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بررسی اجمالی
برآوردگرهای TensorFlow در TensorFlow پشتیبانی می‌شوند و می‌توانند از مدل‌های جدید و موجود tf.keras شوند. این آموزش شامل یک مثال کامل و حداقلی از آن فرآیند است.
برپایی
import tensorflow as tf
import numpy as np
import tensorflow_datasets as tfds
یک مدل Keras ساده بسازید.
در Keras، شما لایه ها را برای ساختن مدل ها مونتاژ می کنید. یک مدل (معمولا) نموداری از لایه ها است. متداول ترین نوع مدل پشته ای از لایه ها است: مدل tf.keras.Sequential .
برای ساخت یک شبکه ساده و کاملا متصل (یعنی پرسپترون چند لایه):
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)
])
مدل را کامپایل کنید و خلاصه بگیرید.
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
_________________________________________________________________
یک تابع ورودی ایجاد کنید
از Datasets API برای مقیاس بندی به مجموعه داده های بزرگ یا آموزش چند دستگاهی استفاده کنید.
برآوردگرها نیاز به کنترل زمان و نحوه ساخت خط لوله ورودی آنها دارند. برای اجازه دادن به این کار، آنها به یک "عملکرد ورودی" یا input_fn . Estimator این تابع را بدون آرگومان فراخوانی می کند. input_fn باید یک tf.data.Dataset .
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 خود را تست کنید
for features_batch, labels_batch in input_fn().take(1):
print(features_batch)
print(labels_batch)
{'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)
یک برآوردگر از مدل tf.keras ایجاد کنید.
یک tf.keras.Model را می توان با tf.estimator API با تبدیل مدل به یک شی tf.estimator.Estimator با tf.keras.estimator.model_to_estimator .
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.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/backend.py:450: 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('`tf.keras.backend.set_learning_phase` is deprecated and '
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmp2jzrjbqb', '_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': '/tmp/tmp2jzrjbqb', '_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}
برآوردگر را آموزش دهید و ارزیابی کنید.
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/python3.7/site-packages/tensorflow/python/training/training_util.py:397: 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/python3.7/site-packages/tensorflow/python/training/training_util.py:397: 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='/tmp/tmp2jzrjbqb/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='/tmp/tmp2jzrjbqb/keras/keras_model.ckpt', vars_to_warm_start='.*', var_name_to_vocab_info={}, var_name_to_prev_var_name={})
INFO:tensorflow:Warm-starting from: /tmp/tmp2jzrjbqb/keras/keras_model.ckpt
INFO:tensorflow:Warm-starting from: /tmp/tmp2jzrjbqb/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 /tmp/tmp2jzrjbqb/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmp2jzrjbqb/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 3.2731433, step = 0
INFO:tensorflow:loss = 3.2731433, step = 0
INFO:tensorflow:global_step/sec: 19.6463
INFO:tensorflow:global_step/sec: 19.6463
INFO:tensorflow:loss = 1.012466, step = 100 (5.092 sec)
INFO:tensorflow:loss = 1.012466, step = 100 (5.092 sec)
INFO:tensorflow:global_step/sec: 19.705
INFO:tensorflow:global_step/sec: 19.705
INFO:tensorflow:loss = 0.9225232, step = 200 (5.075 sec)
INFO:tensorflow:loss = 0.9225232, step = 200 (5.075 sec)
INFO:tensorflow:global_step/sec: 19.9236
INFO:tensorflow:global_step/sec: 19.9236
INFO:tensorflow:loss = 0.8686823, step = 300 (5.019 sec)
INFO:tensorflow:loss = 0.8686823, step = 300 (5.019 sec)
INFO:tensorflow:global_step/sec: 19.8862
INFO:tensorflow:global_step/sec: 19.8862
INFO:tensorflow:loss = 0.6412657, step = 400 (5.029 sec)
INFO:tensorflow:loss = 0.6412657, step = 400 (5.029 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 /tmp/tmp2jzrjbqb/model.ckpt.
INFO:tensorflow:Saving checkpoints for 500 into /tmp/tmp2jzrjbqb/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: 0.65391386.
INFO:tensorflow:Loss for final step: 0.65391386.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/engine/training_v1.py:2057: 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 2022-01-26T06:39:31
INFO:tensorflow:Starting evaluation at 2022-01-26T06:39:31
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmp2jzrjbqb/model.ckpt-500
INFO:tensorflow:Restoring parameters from /tmp/tmp2jzrjbqb/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 : 0.63967s
INFO:tensorflow:Inference Time : 0.63967s
INFO:tensorflow:Finished evaluation at 2022-01-26-06:39:31
INFO:tensorflow:Finished evaluation at 2022-01-26-06:39:31
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.6503415
INFO:tensorflow:Saving dict for global step 500: global_step = 500, loss = 0.6503415
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp2jzrjbqb/model.ckpt-500
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 500: /tmp/tmp2jzrjbqb/model.ckpt-500
Eval result: {'loss': 0.6503415, 'global_step': 500}