CNN--Keras手書きデジタル識別

#!/usr/bin/env python3
''' ： 0:10000000
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'''


import numpy as np

import keras

from keras.datasets import mnist

from keras.utils import np_utils

from keras.models import Sequential

from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Flatten

from keras.optimizers import Adam

from keras.callbacks import ModelCheckpoint

import skimage.io as io



(X_train, y_train), (X_test, y_test) = mnist.load_data()





X_train = X_train.reshape(-1, 28, 28, 1)  # normalize

X_test = X_test.reshape(-1, 28, 28, 1)      # normalize

X_train = X_train / 255

X_test = X_test / 255

y_train = np_utils.to_categorical(y_train, num_classes=10)

y_test = np_utils.to_categorical(y_test, num_classes=10)



model_checkpoint = ModelCheckpoint('lenet5_membrane.hdf5', monitor='loss',verbose=1, save_best_only=True)



model = Sequential()

model.add(Conv2D(input_shape=(28, 28, 1), kernel_size=(5, 5), filters=20, activation='relu'))

model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='same'))



model.add(Conv2D(kernel_size=(5, 5), filters=50,  activation='relu', padding='same'))

model.add(MaxPooling2D(pool_size=(2,2), strides=2, padding='same'))



model.add(Flatten())

model.add(Dense(500, activation='relu'))

model.add(Dense(10, activation='softmax'))

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])





print('Training')

model.fit(X_train, y_train, epochs=2, batch_size=32,callbacks=[model_checkpoint])



print('
Testing')

model.load_weights('lenet5_membrane.hdf5')

loss, accuracy = model.evaluate(X_test, y_test)



print('
test loss: ', loss)

print('
test accuracy: ', accuracy)









def load_data(address):

    im = io.imread(address)

    image_list = []

    for item in im:

        row = []

        for i in item:

            row.append([i[0]])

        image_list.append(row)

    array = np.array(image_list)

    array = array/255

    image = np.expand_dims(array, axis=0)

    return image



address_list = ['0.jpg','1.jpg','2.jpg','3.jpg','4.jpg','5.jpg','6.jpg','7.jpg','8.jpg','9.jpg']





for address in address_list:

    image = load_data(address)

    predictions = model.predict_classes(image)

    print(' :'+str(predictions[0]))