190917_cnn_test.py

190917_cnn_test.py

from keras.models import Sequential
from keras.layers import Activation, Dense, Dropout,Conv2D,MaxPooling2D,Flatten
from keras.utils.np_utils import to_categorical
from keras.optimizers import Adagrad
from keras.optimizers import Adam
import numpy as np
from PIL import Image
import os
from sklearn.model_selection import train_test_split

# 学習用のデータを作る.
image_list = []
label_list = []

for dir in os.listdir("train"):
    if dir == ".DS_Store":
        continue

    dir1 = "./train/" + dir 
    label = 0

    if dir == "001":   
        label = 0
    elif dir == "002": 
        label = 1
    elif dir == "003": 
        label = 2
    elif dir == "004": 
        label = 3
    elif dir == "005": 
        label = 4

    for file in os.listdir(dir1):
        if file != ".DS_Store":
            label_list.append(label)
            filepath = dir1 + "/" + file
            image = Image.open(filepath)
#            image = Image.open(filepath).resize((14, 14)).convert("RGB")
            data = np.asarray(image)
            image_list.append(data)

image_list = np.array(image_list)
image_list = image_list.astype('float32')
image_list = image_list / 255.0
Y = to_categorical(label_list)

print(Y)
print(image_list)

X_train, X_test, y_train, y_test = train_test_split(image_list, Y, test_size=0.20)


# モデルを生成してニューラルネットを構築
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',input_shape=(14, 14, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (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(5))
model.add(Activation('softmax'))

opt = Adam(lr=0.001)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=["accuracy"])
model.fit(X_train, y_train, nb_epoch=50)

#print(image_list)