MNISTを用いた認識をwebの資料などを見ながら試してみました
そこで自分が書いた文字も認識できるのかをやってみようと思い、以下のサイトを見ながら
動作するかどうか試してみました
MNIST vs 俺 (俺の手書き文字を正しく認識できるか)
下部に記載している②のコードを実行しようとすると以下のようにエラーとなります
ImportError Traceback (most recent call last) <ipython-input-82-c08a6616303b> in <module>() 2 import numpy as np 3 from keras.models import load_model ----> 4 from keras.preprocessing.image import array_to_img, img_to_array,list_pictures, load_img 5 6 ImportError: cannot import name 'list_pictures'
from keras.preprocessing.image import array_to_img, img_to_array,list_pictures, load_img
↑からlist_picturesだけを取り除くとエラーは発生しないのですが
kerasのリファレンス等を見てもlist_picturesというものは発見できず行き詰っています
kerasのバージョンは2.2.0です
更新などで使えなくなったのでしょうか?
初歩的な質問ですがアドバイス宜しくお願い致します
①kerasのサンプルコード
python
'''Trains a simple convnet on the MNIST dataset. Gets to 99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning). 16 seconds per epoch on a GRID K520 GPU. ''' from __future__ import print_function import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras import backend as K batch_size = 128 num_classes = 10 epochs = 10 # input image dimensions img_rows, img_cols = 28, 28 # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() if K.image_data_format() == 'channels_first': x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation='softmax')) model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy']) model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) model.save('mnist_model.h5')
②自分で書いた文字を評価するコード
python
# coding:utf-8 import keras import numpy as np from keras.models import load_model from keras.preprocessing.image import array_to_img, img_to_array,list_pictures, load_img model = load_model('mnist_model.h5') for picture in list_pictures(r'C:\Users\Desktop\pic'): X = [] img = img_to_array( load_img(picture, target_size=(28, 28), grayscale=True)) X.append(img) X = np.asarray(X) X = X.astype('float32') X = X / 255.0 features = model.predict(X) print('----------') print(picture) print(features.argmax()) print('----------')
まだ回答がついていません
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