“““
In?order?to?decide?how?many?hidden?nodes?the?hidden?layer?should?have
split?up?the?data?set?into?training?and?testing?data?and?create?networks
with?various?hidden?node?counts?（5?10?15?...?45）?testing?the?performance
for?each.

The?best-performing?node?count?is?used?in?the?actual?system.?If?multiple?counts
perform?similarly?choose?the?smallest?count?for?a?smaller?network?with?fewer?computations.
“““

import?numpy?as?np
from?ocr?import?OCRNeuralNetwork
from?sklearn.cross_validation?import?train_test_split

def?test（data_matrix?data_labels?test_indices?nn）:
????avg_sum?=?0
????for?j?in?xrange（100）:
????????correct_guess_count?=?0
????????for?i?in?test_indices:
????????????test?=?data_matrix[i]
????????????prediction?=?nn.predict（test）
????????????if?data_labels[i]?==?prediction:
????????????????correct_guess_count?+=?1

????????avg_sum?+=?（correct_guess_count?/?float（len（test_indices）））
????return?avg_sum?/?100


#?Load?data?samples?and?labels?into?matrix
data_matrix?=?np.loadtxt（open（‘data.csv‘?‘rb‘）?delimiter?=?‘‘）.tolist（）
data_labels?=?np.loadtxt（open（‘dataLabels.csv‘?‘rb‘））.tolist（）

#?Create?training?and?testing?sets.
train_indices?test_indices?=?train_test_split（list（range（5000）））

print?“PERFORMANCE“
print?“-----------“

#?Try?various?number?of?hidden?nodes?and?see?what?performs?best
#?for?i?in?xrange（5?50?5）:
i=100
nn?=?OCRNeuralNetwork（i?data_matrix?data_labels?train_indices?False）
performance?=?str（test（data_matrix?data_labels?test_indices?nn））
print?“{i}?Hidden?Nodes:?{val}“.format（i=i?val=performance）

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????26337792??2018-06-29?08:14??data.csv
?????文件???????10000??2018-06-29?08:07??dataLabels.csv
?????文件??????248949??2018-07-02?17:53??jquery.js
?????文件????????1564??2018-07-02?16:56??neural_network_design.py
?????文件??????179055??2018-07-04?20:37??nn.json
?????文件????????6062??2018-07-04?20:45??ocr.html
?????文件???????10706??2018-07-04?20:44??ocr.js
?????文件????????5270??2018-07-02?13:27??ocr.py
?????文件????????3719??2018-07-04?15:10??server.py