Q&A
いくつか未定義の変数があるようです。エラーが発生せず動作するコードを提示ください。
手書き画像画像の数字の認識のプログラムを作っています。
しかし、損失が下がらず、認識率も低いまま収束してしまいます。
損失を見た感じ学習をしていない感じです。原因がわからないのでわかる方いましたらご指摘お願いいたします。
発生している問題
Train on 160 samples, validate on 40 samples Epoch 1/30 160/160 [==============================] - 3s 21ms/step - loss: 2.3505 - accuracy: 0.0875 - val_loss: 2.2978 - val_accuracy: 0.1500 Epoch 2/30 160/160 [==============================] - 0s 2ms/step - loss: 2.3065 - accuracy: 0.0875 - val_loss: 2.3016 - val_accuracy: 0.0500 Epoch 3/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2994 - accuracy: 0.1250 - val_loss: 2.3053 - val_accuracy: 0.0500 Epoch 4/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2917 - accuracy: 0.1250 - val_loss: 2.3207 - val_accuracy: 0.0500 Epoch 5/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2993 - accuracy: 0.1250 - val_loss: 2.3048 - val_accuracy: 0.0500 Epoch 6/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2917 - accuracy: 0.1250 - val_loss: 2.2992 - val_accuracy: 0.0500 Epoch 7/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2929 - accuracy: 0.1250 - val_loss: 2.2986 - val_accuracy: 0.0500 Epoch 8/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2885 - accuracy: 0.1250 - val_loss: 2.2991 - val_accuracy: 0.0500 Epoch 9/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2877 - accuracy: 0.1250 - val_loss: 2.3033 - val_accuracy: 0.0500 Epoch 10/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2881 - accuracy: 0.1250 - val_loss: 2.3010 - val_accuracy: 0.0500 Epoch 11/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2890 - accuracy: 0.1250 - val_loss: 2.3040 - val_accuracy: 0.0500 Epoch 12/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2867 - accuracy: 0.1250 - val_loss: 2.2970 - val_accuracy: 0.0500 Epoch 13/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2861 - accuracy: 0.1250 - val_loss: 2.2954 - val_accuracy: 0.0500 Epoch 14/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2862 - accuracy: 0.1250 - val_loss: 2.2940 - val_accuracy: 0.0500 Epoch 15/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2904 - accuracy: 0.1250 - val_loss: 2.2979 - val_accuracy: 0.0500 Epoch 16/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2841 - accuracy: 0.1250 - val_loss: 2.2940 - val_accuracy: 0.0500 Epoch 17/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2855 - accuracy: 0.1312 - val_loss: 2.2938 - val_accuracy: 0.1250 Epoch 18/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2862 - accuracy: 0.1250 - val_loss: 2.2936 - val_accuracy: 0.1250 Epoch 19/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2888 - accuracy: 0.1312 - val_loss: 2.2957 - val_accuracy: 0.1250 Epoch 20/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2861 - accuracy: 0.1312 - val_loss: 2.2975 - val_accuracy: 0.1250 Epoch 21/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2871 - accuracy: 0.1187 - val_loss: 2.2974 - val_accuracy: 0.0500 Epoch 22/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2859 - accuracy: 0.1250 - val_loss: 2.2949 - val_accuracy: 0.0500 Epoch 23/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2853 - accuracy: 0.1312 - val_loss: 2.2952 - val_accuracy: 0.0500 Epoch 24/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2856 - accuracy: 0.1063 - val_loss: 2.2953 - val_accuracy: 0.0500 Epoch 25/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2850 - accuracy: 0.0875 - val_loss: 2.2968 - val_accuracy: 0.0500 Epoch 26/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2849 - accuracy: 0.1250 - val_loss: 2.3000 - val_accuracy: 0.0500 Epoch 27/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2854 - accuracy: 0.1250 - val_loss: 2.2984 - val_accuracy: 0.0500 Epoch 28/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2855 - accuracy: 0.1375 - val_loss: 2.2958 - val_accuracy: 0.1250 Epoch 29/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2843 - accuracy: 0.1312 - val_loss: 2.2929 - val_accuracy: 0.1250 Epoch 30/30 160/160 [==============================] - 0s 2ms/step - loss: 2.2853 - accuracy: 0.1312 - val_loss: 2.2921 - val_accuracy: 0.1250
該当のソースコード
import numpy as np import matplotlib.pyplot as plt import glob,os from sklearn.model_selection import train_test_split import cv2 from keras.layers import Convolution2D, BatchNormalization, Activation, MaxPooling2D, Add, Dropout, Flatten, Dense from keras import optimizers from keras.utils import to_categorical from keras import models from keras import layers x=[] z=[] #画像フォルダのパスを指定 path=os.path.dirname(os.path.abspath('__file__')) in_size=(28,28) out_size=10 path_one = path +'/Dataset/one' path_two = path +'/Dataset/two' path_three= path +'/Dataset/three' path_four = path +'/Dataset/four' file_1=glob.glob(path_one +'/*.jpg') file_2=glob.glob(path_two +'/*.jpg') file_3=glob.glob(path_three +'/*.jpg') file_4=glob.glob(path_four +'/*.jpg') print(len(file_1)) print(len(file_2)) print(len(file_3)) print(len(file_4)) print(len(file_5)) print(len(file_6)) print(len(file_7)) print(len(file_8)) print(len(file_9)) print(len(file_0)) #出力 26 20 23 21 11 19 20 16 22 22 def load_dir_1(path,label): for i in file_1: img=cv2.imread(i) img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img=cv2.resize(img,in_size) img=img/255.0 x.append(img) z.append(label) return [x,z] def load_dir_2(path,label): for i in file_2: img=cv2.imread(i) img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img=cv2.resize(img,in_size) img=img/255.0 x.append(img) z.append(label) return [x,z] def load_dir_3(path,label): for i in file_3: img=cv2.imread(i) img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img=cv2.resize(img,in_size) img=img/255.0 x.append(img) z.append(label) return [x,z] def load_dir_4(path,label): for i in file_4: img=cv2.imread(i) img=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img=cv2.resize(img,in_size) img=img/255.0 x.append(img) z.append(label) return [x,z] load_dir_1(path_one,1) load_dir_2(path_two,2) load_dir_3(path_three,3) load_dir_4(path_four,4) x=np.array(x) z=np.array(z) import keras x_train,x_test,z_train,z_test=train_test_split(x,z,test_size=0.2) x_train=x_train.reshape(len(x_train),28,28,1).astype('float32') x_test=x_test.reshape(len(x_test),28,28,1).astype('float32') z_train=keras.utils.np_utils.to_categorical(z_train.astype('int32'),10) z_test=keras.utils.np_utils.to_categorical(z_test.astype('int32'),10) model=models.Sequential() model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1))) model.add(layers.MaxPooling2D((2,2))) model.add(layers.Conv2D(64,(3,3),activation='relu')) model.add(layers.MaxPooling2D(2,2)) model.add(Dropout(0.2)) model.add(layers.Conv2D(64,(3,3),activation='relu')) model.add(layers.Flatten()) model.add(layers.Dense(64,activation='relu')) model.add(layers.Dense(10,activation='softmax')) from keras import optimizers model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) history = model.fit(x_train, z_train, epochs=30, batch_size=32,validation_data=(x_test, z_test))
修正しました
「NameError: name 'path_one_speckle' is not defined」にて動作しません。
質問に記載しているコードを「そのままコピペ」して実行してみてください（動作しますか？）
何度もすみません。
関数に入れるパスを間違えていたので直しました。
「x_train=x_train.reshape(len(x_train),28,28,1).astype('float32')」にて
「AttributeError: 'list' object has no attribute 'reshape'」が発生します。
質問に記載しているコードを「そのままコピペ」して実行してみてください（動作しますか？）
本当にすみません！
リストを配列に変えるのを忘れてたので修正しました。
とりあえず、修正された質問に記載しているコードを「そのままコピペ」して実行してみてください。
動作しますか？
必要なライブラリのインポートを修正しました。
動作しました。
入力画像に問題がありそうなので、まずはoneからzeroまでの各ファイル数、len(file_1)～len(file_0)をprintした結果を提示ください。
文字数制限を超えたので5以降の数字の読み込みの関数を削除しました。
回答1件
0
ベストアンサー
このあたりの知識に乏しいので的確な原因は指摘できませんが、元画像データに問題があると思われます。
当方にて各数字の画像数１０個、各数字の画像は全く同一のもの（「１」なら１０個すべて同じカタチ「１」の画像）で動作させた結果です。
あたりまえですが、正解率は1.0に収束しています。
PrainText
1Train on 80 samples, validate on 20 samples 2Epoch 1/30 380/80 [==============================] - 0s 2ms/step - loss: 2.3166 - accuracy: 0.1000 - val_loss: 2.3035 - val_accuracy: 0.0500 4Epoch 2/30 580/80 [==============================] - 0s 411us/step - loss: 2.2934 - accuracy: 0.1125 - val_loss: 2.3140 - val_accuracy: 0.1500 6Epoch 3/30 780/80 [==============================] - 0s 412us/step - loss: 2.2740 - accuracy: 0.1250 - val_loss: 2.3295 - val_accuracy: 0.1500 8Epoch 4/30 980/80 [==============================] - 0s 387us/step - loss: 2.2501 - accuracy: 0.2000 - val_loss: 2.3635 - val_accuracy: 0.0500 10Epoch 5/30 1180/80 [==============================] - 0s 388us/step - loss: 2.2377 - accuracy: 0.1500 - val_loss: 2.4008 - val_accuracy: 0.0000e+00 12Epoch 6/30 1380/80 [==============================] - 0s 412us/step - loss: 2.2185 - accuracy: 0.1250 - val_loss: 2.3816 - val_accuracy: 0.0000e+00 14Epoch 7/30 1580/80 [==============================] - 0s 400us/step - loss: 2.1710 - accuracy: 0.1875 - val_loss: 2.3371 - val_accuracy: 0.1000 16Epoch 8/30 1780/80 [==============================] - 0s 400us/step - loss: 2.1129 - accuracy: 0.3875 - val_loss: 2.2777 - val_accuracy: 0.1500 18Epoch 9/30 1980/80 [==============================] - 0s 362us/step - loss: 2.0528 - accuracy: 0.5000 - val_loss: 2.2032 - val_accuracy: 0.2500 20Epoch 10/30 2180/80 [==============================] - 0s 387us/step - loss: 1.9785 - accuracy: 0.4125 - val_loss: 2.1106 - val_accuracy: 0.0500 22Epoch 11/30 2380/80 [==============================] - 0s 375us/step - loss: 1.8595 - accuracy: 0.4500 - val_loss: 1.9630 - val_accuracy: 0.2500 24Epoch 12/30 2580/80 [==============================] - 0s 350us/step - loss: 1.7170 - accuracy: 0.6000 - val_loss: 1.7560 - val_accuracy: 0.5000 26Epoch 13/30 2780/80 [==============================] - 0s 375us/step - loss: 1.5362 - accuracy: 0.7750 - val_loss: 1.4589 - val_accuracy: 1.0000 28Epoch 14/30 2980/80 [==============================] - 0s 375us/step - loss: 1.3673 - accuracy: 0.8625 - val_loss: 1.2133 - val_accuracy: 1.0000 30Epoch 15/30 3180/80 [==============================] - 0s 362us/step - loss: 1.1369 - accuracy: 0.8375 - val_loss: 0.9378 - val_accuracy: 1.0000 32Epoch 16/30 3380/80 [==============================] - 0s 363us/step - loss: 0.9292 - accuracy: 0.9125 - val_loss: 0.7440 - val_accuracy: 0.9000 34Epoch 17/30 3580/80 [==============================] - 0s 363us/step - loss: 0.7212 - accuracy: 0.9375 - val_loss: 0.5730 - val_accuracy: 1.0000 36Epoch 18/30 3780/80 [==============================] - 0s 363us/step - loss: 0.6000 - accuracy: 0.8875 - val_loss: 0.4320 - val_accuracy: 1.0000 38Epoch 19/30 3980/80 [==============================] - 0s 363us/step - loss: 0.4228 - accuracy: 0.9875 - val_loss: 0.3599 - val_accuracy: 0.9000 40Epoch 20/30 4180/80 [==============================] - 0s 363us/step - loss: 0.4129 - accuracy: 0.8625 - val_loss: 0.2648 - val_accuracy: 1.0000 42Epoch 21/30 4380/80 [==============================] - 0s 363us/step - loss: 0.3232 - accuracy: 0.9375 - val_loss: 0.2445 - val_accuracy: 0.9000 44Epoch 22/30 4580/80 [==============================] - 0s 375us/step - loss: 0.2665 - accuracy: 0.9375 - val_loss: 0.1590 - val_accuracy: 1.0000 46Epoch 23/30 4780/80 [==============================] - 0s 363us/step - loss: 0.2104 - accuracy: 0.9625 - val_loss: 0.1119 - val_accuracy: 1.0000 48Epoch 24/30 4980/80 [==============================] - 0s 375us/step - loss: 0.1193 - accuracy: 1.0000 - val_loss: 0.1258 - val_accuracy: 1.0000 50Epoch 25/30 5180/80 [==============================] - 0s 363us/step - loss: 0.1458 - accuracy: 0.9875 - val_loss: 0.0807 - val_accuracy: 1.0000 52Epoch 26/30 5380/80 [==============================] - 0s 350us/step - loss: 0.0805 - accuracy: 1.0000 - val_loss: 0.0575 - val_accuracy: 1.0000 54Epoch 27/30 5580/80 [==============================] - 0s 350us/step - loss: 0.0734 - accuracy: 1.0000 - val_loss: 0.0588 - val_accuracy: 1.0000 56Epoch 28/30 5780/80 [==============================] - 0s 338us/step - loss: 0.0585 - accuracy: 1.0000 - val_loss: 0.0338 - val_accuracy: 1.0000 58Epoch 29/30 5980/80 [==============================] - 0s 338us/step - loss: 0.0635 - accuracy: 1.0000 - val_loss: 0.0279 - val_accuracy: 1.0000 60Epoch 30/30 6180/80 [==============================] - 0s 350us/step - loss: 0.0384 - accuracy: 1.0000 - val_loss: 0.0375 - val_accuracy: 1.0000
投稿2020/07/14 07:09
総合スコア38341
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