers.experimental.Optimizer` in TF estimator, which only supports `tf.keras.optimizers.legacy.Optimizer`. Automatically converting your optimizer to `tf.keras.optimizers.legacy.Optimizer`.
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/backend.py:451: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
warnings.warn(
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmp9h0gesw0', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_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}
2022-12-15 02:45:25.103464: W tensorflow/c/c_api.cc:291] Operation '{name:'training/Adam/dense_1/bias/v/Assign' id:218 op device:{requested: '', assigned: ''} def:{ { {node training/Adam/dense_1/bias/v/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](training/Adam/dense_1/bias/v, training/Adam/dense_1/bias/v/Initializer/zeros)} }' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.
INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmp9h0gesw0', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true
graph_options {
rewrite_options {
meta_optimizer_iterations: ONE
}
}
, '_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}
Estimator をトレーニングして評価します。
keras_estimator.train(input_fn=input_fn, steps=500)
eval_result = keras_estimator.evaluate(input_fn=input_fn, steps=10)
print('Eval result: {}'.format(eval_result))
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.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
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.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmpfs/tmp/tmp9h0gesw0/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting with WarmStartSettings: WarmStartSettings(ckpt_to_initialize_from='/tmpfs/tmp/tmp9h0gesw0/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting from: /tmpfs/tmp/tmp9h0gesw0/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting from: /tmpfs/tmp/tmp9h0gesw0/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-starting variables only in TRAINABLE_VARIABLES.
INFO:tensorflow:Warm-started 4 variables.
INFO:tensorflow:Warm-started 4 variables.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmp9h0gesw0/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmp9h0gesw0/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 2.432807, step = 0
INFO:tensorflow:loss = 2.432807, step = 0
INFO:tensorflow:global_step/sec: 44.6531
INFO:tensorflow:global_step/sec: 44.6531
INFO:tensorflow:loss = 1.0671942, step = 100 (2.241 sec)
INFO:tensorflow:loss = 1.0671942, step = 100 (2.241 sec)
INFO:tensorflow:global_step/sec: 46.4651
INFO:tensorflow:global_step/sec: 46.4651
INFO:tensorflow:loss = 0.82943535, step = 200 (2.152 sec)
INFO:tensorflow:loss = 0.82943535, step = 200 (2.152 sec)
INFO:tensorflow:global_step/sec: 46.1527
INFO:tensorflow:global_step/sec: 46.1527
INFO:tensorflow:loss = 0.6606032, step = 300 (2.167 sec)
INFO:tensorflow:loss = 0.6606032, step = 300 (2.167 sec)
INFO:tensorflow:global_step/sec: 46.4966
INFO:tensorflow:global_step/sec: 46.4966
INFO:tensorflow:loss = 0.56208, step = 400 (2.151 sec)
INFO:tensorflow:loss = 0.56208, step = 400 (2.151 sec)
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 500...
INFO:tensorflow:Saving checkpoints for 500 into /tmpfs/tmp/tmp9h0gesw0/model.ckpt.
INFO:tensorflow:Saving checkpoints for 500 into /tmpfs/tmp/tmp9h0gesw0/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 500...
INFO:tensorflow:Loss for final step: 0.5922533.
INFO:tensorflow:Loss for final step: 0.5922533.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/engine/training_v1.py:2333: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
updates = self.state_updates
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2022-12-15T02:45:38
INFO:tensorflow:Starting evaluation at 2022-12-15T02:45:38
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmp9h0gesw0/model.ckpt-500
INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmp9h0gesw0/model.ckpt-500
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [1/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [2/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [3/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [4/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [5/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [6/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [7/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [8/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [9/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Evaluation [10/10]
INFO:tensorflow:Inference Time : 0.50137s
INFO:tensorflow:Inference Time : 0.50137s
INFO:tensorflow:Finished evaluation at 2022-12-15-02:45:39
INFO:tensorflow:Finished evaluation at 2022-12-15-02:45:39
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.5014641
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.5014641
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmpfs/tmp/tmp9h0gesw0/model.ckpt-500
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmpfs/tmp/tmp9h0gesw0/model.ckpt-500
Eval result: {'loss': 0.5014641, 'global_step': 500}