質問編集履歴
6
文字数制限
test
CHANGED
File without changes
|
test
CHANGED
@@ -190,17 +190,7 @@
|
|
190
190
|
|
191
191
|
path_four = path +'/Dataset/four'
|
192
192
|
|
193
|
-
|
193
|
+
|
194
|
-
|
195
|
-
path_six = path +'/Dataset/six'
|
196
|
-
|
197
|
-
path_seven= path +'/Dataset/seven'
|
198
|
-
|
199
|
-
path_eight= path +'/Dataset/eight'
|
200
|
-
|
201
|
-
path_nine = path +'/Dataset/nine'
|
202
|
-
|
203
|
-
path_zero = path +'/Dataset/zero'
|
204
194
|
|
205
195
|
|
206
196
|
|
@@ -212,17 +202,7 @@
|
|
212
202
|
|
213
203
|
file_4=glob.glob(path_four +'/*.jpg')
|
214
204
|
|
215
|
-
|
205
|
+
|
216
|
-
|
217
|
-
file_6=glob.glob(path_six +'/*.jpg')
|
218
|
-
|
219
|
-
file_7=glob.glob(path_seven +'/*.jpg')
|
220
|
-
|
221
|
-
file_8=glob.glob(path_eight +'/*.jpg')
|
222
|
-
|
223
|
-
file_9=glob.glob(path_nine +'/*.jpg')
|
224
|
-
|
225
|
-
file_0=glob.glob(path_zero +'/*.jpg')
|
226
206
|
|
227
207
|
|
228
208
|
|
@@ -392,28 +372,6 @@
|
|
392
372
|
|
393
373
|
|
394
374
|
|
395
|
-
load_dir_5(path_five,5)
|
396
|
-
|
397
|
-
|
398
|
-
|
399
|
-
load_dir_6(path_six,6)
|
400
|
-
|
401
|
-
|
402
|
-
|
403
|
-
load_dir_7(path_seven,7)
|
404
|
-
|
405
|
-
|
406
|
-
|
407
|
-
load_dir_8(path_eight,8)
|
408
|
-
|
409
|
-
|
410
|
-
|
411
|
-
load_dir_9(path_nine,9)
|
412
|
-
|
413
|
-
|
414
|
-
|
415
|
-
load_dir_0(path_zero,0)
|
416
|
-
|
417
375
|
|
418
376
|
|
419
377
|
x=np.array(x)
|
5
書籍の改善
test
CHANGED
File without changes
|
test
CHANGED
@@ -226,6 +226,50 @@
|
|
226
226
|
|
227
227
|
|
228
228
|
|
229
|
+
print(len(file_1))
|
230
|
+
|
231
|
+
print(len(file_2))
|
232
|
+
|
233
|
+
print(len(file_3))
|
234
|
+
|
235
|
+
print(len(file_4))
|
236
|
+
|
237
|
+
print(len(file_5))
|
238
|
+
|
239
|
+
print(len(file_6))
|
240
|
+
|
241
|
+
print(len(file_7))
|
242
|
+
|
243
|
+
print(len(file_8))
|
244
|
+
|
245
|
+
print(len(file_9))
|
246
|
+
|
247
|
+
print(len(file_0))
|
248
|
+
|
249
|
+
|
250
|
+
|
251
|
+
#出力
|
252
|
+
|
253
|
+
26
|
254
|
+
|
255
|
+
20
|
256
|
+
|
257
|
+
23
|
258
|
+
|
259
|
+
21
|
260
|
+
|
261
|
+
11
|
262
|
+
|
263
|
+
19
|
264
|
+
|
265
|
+
20
|
266
|
+
|
267
|
+
16
|
268
|
+
|
269
|
+
22
|
270
|
+
|
271
|
+
22
|
272
|
+
|
229
273
|
|
230
274
|
|
231
275
|
|
@@ -328,137 +372,7 @@
|
|
328
372
|
|
329
373
|
|
330
374
|
|
331
|
-
|
375
|
+
|
332
|
-
|
333
|
-
|
334
|
-
|
335
|
-
for i in file_5:
|
336
|
-
|
337
|
-
img=cv2.imread(i)
|
338
|
-
|
339
|
-
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
340
|
-
|
341
|
-
img=cv2.resize(img,in_size)
|
342
|
-
|
343
|
-
img=img/255.0
|
344
|
-
|
345
|
-
x.append(img)
|
346
|
-
|
347
|
-
z.append(label)
|
348
|
-
|
349
|
-
return [x,z]
|
350
|
-
|
351
|
-
|
352
|
-
|
353
|
-
def load_dir_6(path,label):
|
354
|
-
|
355
|
-
|
356
|
-
|
357
|
-
for i in file_6:
|
358
|
-
|
359
|
-
img=cv2.imread(i)
|
360
|
-
|
361
|
-
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
362
|
-
|
363
|
-
img=cv2.resize(img,in_size)
|
364
|
-
|
365
|
-
img=img/255.0
|
366
|
-
|
367
|
-
x.append(img)
|
368
|
-
|
369
|
-
z.append(label)
|
370
|
-
|
371
|
-
return [x,z]
|
372
|
-
|
373
|
-
|
374
|
-
|
375
|
-
def load_dir_7(path,label):
|
376
|
-
|
377
|
-
|
378
|
-
|
379
|
-
for i in file_7:
|
380
|
-
|
381
|
-
img=cv2.imread(i)
|
382
|
-
|
383
|
-
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
384
|
-
|
385
|
-
img=cv2.resize(img,in_size)
|
386
|
-
|
387
|
-
img=img/255.0
|
388
|
-
|
389
|
-
x.append(img)
|
390
|
-
|
391
|
-
z.append(label)
|
392
|
-
|
393
|
-
return [x,z]
|
394
|
-
|
395
|
-
|
396
|
-
|
397
|
-
def load_dir_8(path,label):
|
398
|
-
|
399
|
-
|
400
|
-
|
401
|
-
for i in file_8:
|
402
|
-
|
403
|
-
img=cv2.imread(i)
|
404
|
-
|
405
|
-
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
406
|
-
|
407
|
-
img=cv2.resize(img,in_size)
|
408
|
-
|
409
|
-
img=img/255.0
|
410
|
-
|
411
|
-
x.append(img)
|
412
|
-
|
413
|
-
z.append(label)
|
414
|
-
|
415
|
-
return [x,z]
|
416
|
-
|
417
|
-
|
418
|
-
|
419
|
-
def load_dir_9(path,label):
|
420
|
-
|
421
|
-
|
422
|
-
|
423
|
-
for i in file_9:
|
424
|
-
|
425
|
-
img=cv2.imread(i)
|
426
|
-
|
427
|
-
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
428
|
-
|
429
|
-
img=cv2.resize(img,in_size)
|
430
|
-
|
431
|
-
img=img/255.0
|
432
|
-
|
433
|
-
x.append(img)
|
434
|
-
|
435
|
-
z.append(label)
|
436
|
-
|
437
|
-
return [x,z]
|
438
|
-
|
439
|
-
|
440
|
-
|
441
|
-
|
442
|
-
|
443
|
-
def load_dir_0(path,label):
|
444
|
-
|
445
|
-
|
446
|
-
|
447
|
-
for i in file_0:
|
448
|
-
|
449
|
-
img=cv2.imread(i)
|
450
|
-
|
451
|
-
img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
|
452
|
-
|
453
|
-
img=cv2.resize(img,in_size)
|
454
|
-
|
455
|
-
img=img/255.0
|
456
|
-
|
457
|
-
x.append(img)
|
458
|
-
|
459
|
-
z.append(label)
|
460
|
-
|
461
|
-
return [x,z]
|
462
376
|
|
463
377
|
|
464
378
|
|
4
必要なライブラリのインポートを行った
test
CHANGED
File without changes
|
test
CHANGED
@@ -152,6 +152,18 @@
|
|
152
152
|
|
153
153
|
import cv2
|
154
154
|
|
155
|
+
from keras.layers import Convolution2D, BatchNormalization, Activation, MaxPooling2D, Add, Dropout, Flatten, Dense
|
156
|
+
|
157
|
+
from keras import optimizers
|
158
|
+
|
159
|
+
from keras.utils import to_categorical
|
160
|
+
|
161
|
+
|
162
|
+
|
163
|
+
from keras import models
|
164
|
+
|
165
|
+
from keras import layers
|
166
|
+
|
155
167
|
|
156
168
|
|
157
169
|
x=[]
|
@@ -186,7 +198,7 @@
|
|
186
198
|
|
187
199
|
path_eight= path +'/Dataset/eight'
|
188
200
|
|
189
|
-
path_nine = path +'/Dataset/ine'
|
201
|
+
path_nine = path +'/Dataset/nine'
|
190
202
|
|
191
203
|
path_zero = path +'/Dataset/zero'
|
192
204
|
|
@@ -312,9 +324,9 @@
|
|
312
324
|
|
313
325
|
return [x,z]
|
314
326
|
|
315
|
-
|
316
|
-
|
317
|
-
|
327
|
+
|
328
|
+
|
329
|
+
|
318
330
|
|
319
331
|
def load_dir_5(path,label):
|
320
332
|
|
@@ -336,7 +348,7 @@
|
|
336
348
|
|
337
349
|
return [x,z]
|
338
350
|
|
339
|
-
|
351
|
+
|
340
352
|
|
341
353
|
def load_dir_6(path,label):
|
342
354
|
|
@@ -402,7 +414,7 @@
|
|
402
414
|
|
403
415
|
return [x,z]
|
404
416
|
|
405
|
-
|
417
|
+
|
406
418
|
|
407
419
|
def load_dir_9(path,label):
|
408
420
|
|
@@ -424,9 +436,9 @@
|
|
424
436
|
|
425
437
|
return [x,z]
|
426
438
|
|
427
|
-
|
428
|
-
|
429
|
-
|
439
|
+
|
440
|
+
|
441
|
+
|
430
442
|
|
431
443
|
def load_dir_0(path,label):
|
432
444
|
|
@@ -552,6 +564,4 @@
|
|
552
564
|
|
553
565
|
|
554
566
|
|
555
|
-
|
556
|
-
|
557
567
|
```
|
3
test
CHANGED
File without changes
|
test
CHANGED
@@ -490,6 +490,12 @@
|
|
490
490
|
|
491
491
|
|
492
492
|
|
493
|
+
x=np.array(x)
|
494
|
+
|
495
|
+
z=np.array(z)
|
496
|
+
|
497
|
+
|
498
|
+
|
493
499
|
|
494
500
|
|
495
501
|
import keras
|
2
test
CHANGED
File without changes
|
test
CHANGED
@@ -450,43 +450,43 @@
|
|
450
450
|
|
451
451
|
|
452
452
|
|
453
|
-
load_dir_1(path_one
|
453
|
+
load_dir_1(path_one,1)
|
454
|
-
|
455
|
-
|
456
|
-
|
454
|
+
|
455
|
+
|
456
|
+
|
457
|
-
load_dir_2(path_two
|
457
|
+
load_dir_2(path_two,2)
|
458
|
-
|
459
|
-
|
460
|
-
|
458
|
+
|
459
|
+
|
460
|
+
|
461
|
-
load_dir_3(path_three
|
461
|
+
load_dir_3(path_three,3)
|
462
|
-
|
463
|
-
|
464
|
-
|
462
|
+
|
463
|
+
|
464
|
+
|
465
|
-
load_dir_4(path_four
|
465
|
+
load_dir_4(path_four,4)
|
466
|
-
|
467
|
-
|
468
|
-
|
466
|
+
|
467
|
+
|
468
|
+
|
469
|
-
load_dir_5(path_five
|
469
|
+
load_dir_5(path_five,5)
|
470
|
-
|
471
|
-
|
472
|
-
|
470
|
+
|
471
|
+
|
472
|
+
|
473
|
-
load_dir_6(path_six
|
473
|
+
load_dir_6(path_six,6)
|
474
|
-
|
475
|
-
|
476
|
-
|
474
|
+
|
475
|
+
|
476
|
+
|
477
|
-
load_dir_7(path_seven
|
477
|
+
load_dir_7(path_seven,7)
|
478
|
-
|
479
|
-
|
480
|
-
|
478
|
+
|
479
|
+
|
480
|
+
|
481
|
-
load_dir_8(path_eight
|
481
|
+
load_dir_8(path_eight,8)
|
482
|
-
|
483
|
-
|
484
|
-
|
482
|
+
|
483
|
+
|
484
|
+
|
485
|
-
load_dir_9(path_nine
|
485
|
+
load_dir_9(path_nine,9)
|
486
|
-
|
487
|
-
|
488
|
-
|
486
|
+
|
487
|
+
|
488
|
+
|
489
|
-
load_dir_0(path_zero
|
489
|
+
load_dir_0(path_zero,0)
|
490
490
|
|
491
491
|
|
492
492
|
|
1
test
CHANGED
File without changes
|
test
CHANGED
@@ -494,13 +494,13 @@
|
|
494
494
|
|
495
495
|
import keras
|
496
496
|
|
497
|
-
x_train,x_test,
|
497
|
+
x_train,x_test,z_train,z_test=train_test_split(x,z,test_size=0.2)
|
498
|
-
|
499
|
-
|
500
|
-
|
498
|
+
|
499
|
+
|
500
|
+
|
501
|
-
x_train=x_train.reshape(len(x_train),2
|
501
|
+
x_train=x_train.reshape(len(x_train),28,28,1).astype('float32')
|
502
|
-
|
502
|
+
|
503
|
-
x_test=x_test.reshape(len(x_test),2
|
503
|
+
x_test=x_test.reshape(len(x_test),28,28,1).astype('float32')
|
504
504
|
|
505
505
|
|
506
506
|
|