「機械学習」タグの記事が3件件あります

model.save('mnist_model.h5')

## coding:utf-8

import keras
import numpy as np
from keras.models import load_model
from keras.preprocessing.image import array_to_img, img_to_array,load_img
import os
import re

model = load_model('mnist_model.h5')

def list_pictures(directory, ext='jpg|jpeg|bmp|png|ppm'):
    return [os.path.join(root, f)
            for root, _, files in os.walk(directory) for f in files
            if re.match(r'([\w]+\.(?:' + ext + '))', f.lower())]

for picture in list_pictures('./tegaki/'):
    X = []
    img = img_to_array(
        load_img(picture, target_size=(28, 28), grayscale=True))
    X.append(img)

    X = np.asarray(X)
    X = X.astype('float32')
    X = X / 255.0

    features = model.predict(X)

    print('----------')
    print(picture)
    print(features.argmax())
    print('----------')

    # 拡張する際の設定
    generator = ImageDataGenerator(
                    # rotation_range=90, # 90°まで回転
                    width_shift_range=0.1, # 水平方向にランダムでシフト
                    height_shift_range=0.1, # 垂直方向にランダムでシフト
                    channel_shift_range=50.0, # 色調をランダム変更
                    shear_range=0.39, # 斜め方向(pi/8まで)に引っ張る
                    horizontal_flip=False, # 垂直方向にランダムで反転
                    vertical_flip=False # 水平方向にランダムで反転
                    )

## マルチクラス分類問題の場合
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

## 2値分類問題の場合
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

## 前回のコード（参考元のコピペです）
model.compile(loss='categorical_crossentropy',
              optimizer='SGD',
              metrics=['accuracy'])

img = img_to_array(load_img(picture, target_size=(224,224), grayscale=True))

model = MobileNet(include_top=True, weights=None, classes=2,input_shape=(224, 224, 1))

## coding:utf-8

from time import sleep

from keras.applications import mobilenet

from keras.models import load_model
from keras.preprocessing import image
from keras.preprocessing.image import img_to_array, load_img
import numpy as np
from keras.utils import np_utils

model = load_model('0212_my_model_0216.h5', custom_objects={
                   'relu6': mobilenet.relu6,
                   'DepthwiseConv2D': mobilenet.DepthwiseConv2D})

while True:
    X = []

    picture = '/home/pi/motion/capture/lastsnap.jpg'
    img = img_to_array(load_img(picture, target_size=(224, 224), grayscale=True))
    X.append(img)

    X = np.asarray(X)
    X = X.astype('float32')
    X = X / 255.0

    features = model.predict(X)

    print(features)
    if features[0][0] > features[0][1]:
        print('200g以下！')
    else:
        print('200g以上！')

    sleep(5)

## coding:utf-8

from keras.applications.mobilenet import MobileNet

from keras.preprocessing import image
from keras.preprocessing.image import array_to_img, img_to_array, list_pictures, load_img
import numpy as np
from keras.utils import np_utils
from sklearn.model_selection import train_test_split

X = []
Y = []

## 対象Aの画像
for picture in list_pictures('./test0212_1_50/'):
    img = img_to_array(load_img(picture, target_size=(224,224)))
    X.append(img)

    Y.append(0)

## 対象Bの画像
for picture in list_pictures('./test0212_2_100/'):
    img = img_to_array(load_img(picture, target_size=(224,224)))
    X.append(img)

    Y.append(1)

## arrayに変換
X = np.asarray(X)
Y = np.asarray(Y)

## 画素値を0から1の範囲に変換
X = X.astype('float32')
X = X / 255.0

## クラスの形式を変換
Y = np_utils.to_categorical(Y, 2)

## 学習用データとテストデータ
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.33, random_state=111)

## MobileNetのモデルを利用
model = MobileNet(include_top=True, weights=None, classes=2)

## コンパイル
model.compile(loss='categorical_crossentropy',
              optimizer='SGD',
              metrics=['accuracy'])

## 学習
history = model.fit(X_train, y_train, epochs=15,
                   validation_data = (X_test, y_test), verbose = 1)

filepath = '0212_my_model.h5'
model.save(filepath)

## coding:utf-8

from time import sleep

from keras.applications import mobilenet

from keras.models import load_model
from keras.preprocessing import image
from keras.preprocessing.image import img_to_array, load_img
import numpy as np
from keras.utils import np_utils

model = load_model('0212_my_model.h5', custom_objects={
                   'relu6': mobilenet.relu6,
                   'DepthwiseConv2D': mobilenet.DepthwiseConv2D})

while True:
    X = []

    picture = '/home/pi/motion/lastsnap.jpg'
    img = img_to_array(load_img(picture, target_size=(224, 224)))
    X.append(img)

    X = np.asarray(X)
    X = X.astype('float32')
    X = X / 255.0

    features = model.predict(X)

    print(features)

    if features[0][0] > 0.5:
        print('水が減っています！ (' + str(features[0][0]) + ')')
    else:
        print('水は十分あります！ (' + str(features[0][0]) + ')')

    sleep(60)