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def objective(trial):
    import keras
    #セッションのクリア
    keras.backend.clear_session()
    
    num_layer = trial.suggest_int("num_layer", 3, 8)
    mid_units = int(trial.suggest_discrete_uniform("mid_units", 100, 300, 100))
    num_filters = [int(trial.suggest_discrete_uniform("num_filter_"+str(i), 16, 128, 16)) for i in range(num_layer)]
    dropout_rate = trial.suggest_uniform('dropout_rate', 0.0, 0.5)
    metrics = trial.suggest_categorical('metrics', ['accuracy'])

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def create_model(num_layer, mid_units, num_filters, dropout_rate):
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Flatten
    from keras.layers import Conv2D, MaxPooling2D
    model = Sequential()
    model.add(Conv2D(filters=num_filters[0], kernel_size=(3, 3),
                     activation="relu",
                     input_shape=(37, 110, 1)))
    for i in range(1, num_layer):
        model.add(Conv2D(filters=num_filters[i], kernel_size=(3,3),

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def load_data(path, isGray = False, isEqualLength = False, isFlat = False):
    import sys
    from PIL import Image
    import random
    import os
    import numpy as np
    from lib.json_data import read_by_key
    from tensorflow.keras.utils import to_categorical
    import copy
    gun_list = read_by_key(os.path.join('settings', 'data.json'), 'gunList')

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# -*- coding: utf-8 -*-

import Adafruit_PCA9685
import cv2
import numpy as np
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
import time

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wid = 10    # グラデーションの変化幅(%)
color_before = '#efefef'    # もともとの背景色
color_after = '#ffcc99'     # 変化後の背景色
first_duration = 35         # 1変化目の時間幅(%)
second_duration = 30        # 2変化目の時間幅(%)


class Bezier:
    def __init__(self, p):
        self.__p = p

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import matplotlib.pyplot as plt


class Bezier:
    def __init__(self, p):
        self.__p = p
    
    def calc(self, t):
        return self.__calc(self.__p, t)
    

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wid = 10    # グラデーションの変化幅(%)
color_before = '#efefef'    # もともとの背景色
color_after = '#ffcc99'     # 変化後の背景色
first_duration = 35         # 1変化目の時間幅(%)
second_duration = 30        # 2変化目の時間幅(%)

f = open('./css.txt', mode='w')
for i in range(first_duration):
    p = i + 1
    f.write('  ' + str(p) + '% {\n    background: linear-gradient(to right, ' + color_after + ' ' +

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// GETリクエスト
axios
  .get("http://<localIP>:50001")
  .then((response) => {
    console.log(response);
  })
  .catch((error) => {
    console.log("<>", error);
  })

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[package]
name = "hello-world"
version = "0.1.0"
authors = ["kouya17 <my email>"]
edition = "2018"

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[dependencies]
actix-web = "3"

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use actix_web::{get, post, web, App, HttpResponse, HttpServer, Responder};
use serde::{Deserialize, Serialize};

#[derive(Debug, Serialize, Deserialize)]
struct Operation {
    kind: String,
    user: String,
}

#[get("/")]