TensorFlow保存和恢复

Question

我正在尝试使用Tensorflow保存/加载模型。模型似乎保存正确，但我无法使预测函数正常工作。我不断得到以下错误：

AttributeError:“list”对象没有属性“keys”

如有任何建议，将不胜感激

import os
import urllib
import numpy as np
import tensorflow as tf
import csv
import pandas as pd
import sys

##########################
# User Command line inputs
##########################
SYM = sys.argv[1]
TYPE =  sys.argv[2]
AHEAD = sys.argv[3]
DATE = sys.argv[4]


# Set the training file
TRAINING = './training/' + str(SYM) + "_" + str(TYPE) + "_" + str(AHEAD) + 
'.csv'

READER = csv.reader(open(TRAINING, 'rb'), delimiter=",")
NUM_COLS = len(next(READER))
TARGETCOLUMN_CLASS = NUM_COLS - 1
PREDICTORSTART = 1
PREDICTOREND = NUM_COLS - 2

TRAININGDATA = pd.read_csv(TRAINING, delim_whitespace=False, delimiter=',')

# Remove the Test Data from the Sample
TESTDATA = TRAININGDATA.tail(450)
TRAININGDATA.drop(TRAININGDATA.tail(470).index,inplace=True)

TRAININGDATA=TRAININGDATA.replace([np.inf, -np.inf], np.nan)
TRAININGDATA = TRAININGDATA.dropna()
ROW_NUM = len(TRAININGDATA.index)

predictors = TRAININGDATA.iloc[:, PREDICTORSTART:PREDICTOREND]
targets_class = TRAININGDATA.iloc[:, TARGETCOLUMN_CLASS]

# Separate Test Data ( Independant Sample )
predictors_ind = TESTDATA.iloc[:, PREDICTORSTART:PREDICTOREND]
targets_ind_class = TESTDATA.iloc[:, TARGETCOLUMN_CLASS]

feature_columns = [tf.contrib.layers.real_valued_column(k) for k in 
list(predictors)]

# Build 3 layer DNN with 10, 20, 10 units respectively.
classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,
                                      hidden_units=[10, 20, 10],dropout=.7,
                                      n_classes=2,
                                      )
# Define the training inputs
train_input_fn = tf.estimator.inputs.pandas_input_fn(
  x=predictors,
  y=targets_class,
  num_epochs=None,
  shuffle=True)

# Train model.
classifier.train(input_fn=train_input_fn, steps=10)

# Define the test inputs
test_input_fn = tf.estimator.inputs.pandas_input_fn(
    x=predictors_ind,
    y=targets_ind_class,
    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))

export_dir="./tf/"
feature_spec = 
tf.feature_column.make_parse_example_spec(feature_columns)
input_receiver_fn = 
tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
classifier.export_savedmodel(export_dir,input_receiver_fn, as_text=False)

X = predictors.tail(1)
from tensorflow.contrib import predictor
predict_fn = predictor.from_saved_model(export_dir + "/1521675931/",    
    signature_def_key=None,
    signature_def=None,
    tags=None,
    graph=None)

X = X.to_dict("records")
print X
predictions = predict_fn(X)
print(predictions['scores'])

提前谢谢！ -蒂姆