TensorFlow template application for deep learning

License: Apache License 2.0

Python 68.69% Shell 0.74% Java 10.01% Scala 3.53% Go 3.19% C++ 3.88% CMake 0.87% Objective-C 0.81% Objective-C++ 8.22% Ruby 0.05%

tensorflow tfrecords libsvm csv deep-learning machine-learning mlp cnn lstm inference

tensorflow_template_application's Introduction

Introduction

It is the generic golden program for deep learning with TensorFlow.

Usage

Generate TFRecords

If your data is in CSV format, generate TFRecords like this.

cd ./data/cancer/

./generate_csv_tfrecords.py

If your data is in LIBSVM format, generate TFRecords like this.

cd ./data/a8a/

./generate_libsvm_tfrecord.py

For large dataset, you can use Spark to do that. Please refer to data.

Run Training

You can train with the default configuration.

./dense_classifier.py

./sparse_classifier.py

Using different models or hyperparameters is easy with TensorFlow flags.

./dense_classifier.py --batch_size 1024 --epoch_number 1000 --step_to_validate 10 --optmizier adagrad --model dnn --model_network "128 32 8"

If you use other dataset like iris, no need to modify the code. Just run with parameters to specify the TFRecords files.

./dense_classifier.py --train_file ./data/iris/iris_train.csv.tfrecords --validate_file ./data/iris/iris_test.csv.tfrecords --feature_size 4 --label_size 3  --enable_colored_log

./dense_classifier.py --train_file ./data/iris/iris_train.csv --validate_file ./data/iris/iris_test.csv --feature_size 4 --label_size 3 --input_file_format csv --enable_colored_log

If you want to use CNN model, try this command.

./dense_classifier.py --train_file ./data/lung/fa7a21165ae152b13def786e6afc3edf.dcm.csv.tfrecords --validate_file ./data/lung/fa7a21165ae152b13def786e6afc3edf.dcm.csv.tfrecords --feature_size 262144 --label_size 2 --batch_size 2 --validate_batch_size 2 --epoch_number -1 --model cnn

For boston housing dataset.

./dense_classifier.py --train_file ./data/boston_housing/train.csv.tfrecords --validate_file ./data/boston_housing/train.csv.tfrecords --feature_size 13 --label_size 1 --scenario regression  --batch_size 1 --validate_batch_size 1

Export The Model

After training, it will export the model automatically. Or you can export manually.

./dense_classifier.py --mode savedmodel

Validate The Model

If we want to run inference to validate the model, you can run like this.

./dense_classifier.py --mode inference

Use TensorBoard

The program will generate TensorFlow event files automatically.

tensorboard --logdir ./tensorboard/

Then go to http://127.0.0.1:6006 in the browser.

Serving and Predicting

The exported model is compatible with TensorFlow Serving. You can follow the document and run the tensorflow_model_server.

./tensorflow_model_server --port=9000 --model_name=dense --model_base_path=./model/

We have provided some gRPC clients for dense and sparse models, such as Python predict client and Java predict client.

./predict_client.py --host 127.0.0.1 --port 9000 --model_name dense --model_version 1

mvn compile exec:java -Dexec.mainClass="com.tobe.DensePredictClient" -Dexec.args="127.0.0.1 9000 dense 1"

Contribution

This project is widely used for different tasks with dense or sparse data.

If you want to make contributions, feel free to open an issue or pull-request.

tensorflow_template_application's People

Contributors

Stargazers

Watchers

Forkers

clear-datacenter putizl benjamesbabala little1tow yuandy chengshitju xsongx leigaosearch liuhengli shi-wu xiximeng jacktang royshan tonytongzhao nonva wanjinchang birdgun wangxiangbupt vyraun lqshixinlei xuzhaokui chanis wuqixiaobai xiulonghan kryanchen zzmjohn zhichao-li frankiegu chenjun0210 houshanren limingdeng leezqcst xiaoming-qxm jakehsiao chao-jiang undarmaa cattles leo-hzau hundred06 code2015wang zuxianghuang qlc1988 wzppengpeng yj20586 astbl123 joyle tabasuko lc222 adrianzuo tangpanpan23 wenhuazang xwc940512 gwang-vince teardemon bobqiu gzzgz odinlin xiliangsong jianhuaixie mchenla huangpingchun yaochengbupt xuejianche atorefrank hejunbok koryako leanklass moonlightlong wanghaox xiaozhuge080 minghao2016 renly amano-ginji reachym valiant719 wuzhongdehua squirrelmaster xiaojingyi jinyu0310 solertis mworldq jiayong bamboofeller hhungry casywang robustfengbin fangzheng354 xielm12 leecodedog ponggung xiaobijuan wubinzzu kongshuchen stevenlol lanyastar ck8275411 keyeqing lishengfever linpelvis anseey

tensorflow_template_application's Issues

Missing 'streaming_auc'

Hi there, I downloaded deep_recommend_system for study. But failed to run cancer_classifier.py. The following is what I got. Please anybody advise what is the problem? Thanks!

Use the model: wide_and_deep
Use the optimizer: adagrad
Use the model: wide_and_deep
Traceback (most recent call last):
File "cancer_classifier.py", line 203, in
_, auc_op = tf.contrib.metrics.streaming_auc(validate_softmax,
AttributeError: 'module' object has no attribute 'streaming_auc'

KeyError: u'key'

when i send post request to /cancer_predict/predict/, occur error like this:File "/Users/terry/Downloads/deep_recommend_system-master/http_service/cancer_predict/views.py", line 38, in predict feed_dict[v] = np.array(examples[k])

i print examples value:{u'features': u'10,10,10,8,6,1,8,9,1;6,2,1,1,1,1,7,1,1'} and (k, v) value k:features, v:Placeholder:0;

how can i fix the error, thanks for help~

import tensorflow.serving.Model;

hello ,
import org.tensorflow.framework.TensorProto;
import org.tensorflow.framework.TensorShapeProto;
import tensorflow.serving.Model;
import tensorflow.serving.Predict;
import tensorflow.serving.PredictionServiceGrpc;
is error.I don not have this package, and tried mvn clean install. how to solve this?thank you !!!

Error while running CNN

I tried to the CNN using command given in README.md

       ./dense_classifier.py --train_file ./data/lung/fa7a21165ae152b13def786e6afc3edf.dcm.csv.tfrecords --validate_file ./data/lung/fa7a21165ae152b13def786e6afc3edf.dcm.csv.tfrecords --feature_size 262144 --label_size 2 --batch_size 2 --validate_batch_size 2 --epoch_number -1 --model cnn

I get the following error

File "/home/root1/.virtualenv/tensorflow_template_application/local/lib/python2.7/site-packages/tensorflow/python/client/session.py", line 1363, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.NotFoundError: Key conv0/bias not found in checkpoint
	 [[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

Caused by op u'save/RestoreV2', defined at:
  File "./dense_classifier.py", line 580, in <module>
    main()
  File "./dense_classifier.py", line 438, in main
    saver = tf.train.Saver()



NotFoundError (see above for traceback): Key conv0/bias not found in checkpoint
	 [[Node: save/RestoreV2 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/device:CPU:0"](_arg_save/Const_0_0, save/RestoreV2/tensor_names, save/RestoreV2/shape_and_slices)]]

InvalidArgumentError (see above for traceback): Cannot assign a device to node 'save/RestoreV2_8'

distributed/cancer_classifier.py works in only one docker container.

It works in one container:

# both in 127.17.0.3
python cancer_classifier.py --ps_hosts=127.17.0.3:8222 --worker_hosts=127.17.0.3:8223 --job_name=ps --task_index=0
python cancer_classifier.py --ps_hosts=127.17.0.3:8222 --worker_hosts=127.17.0.3:8223 --job_name=worker --task_index=0

But it not work in two containers:

# ps in 127.17.0.3
python cancer_classifier.py --ps_hosts=127.17.0.3:8222 --worker_hosts=127.17.0.4:8223 --job_name=ps --task_index=0`
# worker in 127.17.0.4
python cancer_classifier.py --ps_hosts=127.17.0.3:8222 --worker_hosts=127.17.0.4:8223 --job_name=worker --task_index=0

the error msg I got in the worker:

I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:197] Initialize GrpcChannelCache for job ps -> {0 -> 127.17.0.3:8222}
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:197] Initialize GrpcChannelCache for job worker -> {0 -> localhost:8222}
I tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc:211] Started server with target: grpc://localhost:8222
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py:344 in __init__.: __init__ (from tensorflow.python.training.summary_io) is deprecated and will be removed after 2016-11-30.
Instructions for updating:
Please switch to tf.summary.FileWriter. The interface and behavior is the same; this is just a rename.
I tensorflow/core/distributed_runtime/master_session.cc:993] Start master session 91acfc1008531f4d with config:

Traceback (most recent call last):
  File "cancer_classifier_new.py", line 241, in <module>
    tf.app.run(main=main)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 43, in run
    sys.exit(main(sys.argv[:1] + flags_passthrough))
  File "cancer_classifier_new.py", line 209, in main
    with sv.managed_session(server.target) as sess:
  File "/usr/lib/python2.7/contextlib.py", line 17, in __enter__
    return self.gen.next()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 974, in managed_session
    self.stop(close_summary_writer=close_summary_writer)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 802, in stop
    stop_grace_period_secs=self._stop_grace_secs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/coordinator.py", line 386, in join
    six.reraise(*self._exc_info_to_raise)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 963, in managed_session
    start_standard_services=start_standard_services)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/supervisor.py", line 720, in prepare_or_wait_for_session
    init_feed_dict=self._init_feed_dict, init_fn=self._init_fn)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/session_manager.py", line 227, in prepare_session
    config=config)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/session_manager.py", line 173, in _restore_checkpoint
    saver.restore(sess, ckpt.model_checkpoint_path)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1388, in restore
    {self.saver_def.filename_tensor_name: save_path})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 766, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 964, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1014, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1034, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a device to node 'save/RestoreV2_8': Could not satisfy explicit device specification '/job:ps/task:0/device:CPU:0' because no devices matching that specification are registered in this process; available devices: /job:worker/replica:0/task:0/cpu:0
	 [[Node: save/RestoreV2_8 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:ps/task:0/device:CPU:0"](save/Const, save/RestoreV2_8/tensor_names, save/RestoreV2_8/shape_and_slices)]]

Caused by op u'save/RestoreV2_8', defined at:
  File "cancer_classifier_new.py", line 241, in <module>
    tf.app.run(main=main)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 43, in run
    sys.exit(main(sys.argv[:1] + flags_passthrough))
  File "cancer_classifier_new.py", line 191, in main
    saver = tf.train.Saver()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1000, in __init__
    self.build()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 1030, in build
    restore_sequentially=self._restore_sequentially)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 624, in build
    restore_sequentially, reshape)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 361, in _AddRestoreOps
    tensors = self.restore_op(filename_tensor, saveable, preferred_shard)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py", line 200, in restore_op
    [spec.tensor.dtype])[0])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_io_ops.py", line 441, in restore_v2
    dtypes=dtypes, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 759, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2240, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1128, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError (see above for traceback): Cannot assign a device to node 'save/RestoreV2_8': Could not satisfy explicit device specification '/job:ps/task:0/device:CPU:0' because no devices matching that specification are registered in this process; available devices: /job:worker/replica:0/task:0/cpu:0
	 [[Node: save/RestoreV2_8 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:ps/task:0/device:CPU:0"](save/Const, save/RestoreV2_8/tensor_names, save/RestoreV2_8/shape_and_slices)]]

how to use this project to fine tune or transfer learning

Tensor slice is too large to serialize

when input_unit is sparse feature with size 20000000, network is 1286464*32
train the model ,it errors "Tensor slice is too large to serialize".

did you try this repo in production system with large data?

wish some help to slove this. thanks

benchmark_predict_client.py doesn't exist

The link https://github.com/tobegit3hub/deep_recommend_system/tree/master/minimal_model/python_predict_client/benchmark_predict_client.py doesn't exist

test data file not found

Traceback (most recent call last):
File "./dense_classifier.py", line 420, in
main()
File "./dense_classifier.py", line 349, in main
inference_data = np.genfromtxt(inference_test_file_name, delimiter=",")
File "/usr/local/lib/python2.7/dist-packages/numpy/lib/npyio.py", line 1451, in genfromtxt
fhd = iter(np.lib._datasource.open(fname, 'rbU'))
File "/usr/local/lib/python2.7/dist-packages/numpy/lib/_datasource.py", line 151, in open
return ds.open(path, mode)
File "/usr/local/lib/python2.7/dist-packages/numpy/lib/_datasource.py", line 501, in open
raise IOError("%s not found." % path)
IOError: ./data/cancer_test.csv not found.

the path is './data/cancer/cancer_test.csv', not './data/cancer_test.csv'

wrong in cancer_classifier.py

I test run deep_recommend_system-master/cancer_classifier.py, but it reveals that 'module' object has no attribute 'streaming_auc' in line 203. I don't know why, when the tensorflow_examples are running fluently in my PC.
Thanks for your answers.

Cannot find file: ./tensorflow_model_server

The readme.md in grpc_service

In README.md the inference_server.py and inference_client.py shouldn't be predict_server.py and predict_client.py?

数组转换

我读入一张图片，datetype:float32, shape:[95,92,1], value 是一个[][][]float32的3维数组
现在，想要调 tensorflow 的grpc，
63 request := &pb.PredictRequest{
64 ModelSpec: &pb.ModelSpec{
65 Name: "mnist",
66 SignatureName: "predict_images",
67 Version: &google_protobuf.Int64Value{
68 Value: int64(1),
69 },
70 },
71 Inputs: map[string]*tf_core_framework.TensorProto{
72 "images": &tf_core_framework.TensorProto{
73 Dtype: tf_core_framework.DataType_DT_FLOAT,
74 TensorShape: &tf_core_framework.TensorShapeProto{
75 Dim: []*tf_core_framework.TensorShapeProto_Dim{
76 &tf_core_framework.TensorShapeProto_Dim{
77 Size: tensor.Shape()[0],
78 },
79 &tf_core_framework.TensorShapeProto_Dim{
80 Size: tensor.Shape()[1],
81 },
82
83 &tf_core_framework.TensorShapeProto_Dim{
84 Size: tensor.Shape()[2],
85 },
86 },
87 },
88 FloatVal: tensor.Value().([]float32),
89 },
90 },
91 }

FloatVal: tensor.Value().([]float32), 这个地方tensor.value()是[][][]float32类型的，怎么转换？

请问您试过在redhat6.7版本中编译tensorflow serving么？

https://github.com/tobegit3hub/tensorflow_template_application/tree/master/http_service

你好，上面的链接，我没有找到online_train实例的代码。可否提供tensorflow的online_train的思路，谢谢

Question about valid_batch_data

Hi tobe,

# Read TFRecords file for validatioin
validate_filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("data/cancer_test.csv.tfrecords"),
    num_epochs=epoch_number)
validate_label, validate_features = read_and_decode(validate_filename_queue)
validate_batch_labels, validate_batch_features = tf.train.shuffle_batch(
    [validate_label, validate_features],
    batch_size=validate_batch_size,
    num_threads=thread_number,
    capacity=capacity,
    min_after_dequeue=min_after_dequeue)

In the code, the validation batch data are also generated by QueueRunner. So cooridinator will collect these valid batch threads together with train QueueRunner right?

But if I have a different batch_size settings in train dataset and valid dataset. Their total num are also different( most of time). What should the coordiantor work?

And I found you set the num_of_epochs the same value in train and valid queuerunner. Why it like that?
I think the two num_epoch should not be equal?


flags.DEFINE_integer('epoch_number', None, 'Number of epochs to run trainer.')

filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("data/cancer_train.csv.tfrecords"),
    num_epochs=epoch_number)

validate_filename_queue = tf.train.string_input_producer(
    tf.train.match_filenames_once("data/cancer_test.csv.tfrecords"),
    num_epochs=epoch_number)

Thanks!

how to support regression?

ERROR:root:Get the unsupported parameters, exit now

I entried the command after the Run Training
$ ./dense_classifier.py --mode export
And I got this response.

Trying to search your Flag in the code , I found that there was not a Flag called "export" in Line 66.
And another Flag also couldn't export the report.

Did it just a bug? or I just mistaked something ?

Python gRPC server is not reachable

Does this project support "Python gRPC server"? I open the link in the main page but found error 404.

batch_normalization should be involved in Test mode

When is_train, the bn is added. But test, it's ignored. based on the doc in the page, https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/layers.py
logic should be like the desc below:
is_training: Whether or not the layer is in training mode. In training mode
it would accumulate the statistics of the moments into moving_mean and
moving_variance using an exponential moving average with the given
decay. When it is not in training mode then it would use the values of
the moving_mean and the moving_variance.

DropOut PlaceHolder should be a scalar, how to construct it with tensorProto

I construct it using BoolVal : []bool{fasle}, with shape dim size = 1, but it has errors like behind:

The second input must be a scalar, but it has shape [1]

I don't know how to solve it.

Proposing a PR to fix a few small typos

Issue Type

[x] Bug (Typo)

Steps to Replicate and Expected Behaviour

Examine cpp_predict_client/README.md search for direcotry however expect to see directory.
Examine README.md search for contirbutions however expect to see contributions.

Notes

Semi-automated issue generated by
https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

To avoid wasting CI processing resources a branch with the fix has been
prepared but a pull request has not yet been created. A pull request fixing
the issue can be prepared from the link below, feel free to create it or
request @timgates42 create the PR.

https://github.com/timgates42/tensorflow_template_application/pull/new/bugfix_typos

Thanks.

python predict client problem

Hi tobe,
I want to use the python and java client to get data back from tf serving server on another PC 10.10.10.229. It seems bazel and tensorflow is not necessary here.
But when I run the predict_client.py code, it seems the connection failed. How can I fix?

~/Desktop/tensorflow_template_application/python_predict_client$ ./predict_client.py --host 10.10.10.229 --port 9000 --model_name dense --model_version 1
Traceback (most recent call last):
  File "./predict_client.py", line 51, in <module>
    main()
  File "./predict_client.py", line 46, in main
    result = stub.Predict(request, request_timeout)
  File "/usr/local/lib/python2.7/dist-packages/grpc/beta/_client_adaptations.py", line 309, in __call__
    self._request_serializer, self._response_deserializer)
  File "/usr/local/lib/python2.7/dist-packages/grpc/beta/_client_adaptations.py", line 195, in _blocking_unary_unary
    raise _abortion_error(rpc_error_call)
grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.UNAVAILABLE, details="Connect Failed")

error with sparse_softmax_cross_entropy_with_logits

When run dense_classifier or sparse_classifier, there would be an error as below:
What is the problem? version of python?

File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.py", line 1684, in sparse_softmax_cross_entropy_with_logits
  labels, logits)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/nn_ops.py", line 1533, in _ensure_xent_args
  "named arguments (labels=..., logits=..., ...)" % name)
ValueError: Only call `sparse_softmax_cross_entropy_with_logits` with named arguments (labels=..., logits=..., ...)

libsvm data format

Hi. I have data set with libsvm format where feaidis uint64. Can you give me an example of how to fetch this data to tensorflow model? eg. sparse logistic regression. Thanks.
label feaid:value feaid:value ...

Performance issues in the definition of `main`,blob/master/sparse_classifier.py

Hello,I found a performance issue in the definition of main ,
tobegit3hub/tensorflow_template_application/blob/master/sparse_classifier.py,
train_dataset.map was called without num_parallel_calls.
I think it will increase the efficiency of your program if you add this.

The same issues also exist in validation_dataset.map ,
train_dataset.map and other three places

Here is the documemtation of tensorflow to support this thing.

Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.

feature size of a8a dataset may be wrong

Hi, in the file sparse_classifier.py, feature size is assigned to 124 for a8a dataset.

flags.DEFINE_integer("feature_size", 124, "Number of feature size")

However, I think the feature size should be 123 according to this website.

Am I right?

golang_predict_client proto_buf issue

when I follow the golang_predict_client, it show this issue

how can i use the trained model to predict my own data?

i use python manage.py runserver 0.0.0.0:8000 command to start http service, but how can i use rest client to predict my own data;other words, when i post
http request, what's the content-type(Content-Type: application/x-www-form-urlencoded?) and http body(cancer_features="10,10,10,8,6,1,8,9,1;6,2,1,1,1,1,7,1,1" ?)
of request, thank you for guidance

TF转化后的样本数据训练时没有全部用上是什么原因？

上千万条转化为TF格式后数据，用sparse_classifier.py 的默认配置训练时不到1分钟就结束了，貌似样本没有读完。这个是什么原因？

Binary TensorFlow model server doesn't exist

The link to binary TensorFlow model server in https://github.com/tobegit3hub/deep_recommend_system/blob/master/cpp_predict_server/README.md doesn't exist

Performance issues in your project (by P3)

Hello! I've found a performance issue in your project: batch() should be called before map(), which could make your program more efficient. Here is the tensorflow document to support it.

Detailed description is listed below:

sparse_classifier.py: .batch(FLAGS.train_batch_size)(here) should be called before .map(parse_tfrecords_function)(here).
sparse_classifier.py: .batch(FLAGS.validation_batch_size)(here) should be called before .map(parse_tfrecords_function)(here).
dense_classifier.py: .batch(FLAGS.train_batch_size)(here) should be called before .map(parse_tfrecords_function)(here).
dense_classifier.py: .batch(FLAGS.train_batch_size)(here) should be called before .map(parse_csv_function)(here).
dense_classifier.py: .batch(FLAGS.validation_batch_size)(here) should be called before .map(parse_tfrecords_function)(here).
dense_classifier.py: .batch(FLAGS.validation_batch_size)(here) should be called before .map(parse_csv_function)(here).

Besides, you need to check the function called in map()(e.g., parse_csv_function called in map()) whether to be affected or not to make the changed code work properly. For example, if parse_csv_function needs data with shape (x, y, z) as its input before fix, it would require data with shape (batch_size, x, y, z).

Looking forward to your reply. Btw, I am very glad to create a PR to fix it if you are too busy.

No module named tensorflow_serving.apis

run ./predict_client.py

Error: No module named tensorflow_serving.apis

Run with an error "double free or corruption"

I ran the sparse_classifier.py program and got this error in the export_model() function.

INFO:tensorflow:./sparse_model/00000001-tmp/export is not in all_model_checkpoint_pat
hs. Manually adding it.
*** Error in `python': double free or corruption (!prev): 0x0000000000825660 ***

I want to test the AUC of the entire validation set, setting the validation_batch as the size of validation set is too slow. So I want to export the model and predict AUC on the entire validation set, and I got this problem.

Is there any suggestion？

java predict client problem

In DensePredictClient.java
project can't find these moduels:
import org.tensorflow.framework.DataType;
import org.tensorflow.framework.TensorProto;
import org.tensorflow.framework.TensorShapeProto;
I have involved tensorflow in pom.xml

org.tensorflow
tensorflow
1.3.0

also can't find these:
import tensorflow.serving.Model;
import tensorflow.serving.Predict;
import tensorflow.serving.PredictionServiceGrpc;

Question about test.

I want to output the accuracy with all the train and test data, not the batch_size train and test data. But when I change the "batch_xtrain" to "xtrain", there is a not match error. Why? What is the right way?

train_accuracy_logits = inference(batch_xtrain, False)

train_sigmoid = train_accuracy_logits
train_correct_prediction = tf.equal(tf.argmax(train_sigmoid, 1), tf.argmax(batch_ytrain, 1))
train_accuracy = tf.reduce_mean(tf.cast(train_correct_prediction, tf.float32))

ValueError: Shape must be rank 2 but is rank 1 for 'layer1_1/MatMul' (op: 'MatMul') with input shapes: [440], [440,500].

how can i use your code to train a candidate generation model

grpc.framework.interfaces.face.face.ExpirationError: ExpirationError(code=StatusCode.DEADLINE_EXCEEDED, details="Deadline Exceeded")

When I run "./predict_client.py ... ..." or "cloudml models predict ... ..."
It appear error: grpc.framework.interfaces.face.face.ExpirationError: ExpirationError(code=StatusCode.DEADLINE_EXCEEDED, details="Deadline Exceeded")

Model not getting generated ,getting the message as "Do not export the model yet"

Dear Team ,

I am new to tensorflow API , so with these code I am trying to build the model using the below command
I am trying to build the model using the below command,
./dense_classifier.py --batch_size 1024 --epoch_number 1000 --steps_to_validate 10 --optimizer adagrad --model dnn --dnn_struct "128 32 8"

But with the old code I was able to generate the model successfully ,after the code changes I am not able to generate the model.

From my understanding I have not seen the export_model method in the code, may be because of that model is not being generated, please correct me if I am wrong .

Also please let me know if there is any other step is required to generate the model foder.

Eagerly waiting for your reply. Thank you very much for giving very good project for practice .

Thanking you

cancer_classifier.py error

After fixing issue of #15, i met more complicated issue in assert_is_compatible_with

Traceback (most recent call last):
  File "./cancer_classifier.py", line 241, in <module>
    tf.app.run()
  File "/Library/Python/2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "./cancer_classifier.py", line 174, in main
    concated = tf.concat(1, [indices, sparse_labels])
  File "/Library/Python/2.7/site-packages/tensorflow/python/ops/array_ops.py", line 1032, in concat
    ).assert_is_compatible_with(tensor_shape.scalar())
  File "/Library/Python/2.7/site-packages/tensorflow/python/framework/tensor_shape.py", line 735, in assert_is_compatible_with
    raise ValueError("Shapes %s and %s are incompatible" % (self, other))
ValueError: Shapes (2, 1024, 1) and () are incompatible

my tensorflow version:

python -c 'import tensorflow; print tensorflow.__version__'
1.1.0-rc0

tobegit3hub / tensorflow_template_application Goto Github PK