我成功地在本地和云中训练我的https://github.com/GoogleCloudPlatform/cloudml-samples/tree/master/census模型/实验。我能够部署我的样本并在云中运行预测。
但是，如果我想在本地运行我的预测 - 而不是在云端 - 我会怎么做呢？
我是一个新手，但我尝试了几种天真的方法，都失败了，请参阅下面的3个具体方法。
欢迎任何提示或引用片段。
： - ）
微米。
**关于原始帖子中的方法＃1的更新**
如果我包含单行;
c = tf.contrib.learn.DNNLinearCombinedClassifier(model_dir=job_dir)
我收到错误，请参阅下面的错误#a。
如果我天真地编辑调用以包含缺少的参数，则构造函数可以正常工作，但如果我调用预测失败并出现错误#b，请参阅下文。我在model.py全局中创建wide_columns和deep_columns，并将上面的行修改为
c = tf.contrib.learn.DNNLinearCombinedClassifier(model_dir=job_dir, linear_feature_columns=model.wide_columns, dnn_feature_columns=model.deep_columns)
我的pycharm调试器确认model.wide_columns和model.deep_columns在调用时被实例化/非空。
现在这导致了一个空的＆＃34;分类。我不相信DNNLinearCombinedClassifier会从我的job_dir中获取任何模型内容。我会在检查分类器的同时包含截图 在model.py build_estimator（）中实例化（我在那里也变成了一个变量c，并且有一个断点）和来自task.py中的上面的c，但由于我的缺乏，github不允许声誉。但差别很明显 - 例如对于恢复的分类器，c-> params-＆gt; dnn_hidden_units为空，但是用原始分类器实例化（[100,70,48,34]）。
我为job_dir（称为输出）包含一个ls -R，请参阅下面的#c。
我为每次运行执行rm -rf输出，因此job_dir是干净的。
显然我错在某个地方，但由于我缺乏洞察力，我无法看到哪里。任何进一步的建议表示赞赏。
： - ）
微米。
-----------------------控制台输出（更新）------------ --------------
Starting Census: Please lauch tensorboard to see results:
tensorboard --logdir=$MODEL_DIR
2017-05-30 12:14:10.570030: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-30 12:14:10.570042: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-30 12:14:10.570046: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
Traceback (most recent call last):
File "<..>/trainer/task.py", line 199, in <module>
c = tf.contrib.learn.DNNLinearCombinedClassifier(model_dir=job_dir)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 335, in new_func
return func(*args, **kwargs)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py", line 597, in __init__
raise ValueError("Either linear_feature_columns or dnn_feature_columns "
ValueError: Either linear_feature_columns or dnn_feature_columns must be defined.
Process finished with exit code 1
Starting Census: Please lauch tensorboard to see results:
tensorboard --logdir=$MODEL_DIR
2017-05-30 12:31:47.967638: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-30 12:31:47.967650: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-05-30 12:31:47.967653: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
Traceback (most recent call last):
File "<..>/repository/git/13cx/subject-matter/google-cloud/1705cloudml/170530local-save/trainer/task.py", line 206, in <module>
p = c.predict(input_fn=eval2_input_fn)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 335, in new_func
return func(*args, **kwargs)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 335, in new_func
return func(*args, **kwargs)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py", line 660, in predict
as_iterable=as_iterable)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 335, in new_func
return func(*args, **kwargs)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py", line 695, in predict_classes
as_iterable=as_iterable)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/python/util/deprecation.py", line 281, in new_func
return func(*args, **kwargs)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 565, in predict
as_iterable=as_iterable)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 857, in _infer_model
infer_ops = self._get_predict_ops(features)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1188, in _get_predict_ops
return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.INFER)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1103, in _call_model_fn
model_fn_results = self._model_fn(features, labels, **kwargs)
File "<..>/.local/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py", line 201, in _dnn_linear_combined_model_fn
"dnn_hidden_units must be defined when dnn_feature_columns is "
ValueError: dnn_hidden_units must be defined when dnn_feature_columns is specified.
Process finished with exit code 1
$ ls -R output/
output/:
checkpoint graph.pbtxt model.ckpt-2.data-00000-of-00001
eval model.ckpt-1000.data-00000-of-00001 model.ckpt-2.index
events.out.tfevents.1496140978.yarc-mainlinux model.ckpt-1000.index model.ckpt-2.meta
export model.ckpt-1000.meta
output/eval:
events.out.tfevents.1496140982.yarc-mainlinux events.out.tfevents.1496140987.yarc-mainlinux
output/export:
Servo
output/export/Servo:
1496140989
output/export/Servo/1496140989:
saved_model.pb variables
output/export/Servo/1496140989/variables:
variables.data-00000-of-00001 variables.index
---------- **原帖** ----------
--------我尝试过的事情------------
请参阅底部的代码，参考1,2,3 ..
使用指向模型存储位置的model_dir参数重新实例化DNNLinearCombinedClassifier。计划是运行分类器的预测方法。我无法使分类器反映已保存的模型。
通过saver.restore（）恢复模型。这有效，但我不明白如何从那里开始。由于缺乏张量流洞察力，我猜。
生成一些测试数据以供方法1使用。评估张量永远不会退出。如何评估输入批次，以便将其视为矩阵？
---------附带代码-----------------
（此代码只是附加到trainer / task.py的末尾）
# last original line from task.py:
learn_runner.run(generate_experiment_fn(**arguments), job_dir)
# my stuff:
# 1. restore the classifier from model dir, fails
# c = tf.contrib.learn.DNNLinearCombinedClassifier(model_dir=job_dir)
# 2. restore model, works ok, but then how?
sess = tf.Session()
saver = tf.train.import_meta_graph('output/model.ckpt-1000.meta')
saver.restore(sess, tf.train.latest_checkpoint('./output/'))
sess.run(tf.global_variables_initializer())
print("Sanity check, a variable instance {}".format(
sess.run('dnn/input_from_feature_columns/education_embedding/weights/part_0:0')))
sess.close()
# 3. produce some test input (we're for simplicity reusing the eval set), apparently works, but an evaluation hangs forever
eval2_input_fn = model.generate_input_fn(
arguments['eval_files'],
batch_size=arguments['eval_batch_size'],
shuffle=False
)
# 3a. inspecting some input, the evaluation never ends.
input = eval2_input_fn()
print("input: {}".format(input))
with tf.Session() as sess:
evalinput = input[1].eval()
print("evalinput: {}".format(evalinput))
print("\nDone")
答案 0 :(得分：2)
最简单的方法是使用gcloud：
gcloud ml-engine local predict --model-dir output/export/Servo/1496140989 \
--json-instances ../test.json
答案 1 :(得分：0)
如果不考虑性能，您可以直接使用predict函数（上面的＃1）：
c = tf.contrib.learn.DNNLinearCombinedClassifier(model_dir=job_dir)
eval2_input_fn = model.generate_input_fn(
arguments['eval_files'],
batch_size=arguments['eval_batch_size'],
shuffle=False
)
c.predict(input_fn=eval2_input_fn)
或者你可以手动做一些事情：
class Predictor(object):
def __init__(self, export_dir):
self._sess = tf.Session()
# Load the SavedModel
meta = tf.saved_model.loader.load(self._sess, ['serve'], export_dir)
# Map input aliases to the actual tensor names in the graph.
inputs = meta.signature_def['serving_default'].inputs
self._input_dict = {alias: info.name for alias, info in inputs.iteritems()}
# Get the output aliases and tensor names
outputs = meta.signature_def['serving_default'].outputs
output_dict = [(alias, info.name) for alias, info in outputs.iteritems()]
self._out_aliases, self._fetches = zip(*output_dict)
def predict(self, examples):
"""Perform prediction on a list of examples (dicts)"""
# Convert the list of examples to a feed dict by converting the rows to columns
# and changing the tensor aliases to actual tensor names.
columns = self._columnarize(examples)
feed_dict = {self._input_dict[name]: val for name, val in columns.iteritems()}
# Perform the actual prediction.
fetched = self._sess.run(self._fetches, feed_dict)
# Convert the fetched data to friendlier row-based output whose keys are
# the output names/aliases.
output_dict = dict(zip(self._out_aliases, fetched))
return self._rowify(output_dict)
def _columnarize(self, examples):
"""Convert a list of dicts to a dict of lists."""
columns = collections.defaultdict(list)
for example in examples:
for name, val in example.iteritems():
columns[name].append(val)
return columns
def _rowify(self, output_dict):
"""Convert a dict of lists to a list of dicts."""
rows = []
row_values = zip(*output_dict.values())
for row in row_values:
# Convert the row data to a dict
rows.append(dict(zip(output_dict.keys(), row)))
return rows
# Be sure to set the last path element to the correct value.
export_dir = os.path.join(job_dir, 'export', 'Servo', '1496140989')
p = Predictor(export_dir)
# Create an example. Note the space before strings due to the way
# the CSV file is parsed during training.
example = {'age': 42,
'workclass': ' Private',
'education': ' Masters',
'education_num': 14,
'marital_status': ' Never-married',
'occupation': ' Adm-clerical',
'relationship': ' Not-in-family',
'race': ' White',
'gender': ' Male',
'capital_gain': 0,
'capital_loss': 0,
'hours_per_week': 42,
'native_country': ' United-States'}
p.predict([example])
[{u＆＃39;概率＆＃39;：数组（[0.90454769,0.09545235]，dtype = float32），u＆＃39; logits＆＃39 ;: array（[ - 2.24880791]，dtype = float32），u＆＃ 39; class＆＃39;：0，u＆＃39; logistic＆＃39 ;: array（[0.09545235]，dtype = float32）}]
挂起可能是因为你需要开始＆＃34; queue runners＆＃34;。
with tf.Session() as sess:
# Start populating the filename queue.
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
print(sess.run(...))
coord.request_stop()
coord.join(threads)
也就是说，在使用队列时打印输入有点棘手。
答案 2 :(得分：0)
您可以使用Estimator本身进行预测（虽然这对于生产使用来说还不够快）。
需要注意两件事：
确保您的model_dir具有检查点，该检查点由培训过程保存。 Predict将从检查点加载参数，以便真正预测某些内容。
您需要使用与训练相同的设置构建Estimator。
最简单的方法（给出cloudml-samples提供的示例）是
predict 使用Estimator本身，你需要使用本地python ，因为它无法利用谷歌云。
在下面的示例中，我注释掉了learn_runner.run以禁用培训（假设您已将模型训练为将检查点保存到job_dir中），然后使用numpy_input_fn为predict准备数据。
## Commented out the learn_runner run to do predict.
## Now the code can only work with local python.
# learn_runner.run(generate_experiment_fn(**arguments), job_dir)
# Change the code to construct the Estimator with exactly the same setting as
# distributed training (with Experiment) but take the Estimator out and call
# the predict expliclity.
experiment_fn = generate_experiment_fn(**arguments)
experiment = experiment_fn(job_dir)
print("Using estimator to predict")
estimator = experiment.estimator
# The data contains two items.
data = {
'age': [42, 47],
'workclass': ['Private', 'Private'],
'education': ['Masters', 'Prof-school'],
'education_num': [14, 15],
'marital_status': ['Never-married', 'Married-civ-spouse'],
'occupation': ['Adm-clerical', 'Prof-specialty'],
'relationship': ['Not-in-family', 'Wife'],
'race': ['White', 'White'],
'gender': ['Male', 'Female'],
'capital_gain': [0, 0],
'capital_loss': [0, 1902],
'hours_per_week': [42, 60],
'native_country': ['United-States', 'Honduras'],
}
import numpy as np
for k,v in data.items():
# Convert each column to numpy array and make sure it has rank 2, which is
# required by the DNNCombinedLinearClassifier.
data[k] = np.expand_dims(np.array(v), -1)
predict_input_fn = tf.contrib.learn.io.numpy_input_fn(
x=data, shuffle=False, num_epochs=1)
for predicted_item in estimator.predict(input_fn=predict_input_fn):
print('Predication: {}'.format(predicted_item))