質問編集履歴
3
コードを間違えたため
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -84,122 +84,6 @@
|
|
|
84
84
|
|
|
85
85
|
```ここに言語を入力
|
|
86
86
|
|
|
87
|
-
import numpy as np
|
|
88
|
-
|
|
89
|
-
import tensorflow as tf
|
|
90
|
-
|
|
91
|
-
import keras
|
|
92
|
-
|
|
93
|
-
|
|
94
|
-
|
|
95
|
-
##重み（特殊な正規分布から発生する値）
|
|
96
|
-
|
|
97
|
-
def weight(shape = []):
|
|
98
|
-
|
|
99
|
-
initial = tf.truncated_normal(shape, stddev = 0.01)
|
|
100
|
-
|
|
101
|
-
return tf.Variable(initial)
|
|
102
|
-
|
|
103
|
-
|
|
104
|
-
|
|
105
|
-
##バイアス
|
|
106
|
-
|
|
107
|
-
def bias(dtype = tf.float32, shape = []):
|
|
108
|
-
|
|
109
|
-
initial = tf.zeros(shape, dtype = dtype)
|
|
110
|
-
|
|
111
|
-
return tf.Variable(initial)
|
|
112
|
-
|
|
113
|
-
|
|
114
|
-
|
|
115
|
-
##損失関数（交叉エントロピー）
|
|
116
|
-
|
|
117
|
-
def loss(t, f):
|
|
118
|
-
|
|
119
|
-
cross_entropy = tf.reduce_mean(-tf.reduce_sum(t * tf.log(f)))
|
|
120
|
-
|
|
121
|
-
return cross_entropy
|
|
122
|
-
|
|
123
|
-
|
|
124
|
-
|
|
125
|
-
##正確性の尺度
|
|
126
|
-
|
|
127
|
-
def accuracy(t, f):
|
|
128
|
-
|
|
129
|
-
correct_prediction = tf.equal(tf.argmax(t, 1), tf.argmax(f, 1))
|
|
130
|
-
|
|
131
|
-
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
|
|
132
|
-
|
|
133
|
-
return accuracy
|
|
134
|
-
|
|
135
|
-
|
|
136
|
-
|
|
137
|
-
Q = 60
|
|
138
|
-
|
|
139
|
-
P = 60
|
|
140
|
-
|
|
141
|
-
R = 1
|
|
142
|
-
|
|
143
|
-
|
|
144
|
-
|
|
145
|
-
sess = tf.InteractiveSession()
|
|
146
|
-
|
|
147
|
-
|
|
148
|
-
|
|
149
|
-
X = tf.placeholder(dtype = tf.float32, shape = [None, Q])
|
|
150
|
-
|
|
151
|
-
t = tf.placeholder(dtype = tf.float32, shape = [None, R])
|
|
152
|
-
|
|
153
|
-
|
|
154
|
-
|
|
155
|
-
##隠れ層
|
|
156
|
-
|
|
157
|
-
##活性化関数はシグモイド関数
|
|
158
|
-
|
|
159
|
-
W1 = weight(shape = [Q, P])
|
|
160
|
-
|
|
161
|
-
b1 = bias(shape = [P])
|
|
162
|
-
|
|
163
|
-
f1 = tf.matmul(X, W1) + b1
|
|
164
|
-
|
|
165
|
-
sigm = tf.nn.sigmoid(f1)
|
|
166
|
-
|
|
167
|
-
|
|
168
|
-
|
|
169
|
-
##出力層
|
|
170
|
-
|
|
171
|
-
##fはソフトマックス関数（出力を0～1に制限）
|
|
172
|
-
|
|
173
|
-
W2 = weight(shape = [P, R])
|
|
174
|
-
|
|
175
|
-
b2 = bias(shape = [R])
|
|
176
|
-
|
|
177
|
-
f2 = tf.matmul(sigm, W2) + b2
|
|
178
|
-
|
|
179
|
-
f = tf.nn.softmax(f2)
|
|
180
|
-
|
|
181
|
-
|
|
182
|
-
|
|
183
|
-
loss = loss(t, f)
|
|
184
|
-
|
|
185
|
-
acc = accuracy(t, f)
|
|
186
|
-
|
|
187
|
-
|
|
188
|
-
|
|
189
|
-
##BP法
|
|
190
|
-
|
|
191
|
-
optimizer = tf.train.GradientDescentOptimizer(learning_rate = 0.95)
|
|
192
|
-
|
|
193
|
-
train_step = optimizer.minimize(loss)
|
|
194
|
-
|
|
195
|
-
|
|
196
|
-
|
|
197
|
-
##Adam
|
|
198
|
-
|
|
199
|
-
##train_step = tf.train.AdamOptimizer().minimize(loss)
|
|
200
|
-
|
|
201
|
-
|
|
202
|
-
|
|
203
87
|
##学習を実行
|
|
204
88
|
|
|
205
89
|
with tf.Session() as sess:
|
2
追記
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -76,7 +76,7 @@
|
|
|
76
76
|
|
|
77
77
|
入力データは（60,120）で60次元のデータを120個用意しており，内100個を学習データ，残り20個をテストデータにしています
|
|
78
78
|
|
|
79
|
-
出力データは（1
|
|
79
|
+
出力データは（120，1）で1次元データを120個用意し，内100個を学習データ，残り20個をテストデータにしています
|
|
80
80
|
|
|
81
81
|
これらは，記述コードより前でcsvファイルから読み込み済みです
|
|
82
82
|
|
1
コードを間違えたため
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -264,7 +264,7 @@
|
|
|
264
264
|
|
|
265
265
|
|
|
266
266
|
|
|
267
|
-
|
|
267
|
+
sff_idx = np.random.permutation(num_data)
|
|
268
268
|
|
|
269
269
|
|
|
270
270
|
|