我试图在训练后保存自定义估算器，但总是收到错误。我正在使用TensorFlow v.1.4，并尝试了我可以在网上以及教程和示例中搜索的各种解决方案。
（感谢：我开始关注here的教程，但修改了代码以适应。）
这是我的代码：
#!/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()
以下是我收到的错误：
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'>
非常感谢您的帮助。
答案 0 :(得分：3)
当模式为Predict时，请确保在model_fn函数中包含 export_outputs 。
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)}
return tf.estimator.EstimatorSpec(
mode=mode,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
else:
loss = None
train_op = None
eval_metric_ops = None
# 3. Create predictions
export_outputs = {'predict_output': tf.estimator.export.PredictOutput({"pred_output_classes": predictions, 'probabilities': #your probabilities})}
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,export_outputs=export_outputs )
答案 1 :(得分：1)
导出图表时，export_outputs中有一个必需的EstimatorSpec字段。有关详细信息，请参阅model_fn documentation。
我还注意到tf.contrib.timeseries有一些为你编写的样板文件（包括RNN example）。