1. 简介
- tf.estimator是tensorflow一种高级API，封装了训练、评估、预测、导出以供使用所有的相关流程；tf.estimator另外一个很大的优势其可以很简单的实现分布式训练（比如ps），在cpu, gpu或者tpu上运行而无需修改代码。并且tf.estimator提供了模型保存，tensorboard可视化等基本功能。
- 在用 Estimator 编写应用时，您必须将数据输入流水线与模型分离。这种分离简化了使用不同数据集进行实验。tf.estimator定义好了训练、评估、预测的基本框架，开发者只需要关注模型结构的定义model_fn，输入数据的获取input_fn；
- tf.estimator有预定义好的，用于分类或者回归任务的Estimator：
- tf.estimator.DNNRegressor: for deep models that perform multi-class regression.
- tf.estimator.DNNClassifier: for deep models that perform multi-class classification.
- tf.estimator.DNNLinearCombinedRegressor: for wide & deep models.
- tf.estimator.DNNLinearCombinedClassifier: for wide & deep models.
- tf.estimator.LinearRegressor: for regressors based on linear models.
- tf.estimator.LinearClassifier: for classifiers based on linear models.
2. 预定义Estimator
2.1 mnist分类问题
#coding:utf-8
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data
# 设置日志级别
tf.logging.set_verbosity(tf.logging.INFO)
# 读取数据
mnist = input_data.read_data_sets('MNIST_data')
def input(dataset):
return dataset.images, dataset.labels.astype(np.int32)
# Specify feature
feature_columns = [tf.feature_column.numeric_column("x", shape=[28, 28])]
# Build 2 layer DNN classifier
classifier = tf.estimator.DNNClassifier(
feature_columns=feature_columns,
hidden_units=[256, 32],
optimizer=tf.train.AdamOptimizer(1e-4),
n_classes=10,
dropout=0.1,
model_dir="./tmp/mnist_model"
)
# Define the training inputs
train_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": input(mnist.train)[0]},
y=input(mnist.train)[1],
num_epochs=None,
batch_size=50,
shuffle=True
)
classifier.train(input_fn=train_input_fn, steps=100000)
# Define the test inputs
test_input_fn = tf.estimator.inputs.numpy_input_fn(
x={"x": input(mnist.test)[0]},
y=input(mnist.test)[1],
num_epochs=1,
shuffle=False
)
# Evaluate accuracy
accuracy_score = classifier.evaluate(input_fn=test_input_fn)["accuracy"]
print("\nTest Accuracy: {0:f}%\n".format(accuracy_score*100))
2.2 wide & deep模型
（1）首先需要自定义 feature_columns；
（2）调用自定义tf.estimator.LinearClassifier, tf.estimator.DNNClassifier, tf.estimator.DNNLinearCombinedClassifier；
（3）自定义input_fn获取输入数据；
（4）开始训练、评估、测试；
def build_estimator(model_dir, model_type):
"""Build an estimator."""
if model_type == "wide":
m = tf.estimator.LinearClassifier(
model_dir=model_dir, feature_columns=base_columns + crossed_columns)
elif model_type == "deep":
m = tf.estimator.DNNClassifier(
model_dir=model_dir,
feature_columns=deep_columns,
hidden_units=[100, 50])
else:
m = tf.estimator.DNNLinearCombinedClassifier(
model_dir=model_dir,
linear_feature_columns=crossed_columns,
dnn_feature_columns=deep_columns,
dnn_hidden_units=[100, 50])
return m
def input_fn(data_file, num_epochs, shuffle):
"""Input builder function."""
df_data = pd.read_csv(
tf.gfile.Open(data_file),
names=CSV_COLUMNS,
skipinitialspace=True,
engine="python",
skiprows=1)
# remove NaN elements
df_data = df_data.dropna(how="any", axis=0)
labels = df_data["income_bracket"].apply(lambda x: ">50K" in x).astype(int)
return tf.estimator.inputs.pandas_input_fn(
x=df_data,
y=labels,
batch_size=100,
num_epochs=num_epochs,
shuffle=shuffle,
num_threads=5)
def train_and_eval(model_dir, model_type, train_steps, train_data, test_data):
"""Train and evaluate the model."""
train_file_name, test_file_name = maybe_download(train_data, test_data)
model_dir = tempfile.mkdtemp() if not model_dir else model_dir
m = build_estimator(model_dir, model_type)
# set num_epochs to None to get infinite stream of data.
m.train(
input_fn=input_fn(train_file_name, num_epochs=None, shuffle=True),
steps=train_steps)
# set steps to None to run evaluation until all data consumed.
results = m.evaluate(
input_fn=input_fn(test_file_name, num_epochs=1, shuffle=False),
steps=None)
print("model directory = %s" % model_dir)
for key in sorted(results):
print("%s: %s" % (key, results[key]))
3. 自定义Estimator
- 参考代码：https://github.com/nlpming/tf-estimator/blob/master/tf-estimator-example/trainer.py
- 自定义Estimator大致流程：
（1）自定义model_fn，并定义模型、损失函数、优化器等； 根据model_fn生成Estimator对象。
（2）自定义input_fn；
（3）自定义训练过程；
3.1 自定义model_fn
- model_fn大致有下面几个参数：
（1）features, labels是必须传入的参数，这两个值都由input_fn传入；
（2）mode表示当前属于哪个阶段（可选参数）：train, eval, predict分为三个阶段，分别对应三个常量：tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL, tf.estimator.ModeKeys.PREDICT；
model_fn参数说明.png
def model_body(features):
net = tf.identity(features)
net = tf.layers.conv2d(net, filters=32, kernel_size=5, activation=tf.nn.relu)
net = tf.layers.max_pooling2d(net, pool_size=2, strides=2)
net = tf.layers.conv2d(net, filters=64, kernel_size=5, activation=tf.nn.relu)
net = tf.layers.max_pooling2d(net, pool_size=2, strides=2)
net = tf.contrib.layers.flatten(net)
net = tf.layers.dense(net, 1024, activation=tf.nn.relu)
return net
def build_model_fn(hparams):
'''Build the model function.'''
def model_fn(features, labels, mode, params=None):
'''Define the model graph.'''
if params:
hparams.override_from_dict(params)
net = model_body(features)
logits = tf.layers.dense(net, units=N_LABELS)
xentropies = tf.nn.softmax_cross_entropy_with_logits(logits=logits,
labels=labels)
loss = tf.reduce_mean(xentropies)
if hparams.get('learning_rate_decay_scheme') == 'exponential':
learning_rate = tf.train.exponential_decay(hparams.learning_rate,
tf.train.get_global_step(),
hparams.decay_steps,
hparams.decay_rate)
else:
learning_rate = hparams.learning_rate
optimizer = tf.train.MomentumOptimizer(learning_rate,
hparams.momentum)
if hparams.use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
train_op = optimizer.minimize(loss, global_step=tf.train.get_global_step())
def metric_fn(labels, logits):
predictions = tf.argmax(logits, axis=1)
accuracy = tf.metrics.accuracy(tf.argmax(labels, axis=1), predictions)
return {'accuracy': accuracy}
eval_metrics = (metric_fn, [labels, logits])
eval_metric_ops = metric_fn(labels, logits)
if hparams.use_tpu:
return tf.contrib.tpu.TPUEstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
eval_metrics=eval_metrics)
else:
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
return model_fn
3.2 自定义input_fn
- 自定义数据的读入过程，使用tf.data类；
- input_fn作为model_fn的输入；
def build_input_fn(fnames, hparams, is_training=True, use_tpu=True):
'''Build the input function.'''
def parse_fn(proto):
'''Parse a single Tensorflow example from a `TFRecord`.
Args:
proto: The serialized protobuf of the `tf.Example`
Returns:
A `Tensor` containing the image.
A one-hot `Tensor` containing the label.
'''
features = {
'im': tf.FixedLenFeature([28 * 28], tf.float32),
'label': tf.FixedLenFeature([], tf.int64),
}
parsed_features = tf.parse_single_example(proto, features)
im = tf.reshape(parsed_features['im'], [28, 28, 1])
label = tf.one_hot(parsed_features['label'], N_LABELS)
return im, label
def input_fn(params):
'''Feed input into the graph.'''
with tf.variable_scope('image_preprocessing'):
dataset = tf.data.TFRecordDataset(fnames)
dataset = dataset.shuffle(len(fnames))
dataset = dataset.map(parse_fn)
if is_training:
dataset = dataset.shuffle(args.shuffle_buffer_size)
dataset = dataset.repeat()
if use_tpu:
dataset = dataset.apply(
tf.contrib.data.batch_and_drop_remainder(params['batch_size']))
else:
dataset = dataset.batch(hparams.batch_size)
dataset = dataset.prefetch(buffer_size=1)
iterator = dataset.make_one_shot_iterator()
features, label = iterator.get_next()
return features, label
return input_fn
参考资料
- wide&deep模型：https://github.com/baidu-research/tensorflow-allreduce/blob/master/tensorflow/examples/learn/wide_n_deep_tutorial.py
wide&deep模型简介 - https://github.com/baidu-research/tensorflow-allreduce/blob/master/tensorflow/docs_src/tutorials/wide_and_deep.md - 自定义Estimator：https://github.com/joe-antognini/tf-estimator-example
- BERT中如何自定义Estimator：https://github.com/google-research/bert/blob/master/run_classifier.py
- DeepCTR中自定义Estimator：
（1）自定义model_fn：https://github.com/nlpming/DeepCTR/blob/master/deepctr/estimator/models/deepfm.py
（2）自定义input_fn：https://github.com/nlpming/DeepCTR/blob/master/deepctr/estimator/inputs.py
（3）自定义Estimator训练过程：https://github.com/nlpming/DeepCTR/blob/master/examples/run_estimator_tfrecord_classification.py - tf estimator如何导出模型：https://github.com/guillaumegenthial/tf-estimator-basics
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