مثال‌های مهاجرت: تخمین‌گرهای کنسرو شده

مشاهده در TensorFlow.org در Google Colab اجرا شود مشاهده منبع در GitHub دانلود دفترچه یادداشت

برآوردگرهای کنسرو شده (یا از پیش ساخته شده) به طور سنتی در TensorFlow 1 به عنوان راه‌های سریع و آسان برای آموزش مدل‌ها برای انواع موارد استفاده معمولی استفاده می‌شوند. TensorFlow 2 جایگزین های تقریبی ساده ای برای تعدادی از آنها از طریق مدل های Keras فراهم می کند. برای برآوردگرهای کنسرو شده ای که جایگزین های داخلی TensorFlow 2 ندارند، هنوز هم می توانید جایگزین خود را نسبتاً آسان بسازید.

این راهنما چند نمونه از معادل‌های مستقیم و جایگزین‌های سفارشی را نشان می‌دهد تا نشان دهد که چگونه مدل‌های مشتق شده از tf.estimator TensorFlow 1 را می‌توان به TF2 با Keras منتقل کرد.

یعنی، این راهنما شامل نمونه هایی برای مهاجرت است:

پیش‌روی رایج برای آموزش یک مدل، پیش پردازش ویژگی است که برای مدل‌های تخمینگر TensorFlow 1 با tf.feature_column انجام می‌شود. برای اطلاعات بیشتر در مورد پیش پردازش ویژگی در TensorFlow 2، به این راهنمای مهاجرت ستون های ویژگی مراجعه کنید.

برپایی

با چند وارد کردن ضروری TensorFlow شروع کنید،

pip install tensorflow_decision_forests

import keras
import pandas as pd
import tensorflow as tf
import tensorflow.compat.v1 as tf1
import tensorflow_decision_forests as tfdf

WARNING:root:TF Parameter Server distributed training not available (this is expected for the pre-build release).

چند داده ساده را برای نمایش از مجموعه داده استاندارد تایتانیک آماده کنید،

x_train = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/train.csv')
x_eval = pd.read_csv('https://storage.googleapis.com/tf-datasets/titanic/eval.csv')
x_train['sex'].replace(('male', 'female'), (0, 1), inplace=True)
x_eval['sex'].replace(('male', 'female'), (0, 1), inplace=True)

x_train['alone'].replace(('n', 'y'), (0, 1), inplace=True)
x_eval['alone'].replace(('n', 'y'), (0, 1), inplace=True)

x_train['class'].replace(('First', 'Second', 'Third'), (1, 2, 3), inplace=True)
x_eval['class'].replace(('First', 'Second', 'Third'), (1, 2, 3), inplace=True)

x_train.drop(['embark_town', 'deck'], axis=1, inplace=True)
x_eval.drop(['embark_town', 'deck'], axis=1, inplace=True)

y_train = x_train.pop('survived')
y_eval = x_eval.pop('survived')

# Data setup for TensorFlow 1 with `tf.estimator`
def _input_fn():
  return tf1.data.Dataset.from_tensor_slices((dict(x_train), y_train)).batch(32)


def _eval_input_fn():
  return tf1.data.Dataset.from_tensor_slices((dict(x_eval), y_eval)).batch(32)


FEATURE_NAMES = [
    'age', 'fare', 'sex', 'n_siblings_spouses', 'parch', 'class', 'alone'
]

feature_columns = []
for fn in FEATURE_NAMES:
  feat_col = tf1.feature_column.numeric_column(fn, dtype=tf.float32)
  feature_columns.append(feat_col)

و روشی برای نمونه سازی یک بهینه ساز نمونه ساده برای استفاده با مدل های مختلف TensorFlow 1 Estimator و TensorFlow 2 Keras ایجاد کنید.

def create_sample_optimizer(tf_version):
  if tf_version == 'tf1':
    optimizer = lambda: tf.keras.optimizers.Ftrl(
        l1_regularization_strength=0.001,
        learning_rate=tf1.train.exponential_decay(
            learning_rate=0.1,
            global_step=tf1.train.get_global_step(),
            decay_steps=10000,
            decay_rate=0.9))
  elif tf_version == 'tf2':
    optimizer = tf.keras.optimizers.Ftrl(
        l1_regularization_strength=0.001,
        learning_rate=tf.keras.optimizers.schedules.ExponentialDecay(
            initial_learning_rate=0.1, decay_steps=10000, decay_rate=0.9))
  return optimizer

مثال 1: مهاجرت از LinearEstimator

TF1: استفاده از LinearEstimator

در TensorFlow 1، می توانید از tf.estimator.LinearEstimator برای ایجاد یک مدل خطی پایه برای مشکلات رگرسیون و طبقه بندی استفاده کنید.

linear_estimator = tf.estimator.LinearEstimator(
    head=tf.estimator.BinaryClassHead(),
    feature_columns=feature_columns,
    optimizer=create_sample_optimizer('tf1'))

INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpvoycvffz
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpvoycvffz
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpvoycvffz', '_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/tmpvoycvffz', '_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}

linear_estimator.train(input_fn=_input_fn, steps=100)
linear_estimator.evaluate(input_fn=_eval_input_fn, steps=10)

WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow/python/training/training_util.py:401: 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:401: 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.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:1478: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.
  getter=tf.compat.v1.get_variable)
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.7/site-packages/keras/optimizer_v2/ftrl.py:149: 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/python3.7/site-packages/keras/optimizer_v2/ftrl.py:149: 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
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 /tmp/tmpvoycvffz/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpvoycvffz/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 20...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20...
INFO:tensorflow:Saving checkpoints for 20 into /tmp/tmpvoycvffz/model.ckpt.
INFO:tensorflow:Saving checkpoints for 20 into /tmp/tmpvoycvffz/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20...
INFO:tensorflow:Loss for final step: 0.55268794.
INFO:tensorflow:Loss for final step: 0.55268794.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2022-01-29T02:21:45
INFO:tensorflow:Starting evaluation at 2022-01-29T02:21:45
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpvoycvffz/model.ckpt-20
INFO:tensorflow:Restoring parameters from /tmp/tmpvoycvffz/model.ckpt-20
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]
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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:Inference Time : 0.50224s
INFO:tensorflow:Inference Time : 0.50224s
INFO:tensorflow:Finished evaluation at 2022-01-29-02:21:45
INFO:tensorflow:Finished evaluation at 2022-01-29-02:21:45
INFO:tensorflow:Saving dict for global step 20: accuracy = 0.70075756, accuracy_baseline = 0.625, auc = 0.75472915, auc_precision_recall = 0.65362054, average_loss = 0.5759378, global_step = 20, label/mean = 0.375, loss = 0.5704812, precision = 0.6388889, prediction/mean = 0.41331062, recall = 0.46464646
INFO:tensorflow:Saving dict for global step 20: accuracy = 0.70075756, accuracy_baseline = 0.625, auc = 0.75472915, auc_precision_recall = 0.65362054, average_loss = 0.5759378, global_step = 20, label/mean = 0.375, loss = 0.5704812, precision = 0.6388889, prediction/mean = 0.41331062, recall = 0.46464646
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20: /tmp/tmpvoycvffz/model.ckpt-20
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20: /tmp/tmpvoycvffz/model.ckpt-20
{'accuracy': 0.70075756,
 'accuracy_baseline': 0.625,
 'auc': 0.75472915,
 'auc_precision_recall': 0.65362054,
 'average_loss': 0.5759378,
 'label/mean': 0.375,
 'loss': 0.5704812,
 'precision': 0.6388889,
 'prediction/mean': 0.41331062,
 'recall': 0.46464646,
 'global_step': 20}

TF2: استفاده از Keras LinearModel

در TensorFlow 2، می توانید نمونه ای از Keras tf.compat.v1.keras.models.LinearModel ایجاد کنید که جایگزین tf.estimator.LinearEstimator است. مسیر tf.compat.v1.keras برای نشان دادن اینکه مدل از پیش ساخته شده برای سازگاری وجود دارد استفاده می شود.

linear_model = tf.compat.v1.keras.experimental.LinearModel()
linear_model.compile(loss='mse', optimizer=create_sample_optimizer('tf2'), metrics=['accuracy'])
linear_model.fit(x_train, y_train, epochs=10)
linear_model.evaluate(x_eval, y_eval, return_dict=True)

Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 2.8157 - accuracy: 0.6300
Epoch 2/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2758 - accuracy: 0.6427
Epoch 3/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2470 - accuracy: 0.6699
Epoch 4/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1954 - accuracy: 0.7177
Epoch 5/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1931 - accuracy: 0.7145
Epoch 6/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1816 - accuracy: 0.7496
Epoch 7/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1766 - accuracy: 0.7751
Epoch 8/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2198 - accuracy: 0.7560
Epoch 9/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1657 - accuracy: 0.7959
Epoch 10/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1738 - accuracy: 0.7959
9/9 [==============================] - 0s 2ms/step - loss: 0.2278 - accuracy: 0.6780
{'loss': 0.22778697311878204, 'accuracy': 0.6780303120613098}

مثال 2: مهاجرت از DNNEstimator

TF1: با استفاده از DNNEstimator

در TensorFlow 1، می توانید از tf.estimator.DNNEstimator برای ایجاد یک مدل DNN پایه برای مشکلات رگرسیون و طبقه بندی استفاده کنید.

dnn_estimator = tf.estimator.DNNEstimator(
    head=tf.estimator.BinaryClassHead(),
    feature_columns=feature_columns,
    hidden_units=[128],
    activation_fn=tf.nn.relu,
    optimizer=create_sample_optimizer('tf1'))

INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmphckb8f81
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmphckb8f81
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmphckb8f81', '_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/tmphckb8f81', '_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}

dnn_estimator.train(input_fn=_input_fn, steps=100)
dnn_estimator.evaluate(input_fn=_eval_input_fn, steps=10)

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 /tmp/tmphckb8f81/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmphckb8f81/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 2.1811047, step = 0
INFO:tensorflow:loss = 2.1811047, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20...
INFO:tensorflow:Saving checkpoints for 20 into /tmp/tmphckb8f81/model.ckpt.
INFO:tensorflow:Saving checkpoints for 20 into /tmp/tmphckb8f81/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20...
INFO:tensorflow:Loss for final step: 0.5881681.
INFO:tensorflow:Loss for final step: 0.5881681.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2022-01-29T02:21:48
INFO:tensorflow:Starting evaluation at 2022-01-29T02:21:48
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmphckb8f81/model.ckpt-20
INFO:tensorflow:Restoring parameters from /tmp/tmphckb8f81/model.ckpt-20
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]
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INFO:tensorflow:Evaluation [5/10]
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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:Inference Time : 0.47075s
INFO:tensorflow:Inference Time : 0.47075s
INFO:tensorflow:Finished evaluation at 2022-01-29-02:21:49
INFO:tensorflow:Finished evaluation at 2022-01-29-02:21:49
INFO:tensorflow:Saving dict for global step 20: accuracy = 0.7083333, accuracy_baseline = 0.625, auc = 0.70716256, auc_precision_recall = 0.6146256, average_loss = 0.60399944, global_step = 20, label/mean = 0.375, loss = 0.5986442, precision = 0.6486486, prediction/mean = 0.41256863, recall = 0.4848485
INFO:tensorflow:Saving dict for global step 20: accuracy = 0.7083333, accuracy_baseline = 0.625, auc = 0.70716256, auc_precision_recall = 0.6146256, average_loss = 0.60399944, global_step = 20, label/mean = 0.375, loss = 0.5986442, precision = 0.6486486, prediction/mean = 0.41256863, recall = 0.4848485
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20: /tmp/tmphckb8f81/model.ckpt-20
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20: /tmp/tmphckb8f81/model.ckpt-20
{'accuracy': 0.7083333,
 'accuracy_baseline': 0.625,
 'auc': 0.70716256,
 'auc_precision_recall': 0.6146256,
 'average_loss': 0.60399944,
 'label/mean': 0.375,
 'loss': 0.5986442,
 'precision': 0.6486486,
 'prediction/mean': 0.41256863,
 'recall': 0.4848485,
 'global_step': 20}

TF2: استفاده از Keras برای ایجاد یک مدل DNN سفارشی

در TensorFlow 2، می توانید یک مدل DNN سفارشی ایجاد کنید تا جایگزین مدل تولید شده توسط tf.estimator.DNNEstimator ، با سطوح مشابهی از سفارشی سازی مشخص شده توسط کاربر (به عنوان مثال، مانند مثال قبلی، توانایی سفارشی کردن بهینه ساز مدل انتخابی) .

یک گردش کار مشابه را می توان برای جایگزینی tf.estimator.experimental.RNNEstimator با مدل Keras RNN استفاده کرد. Keras تعدادی انتخاب داخلی و قابل تنظیم را از طریق tf.keras.layers.RNN ، tf.keras.layers.LSTM و tf.keras.layers.GRU - برای جزئیات بیشتر اینجا را ببینید.

dnn_model = tf.keras.models.Sequential(
    [tf.keras.layers.Dense(128, activation='relu'),
     tf.keras.layers.Dense(1)])

dnn_model.compile(loss='mse', optimizer=create_sample_optimizer('tf2'), metrics=['accuracy'])

dnn_model.fit(x_train, y_train, epochs=10)
dnn_model.evaluate(x_eval, y_eval, return_dict=True)

Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 551.2993 - accuracy: 0.5997
Epoch 2/10
20/20 [==============================] - 0s 2ms/step - loss: 16.8562 - accuracy: 0.6427
Epoch 3/10
20/20 [==============================] - 0s 2ms/step - loss: 0.3048 - accuracy: 0.7161
Epoch 4/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2475 - accuracy: 0.7416
Epoch 5/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2334 - accuracy: 0.7512
Epoch 6/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2200 - accuracy: 0.7416
Epoch 7/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2012 - accuracy: 0.7656
Epoch 8/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2025 - accuracy: 0.7624
Epoch 9/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2185 - accuracy: 0.7703
Epoch 10/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2046 - accuracy: 0.7687
9/9 [==============================] - 0s 2ms/step - loss: 0.2227 - accuracy: 0.6856
{'loss': 0.2227054387331009, 'accuracy': 0.685606062412262}

مثال 3: مهاجرت از DNNLinearCombinedEstimator

TF1: استفاده از DNNLinearCombinedEstimator

در TensorFlow 1، می توانید از tf.estimator.DNNLinearCombinedEstimator برای ایجاد یک مدل ترکیبی پایه برای مشکلات رگرسیون و طبقه بندی با ظرفیت سفارشی سازی برای اجزای خطی و DNN آن استفاده کنید.

optimizer = create_sample_optimizer('tf1')

combined_estimator = tf.estimator.DNNLinearCombinedEstimator(
    head=tf.estimator.BinaryClassHead(),
    # Wide settings
    linear_feature_columns=feature_columns,
    linear_optimizer=optimizer,
    # Deep settings
    dnn_feature_columns=feature_columns,
    dnn_hidden_units=[128],
    dnn_optimizer=optimizer)

INFO:tensorflow:Using default config.
INFO:tensorflow:Using default config.
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpwl5e5eaq
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpwl5e5eaq
INFO:tensorflow:Using config: {'_model_dir': '/tmp/tmpwl5e5eaq', '_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/tmpwl5e5eaq', '_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}

combined_estimator.train(input_fn=_input_fn, steps=100)
combined_estimator.evaluate(input_fn=_eval_input_fn, steps=10)

INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/linear.py:1478: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.
  getter=tf.compat.v1.get_variable)
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 /tmp/tmpwl5e5eaq/model.ckpt.
INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpwl5e5eaq/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...
INFO:tensorflow:loss = 2.5475807, step = 0
INFO:tensorflow:loss = 2.5475807, step = 0
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20...
INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 20...
INFO:tensorflow:Saving checkpoints for 20 into /tmp/tmpwl5e5eaq/model.ckpt.
INFO:tensorflow:Saving checkpoints for 20 into /tmp/tmpwl5e5eaq/model.ckpt.
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20...
INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 20...
INFO:tensorflow:Loss for final step: 0.58060575.
INFO:tensorflow:Loss for final step: 0.58060575.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Starting evaluation at 2022-01-29T02:21:53
INFO:tensorflow:Starting evaluation at 2022-01-29T02:21:53
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Restoring parameters from /tmp/tmpwl5e5eaq/model.ckpt-20
INFO:tensorflow:Restoring parameters from /tmp/tmpwl5e5eaq/model.ckpt-20
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:Inference Time : 0.54029s
INFO:tensorflow:Inference Time : 0.54029s
INFO:tensorflow:Finished evaluation at 2022-01-29-02:21:53
INFO:tensorflow:Finished evaluation at 2022-01-29-02:21:53
INFO:tensorflow:Saving dict for global step 20: accuracy = 0.6931818, accuracy_baseline = 0.625, auc = 0.73532283, auc_precision_recall = 0.630229, average_loss = 0.65179086, global_step = 20, label/mean = 0.375, loss = 0.63768697, precision = 0.60714287, prediction/mean = 0.4162652, recall = 0.5151515
INFO:tensorflow:Saving dict for global step 20: accuracy = 0.6931818, accuracy_baseline = 0.625, auc = 0.73532283, auc_precision_recall = 0.630229, average_loss = 0.65179086, global_step = 20, label/mean = 0.375, loss = 0.63768697, precision = 0.60714287, prediction/mean = 0.4162652, recall = 0.5151515
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20: /tmp/tmpwl5e5eaq/model.ckpt-20
INFO:tensorflow:Saving 'checkpoint_path' summary for global step 20: /tmp/tmpwl5e5eaq/model.ckpt-20
{'accuracy': 0.6931818,
 'accuracy_baseline': 0.625,
 'auc': 0.73532283,
 'auc_precision_recall': 0.630229,
 'average_loss': 0.65179086,
 'label/mean': 0.375,
 'loss': 0.63768697,
 'precision': 0.60714287,
 'prediction/mean': 0.4162652,
 'recall': 0.5151515,
 'global_step': 20}

TF2: استفاده از Keras WideDeepModel

در TensorFlow 2، می‌توانید نمونه‌ای از Keras tf.compat.v1.keras.models.WideDeepModel را ایجاد کنید تا جایگزین نمونه تولید شده توسط tf.estimator.DNNLinearCombinedEstimator ، با سطوح مشابهی از سفارشی‌سازی مشخص شده توسط کاربر (به عنوان مثال، مانند مثال قبلی، توانایی سفارشی سازی بهینه ساز مدل انتخابی).

این WideDeepModel بر اساس یک LinearModel تشکیل دهنده و یک مدل DNN سفارشی ساخته شده است، که هر دو در دو مثال قبل مورد بحث قرار گرفتند. در صورت تمایل می توان از یک مدل خطی سفارشی نیز به جای Keras LinearModel استفاده کرد.

اگر می‌خواهید به جای تخمین‌گر کنسرو شده مدل خود را بسازید، نحوه ساخت keras.Sequential را بررسی کنید. مدل ترتیبی . برای اطلاعات بیشتر در مورد آموزش های سفارشی و بهینه سازها، می توانید این راهنما را نیز بررسی کنید.

# Create LinearModel and DNN Model as in Examples 1 and 2
optimizer = create_sample_optimizer('tf2')

linear_model = tf.compat.v1.keras.experimental.LinearModel()
linear_model.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])
linear_model.fit(x_train, y_train, epochs=10, verbose=0)

dnn_model = tf.keras.models.Sequential(
    [tf.keras.layers.Dense(128, activation='relu'),
     tf.keras.layers.Dense(1)])
dnn_model.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])

combined_model = tf.compat.v1.keras.experimental.WideDeepModel(linear_model,
                                                               dnn_model)
combined_model.compile(
    optimizer=[optimizer, optimizer], loss='mse', metrics=['accuracy'])
combined_model.fit([x_train, x_train], y_train, epochs=10)
combined_model.evaluate(x_eval, y_eval, return_dict=True)

Epoch 1/10
20/20 [==============================] - 0s 2ms/step - loss: 1118.0448 - accuracy: 0.6715
Epoch 2/10
20/20 [==============================] - 0s 2ms/step - loss: 0.5682 - accuracy: 0.7305
Epoch 3/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2719 - accuracy: 0.7671
Epoch 4/10
20/20 [==============================] - 0s 2ms/step - loss: 0.2032 - accuracy: 0.7831
Epoch 5/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1911 - accuracy: 0.7783
Epoch 6/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1895 - accuracy: 0.7863
Epoch 7/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1882 - accuracy: 0.7863
Epoch 8/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1717 - accuracy: 0.7974
Epoch 9/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1701 - accuracy: 0.7927
Epoch 10/10
20/20 [==============================] - 0s 2ms/step - loss: 0.1684 - accuracy: 0.7990
9/9 [==============================] - 0s 2ms/step - loss: 0.1930 - accuracy: 0.7424
{'loss': 0.19299836456775665, 'accuracy': 0.7424242496490479}

مثال 4: مهاجرت از BoostedTreesEstimator

TF1: استفاده از BoostedTreesEstimator

در TensorFlow 1، می توانید از tf.estimator.BoostedTreesEstimator برای ایجاد یک خط مبنا برای ایجاد یک مدل تقویت گرادیان پایه با استفاده از مجموعه ای از درختان تصمیم برای مشکلات رگرسیون و طبقه بندی استفاده کنید. این قابلیت دیگر در TensorFlow 2 وجود ندارد.

bt_estimator = tf1.estimator.BoostedTreesEstimator(
    head=tf.estimator.BinaryClassHead(),
    n_batches_per_layer=1,
    max_depth=10,
    n_trees=1000,
    feature_columns=feature_columns)

bt_estimator.train(input_fn=_input_fn, steps=1000)
bt_estimator.evaluate(input_fn=_eval_input_fn, steps=100)

TF2: استفاده از جنگل‌های تصمیم‌گیری TensorFlow

در TensorFlow 2، نزدیکترین جایگزین از پیش بسته بندی شده برای یک مدل تولید شده توسط tf.estimator.BoostedTreesEstimator ، مدلی است که با استفاده از tfdf.keras.GradientBoostedTreesModel ایجاد شده است، که دنباله ای از درخت های تصمیم کم عمق آموزش داده شده را ایجاد می کند، که هر کدام از errorslear طراحی شده اند. ساخته شده توسط پیشینیان خود در سکانس.

GradientBoostedTreesModel گزینه‌های بیشتری را برای سفارشی‌سازی فراهم می‌کند، که به شما امکان می‌دهد همه چیز از محدودیت‌های عمقی اولیه تا شرایط توقف اولیه را مشخص کنید. برای جزئیات بیشتر ویژگی GradientBoostedTreesModel اینجا را ببینید.

gbt_model = tfdf.keras.GradientBoostedTreesModel(
    task=tfdf.keras.Task.CLASSIFICATION)
gbt_model.compile(metrics=['mse', 'accuracy'])

Use /tmp/tmpbr1acn2_ as temporary training directory

train_df, eval_df = x_train.copy(), x_eval.copy()
train_df['survived'], eval_df['survived'] = y_train, y_eval

train_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label='survived')
eval_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(eval_df, label='survived')

gbt_model.fit(train_dataset)
gbt_model.evaluate(eval_dataset, return_dict=True)

Starting reading the dataset
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/tensorflow_decision_forests/keras/core.py:2036: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only
  features_dataframe = dataframe.drop(label, 1)
1/1 [==============================] - ETA: 0s
Dataset read in 0:00:03.161776
Training model
Model trained in 0:00:00.102649
Compiling model
1/1 [==============================] - 3s 3s/step
[INFO kernel.cc:1153] Loading model from path
[INFO abstract_model.cc:1063] Engine "GradientBoostedTreesQuickScorerExtended" built
[INFO kernel.cc:1001] Use fast generic engine
WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f95e9db4e60> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f95e9db4e60> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
WARNING: AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f95e9db4e60> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
1/1 [==============================] - 0s 388ms/step - loss: 0.0000e+00 - mse: 0.1308 - accuracy: 0.8144
{'loss': 0.0, 'mse': 0.13076548278331757, 'accuracy': 0.814393937587738}

در TensorFlow 2، جایگزین TFDF دیگری نیز برای مدل تولید شده توسط tf.estimator.BoostedTreesEstimator - tfdf.keras.RandomForestModel دارد. RandomForestModel یک یادگیر قوی و مقاوم در برابر بیش از حد برازش ایجاد می کند که از جمعیتی از درختان تصمیم گیری عمیق تشکیل شده است که هر کدام بر روی زیرمجموعه های تصادفی از مجموعه داده های آموزشی ورودی آموزش دیده اند.

RandomForestModel و GradientBoostedTreesModel به طور مشابه سطوح گسترده ای از سفارشی سازی را ارائه می دهند. انتخاب بین آنها به مشکل خاص بستگی دارد و به وظیفه یا برنامه شما بستگی دارد.

برای اطلاعات بیشتر در مورد ویژگی RandomForestModel و GradientBoostedTreesModel ، اسناد API را بررسی کنید.

rf_model = tfdf.keras.RandomForestModel(
    task=tfdf.keras.Task.CLASSIFICATION)
rf_model.compile(metrics=['mse', 'accuracy'])

Use /tmp/tmpluh2ebcj as temporary training directory

rf_model.fit(train_dataset)
rf_model.evaluate(eval_dataset, return_dict=True)

Starting reading the dataset
1/1 [==============================] - ETA: 0s
Dataset read in 0:00:00.094262
Training model
Model trained in 0:00:00.083656
Compiling model
1/1 [==============================] - 0s 260ms/step
[INFO kernel.cc:1153] Loading model from path
[INFO kernel.cc:1001] Use fast generic engine
1/1 [==============================] - 0s 123ms/step - loss: 0.0000e+00 - mse: 0.1270 - accuracy: 0.8636
{'loss': 0.0, 'mse': 0.12698587775230408, 'accuracy': 0.8636363744735718}