product_cnn.py

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

classes = ["shs","bns","amatsuji","screw","skyscraper","column"]
num_classes = len(classes)
image_size = 50

# メインの関数を定義する
def main():
X_train, X_test, y_train, y_test = np.load("d:/product_ai/product_aug.npy", allow_pickle=True)
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(3))
model.add(Activation('softmax'))
#最適化手法
opt = keras.optimizers.RMSprop(lr=0.0001, decay=1e-6)
model.compile(loss='categorical_crossentropy',optimizer=opt,metrics=['accuracy'])
#100回学習
model.fit(X, y, batch_size=32, epochs=100)
# モデルの保存
model.save('d:/product_ai/product_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()