質問編集履歴
2
エラーのプログラムをいれました
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -104,6 +104,54 @@
|
|
|
104
104
|
|
|
105
105
|
Google colaboratoryでプログラムをしています。
|
|
106
106
|
|
|
107
|
+
```
|
|
108
|
+
|
|
109
|
+
<ipython-input-4-6bf313ee2707> in learner(x_traina, x_trainb, x_trainc, y_train, x_testa, x_testb, x_testc, y_test, y_test1, flag)
|
|
110
|
+
|
|
111
|
+
44 x = Conv1D(filters, kernel_size2, strides=1, padding="valid",activation="relu")(x)
|
|
112
|
+
|
|
113
|
+
45 x = Conv1D(filters, kernel_size2, strides=1, padding="valid",activation="relu")(x)
|
|
114
|
+
|
|
115
|
+
---> 46 x = Flatten(x)
|
|
116
|
+
|
|
117
|
+
47 x = Model(inputs=input1, outputs=x)
|
|
118
|
+
|
|
119
|
+
48
|
|
120
|
+
|
|
121
|
+
|
|
122
|
+
|
|
123
|
+
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py in __init__(self, data_format, **kwargs)
|
|
124
|
+
|
|
125
|
+
629 def __init__(self, data_format=None, **kwargs):
|
|
126
|
+
|
|
127
|
+
630 super(Flatten, self).__init__(**kwargs)
|
|
128
|
+
|
|
129
|
+
--> 631 self.data_format = conv_utils.normalize_data_format(data_format)
|
|
130
|
+
|
|
131
|
+
632 self.input_spec = InputSpec(min_ndim=1)
|
|
132
|
+
|
|
133
|
+
633
|
|
134
|
+
|
|
135
|
+
|
|
136
|
+
|
|
137
|
+
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/conv_utils.py in normalize_data_format(value)
|
|
138
|
+
|
|
139
|
+
190 if value is None:
|
|
140
|
+
|
|
141
|
+
191 value = backend.image_data_format()
|
|
142
|
+
|
|
143
|
+
--> 192 data_format = value.lower()
|
|
144
|
+
|
|
145
|
+
193 if data_format not in {'channels_first', 'channels_last'}:
|
|
146
|
+
|
|
147
|
+
194 raise ValueError('The `data_format` argument must be one of '
|
|
148
|
+
|
|
149
|
+
|
|
150
|
+
|
|
151
|
+
AttributeError: 'Tensor' object has no attribute 'lower'
|
|
152
|
+
|
|
153
|
+
```
|
|
154
|
+
|
|
107
155
|
```Python
|
|
108
156
|
|
|
109
157
|
import tensorflow.keras
|
1
Flattenを書き加えました
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -99,3 +99,105 @@
|
|
|
99
99
|
model.fit([x_traina,x_trainb,x_trainc], y_train, epochs=1, batch_size=1, validation_data=([x_testa,x_testb,x_testc], y_test))
|
|
100
100
|
|
|
101
101
|
```
|
|
102
|
+
|
|
103
|
+
###編集した部分
|
|
104
|
+
|
|
105
|
+
Google colaboratoryでプログラムをしています。
|
|
106
|
+
|
|
107
|
+
```Python
|
|
108
|
+
|
|
109
|
+
import tensorflow.keras
|
|
110
|
+
|
|
111
|
+
from tensorflow.keras.layers import Input, concatenate, Dense, Reshape, Conv1D, Flatten
|
|
112
|
+
|
|
113
|
+
from tensorflow.keras.models import Model
|
|
114
|
+
|
|
115
|
+
|
|
116
|
+
|
|
117
|
+
input1 = Input(shape=(90,))
|
|
118
|
+
|
|
119
|
+
input2 = Input(shape=(96,))
|
|
120
|
+
|
|
121
|
+
input3 = Input(shape=(96,))
|
|
122
|
+
|
|
123
|
+
hidden1 = Reshape((90, 1), input_shape = (90,))(input1)
|
|
124
|
+
|
|
125
|
+
hidden2 = Reshape((96, 1), input_shape = (96,))(input2)
|
|
126
|
+
|
|
127
|
+
hidden3 = Reshape((96, 1), input_shape = (96,))(input3)
|
|
128
|
+
|
|
129
|
+
|
|
130
|
+
|
|
131
|
+
filters = 8
|
|
132
|
+
|
|
133
|
+
kernel_size1 = 6
|
|
134
|
+
|
|
135
|
+
kernel_size2 = 3
|
|
136
|
+
|
|
137
|
+
x = Conv1D(filters, kernel_size1, strides=3, padding="valid",activation="relu")(hidden1)
|
|
138
|
+
|
|
139
|
+
x = Conv1D(filters, kernel_size2, strides=1, padding="valid",activation="relu")(x)
|
|
140
|
+
|
|
141
|
+
x = Conv1D(filters, kernel_size2, strides=1, padding="valid",activation="relu")(x)
|
|
142
|
+
|
|
143
|
+
x = Flatten(x)
|
|
144
|
+
|
|
145
|
+
x = Model(inputs=input1, outputs=x)
|
|
146
|
+
|
|
147
|
+
|
|
148
|
+
|
|
149
|
+
y = Conv1D(filters, kernel_size1, strides=3, padding="valid",activation="relu")(hidden2)
|
|
150
|
+
|
|
151
|
+
y = Conv1D(filters, kernel_size2, strides=1, padding="valid",activation="relu")(y)
|
|
152
|
+
|
|
153
|
+
y = Conv1D(filters, kernel_size2, strides=1, padding="valid",activation="relu")(y)
|
|
154
|
+
|
|
155
|
+
y = Flatten(y)
|
|
156
|
+
|
|
157
|
+
y = Model(inputs=input2, outputs=y)
|
|
158
|
+
|
|
159
|
+
|
|
160
|
+
|
|
161
|
+
z = Conv1D(filters, kernel_size1, strides=3, padding="valid",activation="relu")(hidden3)
|
|
162
|
+
|
|
163
|
+
z = Conv1D(filters, kernel_size2, strides=1, padding="valid",activation="relu")(z)
|
|
164
|
+
|
|
165
|
+
z = Conv1D(filters, kernel_size2, strides=1, padding="valid",activation="relu")(z)
|
|
166
|
+
|
|
167
|
+
z = Flatten(z)
|
|
168
|
+
|
|
169
|
+
z = Model(inputs=input3, outputs=z)
|
|
170
|
+
|
|
171
|
+
|
|
172
|
+
|
|
173
|
+
combined = concatenate([x.output, y.output, z.output])
|
|
174
|
+
|
|
175
|
+
|
|
176
|
+
|
|
177
|
+
d = Dense(30, activation="relu")(combined)
|
|
178
|
+
|
|
179
|
+
|
|
180
|
+
|
|
181
|
+
d = Dense(3, activation="softmax")(d)
|
|
182
|
+
|
|
183
|
+
|
|
184
|
+
|
|
185
|
+
model = Model(inputs=[input1, input2, input3], outputs=d)
|
|
186
|
+
|
|
187
|
+
|
|
188
|
+
|
|
189
|
+
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
|
|
190
|
+
|
|
191
|
+
if flag:
|
|
192
|
+
|
|
193
|
+
pass
|
|
194
|
+
|
|
195
|
+
else:
|
|
196
|
+
|
|
197
|
+
model.load_weights('transfer0.h5')
|
|
198
|
+
|
|
199
|
+
model.fit([x_traina,x_trainb,x_trainc], y_train, epochs=1, batch_size=1, validation_data=([x_testa,x_testb,x_testc], y_test))
|
|
200
|
+
|
|
201
|
+
model.save_weights('transfer0.h5')
|
|
202
|
+
|
|
203
|
+
```
|