深層学習を用いて画像判定AIを作ってみよう。（その3画像データの数値化・CNN）

from PIL import Image
import os,glob
import numpy as np
from sklearn import model_selection


image_path = "./image"
classes = os.listdir(image_path)

image_size = 50

X = []
Y = []
for index,classlabel in enumerate(classes):
    photos_dir = "./image/" + classlabel
    files = glob.glob(photos_dir + "/*.jpg")
    for i,file in enumerate(files):
        if i > 300: break
        image = Image.open(file)
        image = image.convert("RGB")
        image = image.resize((image_size, image_size))
        data = np.asarray(image)
        X.append(data)
        Y.append(index)

X = np.array(X)
Y = np.array(Y)

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, Y)
xy = (X_train, X_test, y_train, y_test)
np.save("./image_data.npy", xy)

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras.utils import np_utils
import keras
import numpy as np
import os

image_path = "./image"
classes = os.listdir(image_path)

num_classes = len(classes)
image_size = 50


def main():
    X_train, X_test, y_train, y_test = np.load("./image_data.npy")
    X_train = X_train.astype("float") / 256
    X_test = X_test.astype("float") / 256
    y_train = np_utils.to_categorical(y_train, num_classes)
    y_test = np_utils.to_categorical(y_test, num_classes)

    model = model_train(X_train, y_train)
    model_eval(model, X_test, y_test)

def model_train(X, y):
    model = Sequential()
    model.add(Conv2D(32,(3,3), padding='same',input_shape=X.shape[1:]))
    model.add(Activation('relu'))
    model.add(Conv2D(32,(3,3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Dropout(0.25))

    model.add(Conv2D(64,(3,3), padding='same'))
    model.add(Activation('relu'))
    model.add(Conv2D(64,(3,3)))
    model.add(Activation('relu'))
    model.add(MaxPooling2D(pool_size=(2,2)))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(512))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(num_classes))
    model.add(Activation('softmax'))

    opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
    model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])
    model.fit(X, y, batch_size=32, epochs=100)


    model.save('./image_cnn.h5')

    return model

def model_eval(model, X, y):
    scores = model.evaluate(X, y, verbose=1)
    print('Test Loss: ', scores[0])
    print('Test Accuracy: ', scores[1])

if __name__ == "__main__":
    main()