TensorFlowでカスタム見積もりを保存する

Question

トレーニング後にカスタム見積もりを保存しようとしていますが、常にエラーが発生します。私はTensorFlow v.1.4を使用しており、ウェブやチュートリアルやサンプルで検索できるさまざまなソリューションを試しました。

（提供：私はhereでチュートリアルを開始しましたが、コードを修正しました）。

File "/.../RNN-estimators-v3.py", line 172, in <module> 
main() 

    File "/.../RNN-estimators-v3.py", line 167, in main 
save_nn(nn, './test_dir') 

    File "/.../RNN-estimators-v3.py", line 142, in save_nn 
nn_estimator.export_savedmodel(output_dir, my_serving_input_fn) 

    File "/.../anaconda/envs/TF-1-4-CPU/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 534, in export_savedmodel 
serving_input_receiver.receiver_tensors_alternatives) 

    File "/.../anaconda/envs/TF-1-4-CPU/lib/python3.6/site-packages/tensorflow/python/estimator/export/export.py", line 195, in build_all_signature_defs 
'{}'.format(type(export_outputs))) 

ValueError: export_outputs must be a dict and not<class 'NoneType'> 

あなたの助けがはるかに高く評価されています。ここでは

#!/usr/bin/env python3 
# -*- coding: utf-8 -*- 
""" 
Created on Sat Dec 16 10:17:59 2017 

@author: ali 
""" 

import tensorflow as tf 
import numpy as np 
import shutil 

# Define variables 
SEQ_LEN = 10 
DEFAULTS = [[0.0] for x in range(0, SEQ_LEN)] 
BATCH_SIZE = 20 
TIMESERIES_COL = 'rawdata' 
N_OUTPUTS = 2 # in each sequence, 1-8 are features, and 9-10 is label 
N_INPUTS = SEQ_LEN - N_OUTPUTS 
N_EPOCHS = 100 
LSTM_SIZE = 3 # number of hidden layers in each of the LSTM cells 
LEARNING_RATE = 0.01 

def create_time_series(): 
    freq = (np.random.random()*0.5) + 0.1 # 0.1 to 0.6 
    ampl = np.random.random() + 0.5 # 0.5 to 1.5 
    x = np.sin(np.arange(0,SEQ_LEN) * freq) * ampl 
    return x 

def to_csv(filename, N): 
    with open(filename, 'w') as ofp: 
    for lineno in range(0, N): 
     seq = create_time_series() 
     line = ",".join(map(str, seq)) 
     ofp.write(line + '\n') 

# read data and convert to needed format 
def read_dataset(filename, mode=tf.contrib.learn.ModeKeys.TRAIN): 

    def _input_fn(): 
     num_epochs = N_EPOCHS if mode == tf.contrib.learn.ModeKeys.TRAIN else 1 

     # could be a path to one file or a file pattern. 
     input_file_names = tf.train.match_filenames_once(filename) 

     filename_queue = tf.train.string_input_producer(input_file_names, num_epochs=num_epochs) 
     reader = tf.TextLineReader() 
     _, value = reader.read_up_to(filename_queue, num_records=BATCH_SIZE) 

     value_column = tf.expand_dims(value, -1) 
     print('readcsv={}'.format(value_column)) 

     # all_data is a list of tensors 
     all_data = tf.decode_csv(value_column, record_defaults=DEFAULTS) 
     inputs = all_data[:len(all_data)-N_OUTPUTS] # first few values 
     label = all_data[len(all_data)-N_OUTPUTS : ] # last few values 

     # from list of tensors to tensor with one more dimension 
     inputs = tf.concat(inputs, axis=1) 
     label = tf.concat(label, axis=1) 
     print('inputs={}'.format(inputs)) 

     return {TIMESERIES_COL: inputs}, label # dict of features, label 

    return _input_fn 


# create the inference model 
def simple_rnn(features, labels, mode, params): 

    # 0. Reformat input shape to become a sequence 
    x = tf.split(features[TIMESERIES_COL], N_INPUTS, 1) 
    #print 'x={}'.format(x) 

    # 1. configure the RNN 
    lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(LSTM_SIZE, forget_bias=1.0) 
    outputs, _ = tf.nn.static_rnn(lstm_cell, x, dtype=tf.float32) 

    # slice to keep only the last cell of the RNN 
    outputs = outputs[-1] 
    #print 'last outputs={}'.format(outputs) 

    # output is result of linear activation of last layer of RNN 
    weight = tf.Variable(tf.random_normal([LSTM_SIZE, N_OUTPUTS])) 
    bias = tf.Variable(tf.random_normal([N_OUTPUTS])) 
    predictions = tf.matmul(outputs, weight) + bias 

    # 2. loss function, training/eval ops 
    if mode == tf.contrib.learn.ModeKeys.TRAIN or mode == tf.contrib.learn.ModeKeys.EVAL: 
     loss = tf.losses.mean_squared_error(labels, predictions) 
     optimizer = tf.train.GradientDescentOptimizer(learning_rate=params["l_rate"]) 
     train_op = optimizer.minimize(loss=loss, global_step=tf.train.get_global_step()) 
     eval_metric_ops = {"rmse": tf.metrics.root_mean_squared_error(labels, predictions)} 
    else: 
     loss = None 
     train_op = None 
     eval_metric_ops = None 

    # 3. Create predictions 
    predictions_dict = {"predicted": predictions} 

    # 4. return ModelFnOps 
    return tf.estimator.EstimatorSpec(
     mode=mode, 
     predictions=predictions_dict, 
     loss=loss, 
     train_op=train_op, 
     eval_metric_ops=eval_metric_ops) 


def get_train(): 
    return read_dataset('train.csv', mode=tf.contrib.learn.ModeKeys.TRAIN) 

def get_valid(): 
    return read_dataset('valid.csv', mode=tf.contrib.learn.ModeKeys.EVAL) 

def my_serving_input_fn(): 
    ''' serving input function for saving the estimator''' 

    feature_spec = {TIMESERIES_COL: tf.FixedLenFeature(dtype=tf.float32, shape=[N_INPUTS])} 

    serialized_tf_example = tf.placeholder(dtype=tf.string, shape=[None], name='input_example_tensor') 
    receiver_tensors = {TIMESERIES_COL: serialized_tf_example} 
    features = tf.parse_example(serialized_tf_example, feature_spec) 

    return tf.estimator.export.ServingInputReceiver(features, receiver_tensors) 
    #return tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)() 


def generate_nn(): 
    model_params = {"l_rate": LEARNING_RATE} 
    nn = tf.estimator.Estimator(model_fn=simple_rnn, params=model_params, model_dir='./output_dir') 
    return nn 


def save_nn(nn_estimator, output_dir): 
    nn_estimator.export_savedmodel(output_dir, my_serving_input_fn) 
    print('Successfully saved the estimator...') 


def main(): 

    # remove previous files 
    shutil.rmtree('output_dir', ignore_errors=True) 
    shutil.rmtree('test_dir', ignore_errors=True) 

    # generate data 
    to_csv('train.csv', 5000) 
    to_csv('test.csv', 1000) 

    # instantiate the nn estimator 
    nn = generate_nn() 

    # train nn 
    nn.train(get_train(), steps=2000) 

    # evaluate nn 
    ev = nn.evaluate(input_fn=get_valid()) 
    print(ev) 

    # save nn for future use 
    save_nn(nn, './test_dir') 

if __name__ == '__main__': 
    main() 

は、私が受け取るエラーです：

は、ここに私のコードです。

Answer 1

グラフをエクスポートする場合、EstimatorSpecには、必要なexport_outputsフィールドがあります。詳細はmodel_fn documentationを参照してください。

tf.contrib.timeseriesには、この定型文の一部が書かれています（RNN exampleを含む）。