„TensorFlow“ vertintojai yra nuvertinti – štai kaip (ir kodėl) pereiti

Turinio apžvalga Setup Advantages Estimators Capabilities Using pre-made Estimators Write an input functions Define the feature columns. Instantiate the relevant pre-made Estimator. Call a training, evaluation, or inference method. Benefits of pre-made Estimators Custom Estimators Create an Estimator from a Keras model Saving object-based checkpoints with Estimator SavedModels from Estimators Using tf.distribute.Strategy with Estimator (Limited support) What’s supported now? Examples and tutorial Įspėjimas: „TensorFlow 2.15“ įtraukė galutinį „tf-estimator“ paketo išleidimą. „TensorFlow 2.16“ ar vėliau nebus galima naudoti vertintojų. Daugiau informacijos apie tai, kaip konvertuoti iš „Estimators“, rasite migracijos vadove. TensorFlow 2.15 įtraukė galutinį išleidimą Apskaičiavimai nebus prieinami „TensorFlow 2.16“ arba vėliau. Norėdami sužinoti daugiau apie tai, kaip konvertuoti iš Estimatoriai. Warning: tf-estimator migration guide Migracijos vadovas Šiame dokumente pateikiama —a aukšto lygio TensorFlow API. Apskaičiuokliai įterpia šiuos veiksmus: tf.estimator Treniruotės vertinimas Prognozė Eksportas į aptarnavimą „TensorFlow“ įgyvendina keletą iš anksto pagamintų vertintojų. „Custom“ vertintojai vis dar palaikomi, bet daugiausia kaip atgalinio suderinamumo priemonė. Visi vertintojai – iš anksto pagaminti arba pritaikyti – yra klasės, pagrįstos Iš klasės. Custom estimators should not be used for new code tf.estimator.Estimator Greitas pavyzdys – pabandykite Norėdami peržiūrėti API dizainą, patikrinkite . Apskaičiuokite tutorialus Baltoji knyga Įdiegti pip install -U tensorflow_datasets import tempfile import os import tensorflow as tf import tensorflow_datasets as tfds 2024-01-24 02:20:44.732508: 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 2024-01-24 02:20:44.732557: 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 2024-01-24 02:20:44.734001: 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 Privalumai Panašiai kaip a Vienas yra abstrakcijos lygio modelis. Šiuo metu kuriami tam tikri pajėgumai Tai yra: tf.keras.Model estimator tf.estimator tf.keras Parametrų serverio mokymas TFX integracija Vertintojų pajėgumai Vertintojai siūlo šiuos privalumus: Galite paleisti „Estimator“ pagrįstus modelius vietiniame prieglobos sistemoje arba paskirstytame kelių serverių aplinkoje nekeičiant modelio. Estimators provide a safe distributed training loop that controls how and when to: Load data Handle exceptions Create checkpoint files and recover from failures Save summaries for TensorBoard Kai rašote programą naudojant Estimators, turite atskirti duomenų įvesties vamzdelį nuo modelio. Naudojant iš anksto pagamintus įvertinimus Iš anksto pagaminti vertintojai leidžia jums dirbti daug aukštesniu konceptualiu lygmeniu nei pagrindiniai TensorFlow API. Jums nebereikia nerimauti dėl skaičiavimo grafiko ar seansų kūrimo, nes vertintojai tvarko visus "plumbing" už jus. , pavyzdžiui, yra iš anksto pagaminta Estimator klasė, kuri moko klasifikavimo modelius, pagrįstus tankiu, pašalintu neuroniniu tinklu. tf.estimator.DNNClassifier „TensorFlow“ programa, kuri remiasi iš anksto pagaminta „Estimator“, paprastai susideda iš šių keturių žingsnių: 1 Įveskite įvesties funkciją Pavyzdžiui, galite sukurti vieną funkciją, kad importuotumėte mokymo rinkinį, ir kitą funkciją, kad importuotumėte bandymo rinkinį. Žodynas, kuriame raktai yra funkcijų pavadinimai, o reikšmės yra Tensors (arba SparseTensors), kuriame yra atitinkami funkcijų duomenys Tensor, kuriame yra vienas ar daugiau etikečių Tų Reikėtų grąžinti a Tai sukuria poras tokiu formatu. input_fn tf.data.Dataset Pavyzdžiui, toliau pateikiamas kodas sukuria a Iš „Titaniko“ duomenų rinkinio failą : tf.data.Dataset train.csv def train_input_fn(): titanic_file = tf.keras.utils.get_file("train.csv", "https://storage.googleapis.com/tf-datasets/titanic/train.csv") titanic = tf.data.experimental.make_csv_dataset( titanic_file, batch_size=32, label_name="survived") titanic_batches = ( titanic.cache().repeat().shuffle(500) .prefetch(tf.data.AUTOTUNE)) return titanic_batches Tų Jis įvykdytas A Taip pat galima grąžinti a poros, kuriose yra grafinių tensorių, tačiau tai yra klaidų, esančių už paprastų atvejų, pvz., grąžinamų konstantų, ribų. input_fn tf.Graph (features_dics, labels) Apibrėžti funkcinius stulpelius. Kiekvienas identifikuoja funkcijos pavadinimą, jo tipą ir bet kokį įvesties išankstinį apdorojimą. tf.feature_column Pavyzdžiui, toliau pateikiamas fragmentas sukuria tris funkcijų stulpelius. Pirmasis naudoja amžiaus funkciją tiesiogiai kaip plaukiojančio taško įvestį. Antrasis naudoja klasės funkciją kaip kategorinį įvestį. Trečiasis naudoja embark_town kaip kategorinį įvestį, tačiau naudoja hashing triuką, kad būtų išvengta poreikio išvardyti parinktis ir nustatyti parinkčių skaičių. Norėdami gauti daugiau informacijos, patikrinkite . Žymės: Columns tutorial age = tf.feature_column.numeric_column('age') cls = tf.feature_column.categorical_column_with_vocabulary_list('class', ['First', 'Second', 'Third']) embark = tf.feature_column.categorical_column_with_hash_bucket('embark_town', 32) WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4100412726.py:1: numeric_column (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4100412726.py:2: categorical_column_with_vocabulary_list (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4100412726.py:3: categorical_column_with_hash_bucket (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model. Įveskite atitinkamą iš anksto sukurtą skaičiuoklę. Pavyzdžiui, čia pateikiamas iš anksto sukurto Estimator pavyzdys, pavadintas : LinearClassifier model_dir = tempfile.mkdtemp() model = tf.estimator.LinearClassifier( model_dir=model_dir, feature_columns=[embark, cls, age], n_classes=2 ) WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/1904628810.py:2: LinearClassifierV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/head_utils.py:54: BinaryClassHead.__init__ (from tensorflow_estimator.python.estimator.head.binary_class_head) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:944: Estimator.__init__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1844: RunConfig.__init__ (from tensorflow_estimator.python.estimator.run_config) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:Using default config. INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpbt9n791j', '_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} Norėdami gauti daugiau informacijos, galite eiti į . Linijinis klasifikatorius Tutorial Skambinkite mokymo, vertinimo ar išvados metodu. Kiekvienas vertintojas siūlo , ir ir metodus. train evaluate predict model = model.train(input_fn=train_input_fn, steps=100) WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:385: StopAtStepHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. Downloading data from https://storage.googleapis.com/tf-datasets/titanic/train.csv 30874/30874 [==============================] - 0s 0us/step INFO:tensorflow:Calling model_fn. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/optimizers/legacy/ftrl.py:173: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/model_fn.py:250: EstimatorSpec.__new__ (from tensorflow_estimator.python.estimator.model_fn) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:Done calling model_fn. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1416: NanTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1419: LoggingTensorHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/basic_session_run_hooks.py:232: SecondOrStepTimer.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1456: CheckpointSaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:Create CheckpointSaverHook. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:579: StepCounterHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:586: SummarySaverHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpbt9n791j/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1455: SessionRunArgs.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1454: SessionRunContext.__init__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/monitored_session.py:1474: SessionRunValues.__new__ (from tensorflow.python.training.session_run_hook) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:loss = 0.6931472, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 100... INFO:tensorflow:Saving checkpoints for 100 into /tmpfs/tmp/tmpbt9n791j/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 100... INFO:tensorflow:Loss for final step: 0.59910536. 2024-01-24 02:20:52.016569: 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. result = model.evaluate(train_input_fn, steps=10) for key, value in result.items(): print(key, ":", value) INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2024-01-24T02:20:52 WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/training/evaluation.py:260: FinalOpsHook.__init__ (from tensorflow.python.training.basic_session_run_hooks) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpbt9n791j/model.ckpt-100 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Evaluation [1/10] INFO:tensorflow:Evaluation [2/10] INFO:tensorflow:Evaluation [3/10] INFO:tensorflow:Evaluation [4/10] INFO:tensorflow:Evaluation [5/10] INFO:tensorflow:Evaluation [6/10] INFO:tensorflow:Evaluation [7/10] INFO:tensorflow:Evaluation [8/10] INFO:tensorflow:Evaluation [9/10] INFO:tensorflow:Evaluation [10/10] INFO:tensorflow:Inference Time : 3.02373s INFO:tensorflow:Finished evaluation at 2024-01-24-02:20:55 INFO:tensorflow:Saving dict for global step 100: accuracy = 0.715625, accuracy_baseline = 0.615625, auc = 0.7548389, auc_precision_recall = 0.66771877, average_loss = 0.56671846, global_step = 100, label/mean = 0.384375, loss = 0.56671846, precision = 0.7, prediction/mean = 0.37144834, recall = 0.45528457 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 100: /tmpfs/tmp/tmpbt9n791j/model.ckpt-100 accuracy : 0.715625 accuracy_baseline : 0.615625 auc : 0.7548389 auc_precision_recall : 0.66771877 average_loss : 0.56671846 label/mean : 0.384375 loss : 0.56671846 precision : 0.7 prediction/mean : 0.37144834 recall : 0.45528457 global_step : 100 2024-01-24 02:20:55.868009: 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. for pred in model.predict(train_input_fn): for key, value in pred.items(): print(key, ":", value) break INFO:tensorflow:Calling model_fn. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/base_head.py:786: ClassificationOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py:561: RegressionOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/head/binary_class_head.py:563: PredictOutput.__init__ (from tensorflow.python.saved_model.model_utils.export_output) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpbt9n791j/model.ckpt-100 INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. logits : [-0.6829183] logistic : [0.33561027] probabilities : [0.6643897 0.33561027] class_ids : [0] classes : [b'0'] all_class_ids : [0 1] all_classes : [b'0' b'1'] 2024-01-24 02:20:56.898963: 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. Iš anksto pagamintų vertintojų privalumai Iš anksto pagaminti vertintojai koduoja geriausią praktiką, suteikdami šiuos privalumus: Geriausia praktika nustatant, kur skirtingos skaičiavimo grafiko dalys turėtų vykti, įgyvendinant strategijas vienoje mašinoje arba grupėje. Geriausia praktika renginių (suskaitų) rašymui ir visuotinai naudingoms santraukoms. Jei nenaudojate iš anksto sukurtų įvertinimų, pirmiau nurodytas funkcijas turite įgyvendinti patys. Kūrybiniai įvertinimai Kiekvieno Estimatorio širdis – iš anksto pagaminta ar įprasta – yra jo , , kuris yra metodas, kuris kuria grafikus mokymui, vertinimui ir prognozavimui. Kai naudojate iš anksto sukurtą vertintoją, kažkas kitas jau įgyvendino modelio funkciją. Modelio funkcijos model_fn Pastaba: pritaikytas model_fn vis dar bus paleistas 1.x stiliaus grafiko režimu. Tai reiškia, kad nėra jokios aistringos vykdymo ir jokios automatinės kontrolės priklausomybės. Turėtumėte planuoti pereiti nuo tf.estimator su pritaikytu model_fn. Alternatyvūs API yra tf.keras ir tf.distribute. Jei vis dar reikia vertintojo tam tikroje jūsų mokymo dalyje, galite naudoti tf.keras.estimator.model_to_estimator konverterį, kad sukurtumėte vertintoją iš keras.Model. Įpročiai vis dar veikia 1.x stiliaus grafiko režimu. Tai reiškia, kad nėra aistringo vykdymo ir nėra automatinės kontrolės priklausomybės. Su įpročiu Alternatyvūs APIs yra ir Jei jums vis dar reikia Kai kurioms treniruočių programoms galite naudotis Konvertuoti sukurti Iš a . Note: model_fn tf.estimator model_fn tf.keras tf.distribute Estimator tf.keras.estimator.model_to_estimator Estimator keras.Model Sukurti skaičiuoklę iš Keras modelio Galite konvertuoti esamus Keras modelius į Estimator su Tai naudinga, jei norite modernizuoti savo modelio kodą, tačiau jūsų mokymo vamzdynas vis dar reikalauja vertintojų. tf.keras.estimator.model_to_estimator Instantijuokite „Keras MobileNet V2“ modelį ir sukurkite modelį su optimizatoriumi, nuostoliais ir metrika, kad galėtumėte mokytis: keras_mobilenet_v2 = tf.keras.applications.MobileNetV2( input_shape=(160, 160, 3), include_top=False) keras_mobilenet_v2.trainable = False estimator_model = tf.keras.Sequential([ keras_mobilenet_v2, tf.keras.layers.GlobalAveragePooling2D(), tf.keras.layers.Dense(1) ]) # Compile the model estimator_model.compile( optimizer='adam', loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), metrics=['accuracy']) Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_160_no_top.h5 9406464/9406464 [==============================] - 0s 0us/step Sukurkite iš surinkto Keras modelio. Pradinė Keras modelio modelio būklė išlieka sukurtoje : Estimator Estimator est_mobilenet_v2 = tf.keras.estimator.model_to_estimator(keras_model=estimator_model) INFO:tensorflow:Using default config. WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpjcucleiz 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/src/backend.py:452: 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( 2024-01-24 02:21:01.520653: W tensorflow/c/c_api.cc:305] Operation '{name:'block_16_project_BN/gamma/Assign' id:2164 op device:{requested: '', assigned: ''} def:{ { {node block_16_project_BN/gamma/Assign} } = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](block_16_project_BN/gamma, block_16_project_BN/gamma/Initializer/ones)} }' 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/tmpjcucleiz', '_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} INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjcucleiz', '_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} WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:2404: WarmStartSettings.__new__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:2404: WarmStartSettings.__new__ (from tensorflow_estimator.python.estimator.estimator) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. Apsvarstykite išvestinę Kaip ir su bet kuriuo kitu . Estimator Estimator IMG_SIZE = 160 # All images will be resized to 160x160 def preprocess(image, label): image = tf.cast(image, tf.float32) image = (image/127.5) - 1 image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE)) return image, label def train_input_fn(batch_size): data = tfds.load('cats_vs_dogs', as_supervised=True) train_data = data['train'] train_data = train_data.map(preprocess).shuffle(500).batch(batch_size) return train_data Norėdami traukinį, skambinkite Estimator traukinio funkcija: est_mobilenet_v2.train(input_fn=lambda: train_input_fn(32), steps=50) 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/tmpjcucleiz/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/tmpjcucleiz/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/tmpjcucleiz/keras/keras_model.ckpt INFO:tensorflow:Warm-starting from: /tmpfs/tmp/tmpjcucleiz/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 158 variables. INFO:tensorflow:Warm-started 158 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/tmpjcucleiz/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpjcucleiz/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:loss = 0.6796611, step = 0 INFO:tensorflow:loss = 0.6796611, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50... INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmpjcucleiz/model.ckpt. INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmpjcucleiz/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50... INFO:tensorflow:Loss for final step: 0.69532585. INFO:tensorflow:Loss for final step: 0.69532585. Panašiai, norėdami įvertinti, vadinkite vertintojo vertinimo funkciją: est_mobilenet_v2.evaluate(input_fn=lambda: train_input_fn(32), steps=10) INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/engine/training_v1.py:2335: 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:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:19 INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:19 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjcucleiz/model.ckpt-50 INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjcucleiz/model.ckpt-50 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 : 2.81526s INFO:tensorflow:Inference Time : 2.81526s INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:22 INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:22 INFO:tensorflow:Saving dict for global step 50: accuracy = 0.54375, global_step = 50, loss = 0.6558426 INFO:tensorflow:Saving dict for global step 50: accuracy = 0.54375, global_step = 50, loss = 0.6558426 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50: /tmpfs/tmp/tmpjcucleiz/model.ckpt-50 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 50: /tmpfs/tmp/tmpjcucleiz/model.ckpt-50 {'accuracy': 0.54375, 'loss': 0.6558426, 'global_step': 50} Norėdami gauti daugiau informacijos, prašome susipažinti su dokumentais apie . tf.keras.estimator.model_to_estimator Objektinių kontrolinių taškų išsaugojimas naudojant Estimator Pagal numatytuosius nustatymus vertintojai išsaugo kontrolinius taškus su kintamųjų pavadinimais, o ne objektų grafiku, aprašytu • skaityti pavadinimu pagrįstus kontrolinius taškus, tačiau kintamųjų pavadinimai gali pasikeisti, kai modelio dalys perkeliamos už Estimator Tolesniam suderinamumui taupant objektų pagrindu pagrįstus kontrolinius taškus lengviau mokyti modelį „Estimator“ viduje ir tada jį naudoti už jo ribų. Checkpoint vadovas tf.train.Checkpoint model_fn import tensorflow.compat.v1 as tf_compat def toy_dataset(): inputs = tf.range(10.)[:, None] labels = inputs * 5. + tf.range(5.)[None, :] return tf.data.Dataset.from_tensor_slices( dict(x=inputs, y=labels)).repeat().batch(2) class Net(tf.keras.Model): """A simple linear model.""" def __init__(self): super(Net, self).__init__() self.l1 = tf.keras.layers.Dense(5) def call(self, x): return self.l1(x) def model_fn(features, labels, mode): net = Net() opt = tf.keras.optimizers.Adam(0.1) ckpt = tf.train.Checkpoint(step=tf_compat.train.get_global_step(), optimizer=opt, net=net) with tf.GradientTape() as tape: output = net(features['x']) loss = tf.reduce_mean(tf.abs(output - features['y'])) variables = net.trainable_variables gradients = tape.gradient(loss, variables) return tf.estimator.EstimatorSpec( mode, loss=loss, train_op=tf.group(opt.apply_gradients(zip(gradients, variables)), ckpt.step.assign_add(1)), # Tell the Estimator to save "ckpt" in an object-based format. scaffold=tf_compat.train.Scaffold(saver=ckpt)) tf.keras.backend.clear_session() est = tf.estimator.Estimator(model_fn, './tf_estimator_example/') est.train(toy_dataset, steps=10) INFO:tensorflow:Using default config. INFO:tensorflow:Using default config. INFO:tensorflow:Using config: {'_model_dir': './tf_estimator_example/', '_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} INFO:tensorflow:Using config: {'_model_dir': './tf_estimator_example/', '_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} INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. 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 ./tf_estimator_example/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into ./tf_estimator_example/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:loss = 4.4765882, step = 1 INFO:tensorflow:loss = 4.4765882, step = 1 WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 4 vs previous value: 4. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize. WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 4 vs previous value: 4. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10... INFO:tensorflow:Saving checkpoints for 10 into ./tf_estimator_example/model.ckpt. INFO:tensorflow:Saving checkpoints for 10 into ./tf_estimator_example/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10... INFO:tensorflow:Loss for final step: 44.10201. INFO:tensorflow:Loss for final step: 44.10201. Tada galite įkelti Estimator kontrolinius taškus iš savo . tf.train.Checkpoint model_dir opt = tf.keras.optimizers.Adam(0.1) net = Net() ckpt = tf.train.Checkpoint( step=tf.Variable(1, dtype=tf.int64), optimizer=opt, net=net) ckpt.restore(tf.train.latest_checkpoint('./tf_estimator_example/')) ckpt.step.numpy() # From est.train(..., steps=10) 10 Išsaugoti modeliai iš vertintojų Vertintojai eksportuoja SavedModels per . tf.Estimator.export_saved_model input_column = tf.feature_column.numeric_column("x") estimator = tf.estimator.LinearClassifier(feature_columns=[input_column]) def input_fn(): return tf.data.Dataset.from_tensor_slices( ({"x": [1., 2., 3., 4.]}, [1, 1, 0, 0])).repeat(200).shuffle(64).batch(16) estimator.train(input_fn) INFO:tensorflow:Using default config. INFO:tensorflow:Using default config. WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmps_vspwiv WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmps_vspwiv INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmps_vspwiv', '_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} INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmps_vspwiv', '_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} INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. 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/tmps_vspwiv/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmps_vspwiv/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:loss = 0.6931472, step = 0 INFO:tensorflow:loss = 0.6931472, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50... INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmps_vspwiv/model.ckpt. INFO:tensorflow:Saving checkpoints for 50 into /tmpfs/tmp/tmps_vspwiv/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50... INFO:tensorflow:Loss for final step: 0.42848104. INFO:tensorflow:Loss for final step: 0.42848104. Norėdami išgelbėti Jums reikia sukurti a Ši funkcija sudaro dalį a Tai apibūdina žaliavos duomenis, kuriuos gavo SavedModel. Estimator serving_input_receiver tf.Graph Tų Modulis apima funkcijas, padedančias kurti šias . tf.estimator.export receivers Kitas kodas sukuria imtuvą, pagrįstą , kuris priima serializuotas Protokolo buferiai, kurie dažnai naudojami . feature_columns tf.Example TF aptarnavimas tmpdir = tempfile.mkdtemp() serving_input_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn( tf.feature_column.make_parse_example_spec([input_column])) estimator_base_path = os.path.join(tmpdir, 'from_estimator') estimator_path = estimator.export_saved_model(estimator_base_path, serving_input_fn) WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:4: make_parse_example_spec_v2 (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:4: make_parse_example_spec_v2 (from tensorflow.python.feature_column.feature_column_v2) is deprecated and will be removed in a future version. Instructions for updating: Use Keras preprocessing layers instead, either directly or via the `tf.keras.utils.FeatureSpace` utility. Each of `tf.feature_column.*` has a functional equivalent in `tf.keras.layers` for feature preprocessing when training a Keras model. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:3: build_parsing_serving_input_receiver_fn (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/121230551.py:3: build_parsing_serving_input_receiver_fn (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/export/export.py:312: ServingInputReceiver.__new__ (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/export/export.py:312: ServingInputReceiver.__new__ (from tensorflow_estimator.python.estimator.export.export) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:168: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:168: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/saved_model/model_utils/export_utils.py:83: get_tensor_from_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version. Instructions for updating: This API was designed for TensorFlow v1. See https://www.tensorflow.org/guide/migrate for instructions on how to migrate your code to TensorFlow v2. INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification'] INFO:tensorflow:Signatures INCLUDED in export for Classify: ['serving_default', 'classification'] INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression'] INFO:tensorflow:Signatures INCLUDED in export for Regress: ['regression'] INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict'] INFO:tensorflow:Signatures INCLUDED in export for Predict: ['predict'] INFO:tensorflow:Signatures INCLUDED in export for Train: None INFO:tensorflow:Signatures INCLUDED in export for Train: None INFO:tensorflow:Signatures INCLUDED in export for Eval: None INFO:tensorflow:Signatures INCLUDED in export for Eval: None INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmps_vspwiv/model.ckpt-50 INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmps_vspwiv/model.ckpt-50 INFO:tensorflow:Assets added to graph. INFO:tensorflow:Assets added to graph. INFO:tensorflow:No assets to write. INFO:tensorflow:No assets to write. INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tmpvsrxtilj/from_estimator/temp-1706062885/saved_model.pb INFO:tensorflow:SavedModel written to: /tmpfs/tmp/tmpvsrxtilj/from_estimator/temp-1706062885/saved_model.pb Taip pat galite įkelti ir paleisti šį modelį iš python: imported = tf.saved_model.load(estimator_path) def predict(x): example = tf.train.Example() example.features.feature["x"].float_list.value.extend([x]) return imported.signatures["predict"]( examples=tf.constant([example.SerializeToString()])) print(predict(1.5)) print(predict(3.5)) {'logistic': , 'probabilities': , 'class_ids': , 'logits': , 'classes': , 'all_class_ids': , 'all_classes': } {'logistic': , 'probabilities': , 'class_ids': , 'logits': , 'classes': , 'all_class_ids': , 'all_classes': } leidžia jums sukurti įvesties funkcijas, kurios imasi žaliųjų tensorių, o ne S į tf.estimator.export.build_raw_serving_input_receiver_fn tf.train.Example Naudoti su Estimator (riboto palaikymo) Tf.distribute.Strategija Tf.distribute.Strategija yra paskirstytas mokymo TensorFlow API, kuris iš pradžių palaikė async parametrų serverio požiūrį. Dabar palaiko Jei naudojate , galite pereiti į paskirstytą mokymą su labai nedaug pakeitimų savo kodą. Su šiuo, Estimator vartotojai dabar gali atlikti sinchroninį paskirstytą mokymą kelių GPU ir kelių darbuotojų, taip pat naudoti TPU. Žemiau esančiame skyriuje – daugiau detalių. tf.estimator tf.estimator tf.distribute.Strategy tf.estimator Kas yra palaikoma dabar Naudoti su Estimator šiek tiek skiriasi nuo Keras atveju. vietoj naudojant , dabar jūs perkeliate strategijos objektą į Dėl šio skaičiavimo. tf.distribute.Strategy strategy.scope RunConfig Galite kreiptis į Dėl daugiau informacijos. Paskirstytas mokymo vadovas Štai kodo fragmentas, kuris parodo tai su iš anksto paruoštu Estimatoriumi ir : LinearRegressor MirroredStrategy mirrored_strategy = tf.distribute.MirroredStrategy() config = tf.estimator.RunConfig( train_distribute=mirrored_strategy, eval_distribute=mirrored_strategy) regressor = tf.estimator.LinearRegressor( feature_columns=[tf.feature_column.numeric_column('feats')], optimizer='SGD', config=config) 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') 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') INFO:tensorflow:Initializing RunConfig with distribution strategies. INFO:tensorflow:Initializing RunConfig with distribution strategies. INFO:tensorflow:Not using Distribute Coordinator. INFO:tensorflow:Not using Distribute Coordinator. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4277059788.py:4: LinearRegressorV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/tmp/ipykernel_9073/4277059788.py:4: LinearRegressorV2.__init__ (from tensorflow_estimator.python.estimator.canned.linear) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:1344: RegressionHead.__init__ (from tensorflow_estimator.python.estimator.head.regression_head) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:1344: RegressionHead.__init__ (from tensorflow_estimator.python.estimator.head.regression_head) is deprecated and will be removed in a future version. Instructions for updating: Use tf.keras instead. WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpjagdxixr WARNING:tensorflow:Using temporary folder as model directory: /tmpfs/tmp/tmpjagdxixr INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjagdxixr', '_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': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_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, '_distribute_coordinator_mode': None} INFO:tensorflow:Using config: {'_model_dir': '/tmpfs/tmp/tmpjagdxixr', '_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': , '_device_fn': None, '_protocol': None, '_eval_distribute': , '_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, '_distribute_coordinator_mode': None} Čia naudojate iš anksto paruoštą vertintoją, tačiau tas pats kodas veikia ir su individualiu vertintoju. nustato, kaip mokymas bus paskirstytas, ir Tai dar vienas skirtumas nuo Keras, kur jūs naudojate tą pačią strategiją tiek mokymui, tiek eval. train_distribute eval_distribute Dabar galite mokyti ir vertinti šį vertintoją su įvesties funkcija: def input_fn(): dataset = tf.data.Dataset.from_tensors(({"feats":[1.]}, [1.])) return dataset.repeat(1000).batch(10) regressor.train(input_fn=input_fn, steps=10) regressor.evaluate(input_fn=input_fn, steps=10) WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1246: StrategyBase.configure (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version. Instructions for updating: use `update_config_proto` instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/estimator.py:1246: StrategyBase.configure (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version. Instructions for updating: use `update_config_proto` instead. /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/data/ops/dataset_ops.py:462: UserWarning: To make it possible to preserve tf.data options across serialization boundaries, their implementation has moved to be part of the TensorFlow graph. As a consequence, the options value is in general no longer known at graph construction time. Invoking this method in graph mode retains the legacy behavior of the original implementation, but note that the returned value might not reflect the actual value of the options. warnings.warn("To make it possible to preserve tf.data options across " INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Collective all_reduce tensors: 2 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1 INFO:tensorflow:Collective all_reduce tensors: 2 all_reduces, num_devices = 4, group_size = 4, implementation = CommunicationImplementation.NCCL, num_packs = 1 INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Create CheckpointSaverHook. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/util.py:95: DistributedIteratorV1.initialize (from tensorflow.python.distribute.v1.input_lib) is deprecated and will be removed in a future version. Instructions for updating: Use the iterator's `initializer` property instead. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow_estimator/python/estimator/util.py:95: DistributedIteratorV1.initialize (from tensorflow.python.distribute.v1.input_lib) is deprecated and will be removed in a future version. Instructions for updating: Use the iterator's `initializer` property instead. 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/tmpjagdxixr/model.ckpt. INFO:tensorflow:Saving checkpoints for 0 into /tmpfs/tmp/tmpjagdxixr/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... 2024-01-24 02:21:31.119820: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:31.121097: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:31.125859: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:31.126357: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:31.137091: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:31.137578: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:31.143860: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:31.144344: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' INFO:tensorflow:loss = 4.0, step = 0 INFO:tensorflow:loss = 4.0, step = 0 INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10... INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 10... INFO:tensorflow:Saving checkpoints for 10 into /tmpfs/tmp/tmpjagdxixr/model.ckpt. INFO:tensorflow:Saving checkpoints for 10 into /tmpfs/tmp/tmpjagdxixr/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10... INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 10... INFO:tensorflow:Loss for final step: 1.1510792e-12. INFO:tensorflow:Loss for final step: 1.1510792e-12. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Calling model_fn. INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Reduce to /replica:0/task:0/device:CPU:0 then broadcast to ('/replica:0/task:0/device:CPU:0',). INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:35 INFO:tensorflow:Starting evaluation at 2024-01-24T02:21:35 INFO:tensorflow:Graph was finalized. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjagdxixr/model.ckpt-10 INFO:tensorflow:Restoring parameters from /tmpfs/tmp/tmpjagdxixr/model.ckpt-10 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. 2024-01-24 02:21:36.170934: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:36.172154: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:36.176484: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:36.176957: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:36.182511: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:36.182980: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 2024-01-24 02:21:36.196737: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorFromStringHandle' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorFromStringHandle} } . Registered: device='CPU' 2024-01-24 02:21:36.197227: W tensorflow/core/grappler/utils/graph_view.cc:836] No registered 'MultiDeviceIteratorGetNextFromShard' OpKernel for GPU devices compatible with node { {node MultiDeviceIteratorGetNextFromShard} } . Registered: device='CPU' 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 : 1.83647s INFO:tensorflow:Inference Time : 1.83647s INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:37 INFO:tensorflow:Finished evaluation at 2024-01-24-02:21:37 INFO:tensorflow:Saving dict for global step 10: average_loss = 1.4210855e-14, global_step = 10, label/mean = 1.0, loss = 1.4210855e-14, prediction/mean = 0.99999994 INFO:tensorflow:Saving dict for global step 10: average_loss = 1.4210855e-14, global_step = 10, label/mean = 1.0, loss = 1.4210855e-14, prediction/mean = 0.99999994 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmpfs/tmp/tmpjagdxixr/model.ckpt-10 INFO:tensorflow:Saving 'checkpoint_path' summary for global step 10: /tmpfs/tmp/tmpjagdxixr/model.ckpt-10 {'average_loss': 1.4210855e-14, 'label/mean': 1.0, 'loss': 1.4210855e-14, 'prediction/mean': 0.99999994, 'global_step': 10} Kitas skirtumas, kurį reikėtų pabrėžti čia tarp Estimator ir Keras, yra įvesties tvarkymas. „Keras“ kiekvienoje duomenų rinkinio dalyje automatiškai pasiskirstoma per daugelį kopijų. „Estimator“, tačiau jūs nevykdote automatinio partijų padalijimo, taip pat automatiškai nesiskirstote duomenų per skirtingus darbuotojus. Turite visišką kontrolę, kaip norite, kad jūsų duomenys būtų paskirstyti darbuotojams ir įrenginiams, ir turite pateikti Nurodykite, kaip paskirstyti savo duomenis. input_fn Jūsų yra vadinamas vieną kartą už darbuotoją, taigi suteikiant vieną duomenų rinkinį už darbuotoją. Tada viena partija iš to duomenų rinkinio yra maitinama vienai kopijai ant to darbuotojo, tokiu būdu sunaudojant N partijas už N kopijas 1 darbuotojui. Reikėtų pasiūlyti batus dydžio Ir pasaulinis partijos dydis už žingsnį gali būti gautas kaip . input_fn input_fn PER_REPLICA_BATCH_SIZE PER_REPLICA_BATCH_SIZE * strategy.num_replicas_in_sync Atliekant daugialypį mokymą, turėtumėte arba suskirstyti savo duomenis tarp darbuotojų, arba kiekviename iš jų sumaišyti su atsitiktine sėkla. Tatuiruotės Daugiafunkcinis mokymas su Estimator Taip pat galite naudoti kelių darbuotojų ir parametrų serverio strategijas. ir nustatyti Aplinkos kintamieji kiekvienam binariui, veikiančiam jūsų grupėje. tf.estimator.train_and_evaluate TF_CONFIG Kas dabar palaikoma? Ribotas palaikymas mokymui su Estimator, naudojant visas strategijas, išskyrus Pagrindinis mokymas ir vertinimas turėtų veikti, tačiau yra keletas pažangių funkcijų, tokių kaip Taip pat gali būti keletas klaidų šioje integracijoje ir nėra planų aktyviai tobulinti šią paramą (pagrindinis dėmesys skiriamas "Keras" ir "Custom training loop" paramai). Vietoj to, su šiais vaikais. TPUStrategy v1.train.Scaffold tf.distribute Training API MirroredStrategy TPUStrategy MultiWorkerMirroredStrategy CentralStorageStrategy ParameterServerStrategy Estimator API Limited support Not supported Limited support Limited support Limited support Apskaičiavimas API Ribotas palaikymas Nepalaikė Ribotas palaikymas Ribotas palaikymas Ribotas palaikymas Pavyzdžiai ir tutoriai Štai keletas galutinių pavyzdžių, kurie parodo, kaip naudoti įvairias strategijas su Estimator: "Multi-worker Training with Estimator" pamoka rodo, kaip mokyti su keliais darbuotojais naudojant "MultiWorkerMirroredStrategy" MNIST duomenų rinkinyje. Daugiafunkcinis mokymas su paskirstymo strategijomis tensorflow/ekosistemoje naudojant Kubernetes šablonus prasideda Keras modeliu ir konvertuojamas į Estimator naudojant tf.keras.estimator.model_to_estimator API. Oficiali ResNet50 modelis, kuris gali būti mokomas naudojant arba MirroredStrategy arba MultiWorkerMirroredStrategy. Iš pradžių paskelbtas „TensorFlow“ svetainėje, šis straipsnis čia pasirodo pagal naują antraštę ir yra licencijuotas pagal CC BY 4.0. Iš pradžių paskelbtas „TensorFlow“ svetainėje, šis straipsnis čia pasirodo pagal naują antraštę ir yra licencijuotas pagal CC BY 4.0. „TensorFlow“ „TensorFlow“