AIのモデルの精度

Epoch 1/100
10/10 [==============================] - 2s 163ms/step - loss: 0.7666 - accuracy: 0.5100 - val_loss: 0.6794 - val_accuracy: 0.5400
Epoch 2/100
10/10 [==============================] - 1s 97ms/step - loss: 0.6429 - accuracy: 0.5900 - val_loss: 0.5977 - val_accuracy: 0.6600
Epoch 3/100
10/10 [==============================] - 1s 106ms/step - loss: 0.5978 - accuracy: 0.6289 - val_loss: 0.5948 - val_accuracy: 0.7800
中略
Epoch 70/100
10/10 [==============================] - 1s 130ms/step - loss: 0.0772 - accuracy: 0.9700 - val_loss: 0.8683 - val_accuracy: 0.8000
中略
Epoch 95/100
10/10 [==============================] - 1s 125ms/step - loss: 0.0708 - accuracy: 0.9600 - val_loss: 0.8159 - val_accuracy: 0.8200
Epoch 96/100
10/10 [==============================] - 1s 125ms/step - loss: 0.0186 - accuracy: 1.0000 - val_loss: 1.7726 - val_accuracy: 0.6800
Epoch 97/100
10/10 [==============================] - 1s 120ms/step - loss: 0.0107 - accuracy: 1.0000 - val_loss: 1.1480 - val_accuracy: 0.7800
Epoch 98/100
10/10 [==============================] - 1s 101ms/step - loss: 0.0084 - accuracy: 1.0000 - val_loss: 1.0915 - val_accuracy: 0.7800
Epoch 99/100
10/10 [==============================] - 1s 110ms/step - loss: 0.0302 - accuracy: 0.9897 - val_loss: 1.3684 - val_accuracy: 0.7200
Epoch 100/100
10/10 [==============================] - 1s 105ms/step - loss: 0.0354 - accuracy: 0.9800 - val_loss: 1.4646 - val_accuracy: 0.7200

from keras.models import Sequential
from keras.layers import Activation, Dense
from keras.layers import Conv2D, MaxPooling2D, Flatten
from keras.preprocessing.image import ImageDataGenerator


def main():
    model = Sequential()
    model.add(Conv2D(64, (3, 3), input_shape=(64, 64, 3)))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(64, (3, 3)))
    model.add(Activation("relu"))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(256))
    model.add(Activation("relu"))
    model.add(Dense(2))
    model.add(Activation("softmax"))
    # model.summary()
    model.compile(
            optimizer="adam",
            loss="categorical_crossentropy",
            metrics=["accuracy"])
    train_datagen = ImageDataGenerator(rescale=1./255)
    test_datagen = ImageDataGenerator(rescale=1./255)
    train_generator = train_datagen.flow_from_directory(
            "data/train",
            target_size=(64, 64),
            batch_size=10)
    validation_generator = test_datagen.flow_from_directory(
            "data/validation",
            target_size=(64, 64),
            batch_size=10)
    model.fit_generator(
            train_generator,
            epochs=100,
            steps_per_epoch=10,
            validation_data=validation_generator,
            validation_steps=5)
    model.save("model.h5")


if __name__ == '__main__':
    main()

import sys
from PIL import Image
from keras.models import load_model
import numpy as np


def main():
    name = sys.argv[1]
    # print(name)
    image = Image.open(name)
    image = image.resize((64, 64))
    image.show()
    model = load_model("model.h5")
    np_image = np.array(image)
    np_image = np_image / 255
    np_image = np_image[np.newaxis, :, :, :]
    result = model.predict(np_image)
    # print(result)
    if result[0][0] > result[0][1]:
        print("しいたけ", end=" ")
        print(result[0][0]*100, end="%")
    else:
        print("ツキヨタケ", end=" ")
        print(result[0][1]*100, end="%")


if __name__ == '__main__':
    main()