{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":5441,"databundleVersionId":38425,"sourceType":"competition"}],"dockerImageVersionId":30746,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# データの準備！！！！！\n## まず解凍するよ🧊🔥","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-07-21T11:06:05.58289Z","iopub.execute_input":"2024-07-21T11:06:05.583851Z","iopub.status.idle":"2024-07-21T11:06:06.848232Z","shell.execute_reply.started":"2024-07-21T11:06:05.583803Z","shell.execute_reply":"2024-07-21T11:06:06.846964Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/dogs-vs-cats-redux-kernels-edition/sample_submission.csv\n/kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip\n/kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip\n","output_type":"stream"}]},{"cell_type":"code","source":"import zipfile # ZIPアーカイブを作成するためのクラス\n\n# train.zipの解凍\nwith zipfile.ZipFile('/kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip', 'r') as zip_ref:\n zip_ref.extractall('/kaggle/working/train')\n\n# test.zipの解凍\nwith zipfile.ZipFile('/kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip', 'r') as zip_ref:\n zip_ref.extractall('/kaggle/working/test')","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:06:06.850125Z","iopub.execute_input":"2024-07-21T11:06:06.850692Z","iopub.status.idle":"2024-07-21T11:06:24.216035Z","shell.execute_reply.started":"2024-07-21T11:06:06.850654Z","shell.execute_reply":"2024-07-21T11:06:24.215175Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"# ライブラリインストール🐶😺","metadata":{}},{"cell_type":"code","source":"import numpy as np # みんな大好き numpy（Pythonでの機械学習の計算をより速く、効率的に行えるようにする拡張モジュール）\nimport pandas as pd\nimport os\nimport shutil\nimport cv2\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.models import load_model","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:06:24.217249Z","iopub.execute_input":"2024-07-21T11:06:24.217529Z","iopub.status.idle":"2024-07-21T11:06:38.853883Z","shell.execute_reply.started":"2024-07-21T11:06:24.217506Z","shell.execute_reply":"2024-07-21T11:06:38.853024Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stderr","text":"2024-07-21 11:06:26.684027: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-07-21 11:06:26.684479: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-07-21 11:06:26.861436: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}]},{"cell_type":"markdown","source":"ディレクトリはフォルダといっしょ","metadata":{}},{"cell_type":"markdown","source":"# パス管理🧚♀️\n- **パスとは** ファイルがどこにあるか（住所みたいなもん）","metadata":{}},{"cell_type":"code","source":"# trainデータのディレクトリのパスだよ\ntrain_dir = '/kaggle/working/train/train'","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:06:38.855966Z","iopub.execute_input":"2024-07-21T11:06:38.856528Z","iopub.status.idle":"2024-07-21T11:06:38.861052Z","shell.execute_reply.started":"2024-07-21T11:06:38.8565Z","shell.execute_reply":"2024-07-21T11:06:38.859972Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"# testデータのディレクトリのパスだよ\ntest_dir = '/kaggle/working/test/test'","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:06:38.86254Z","iopub.execute_input":"2024-07-21T11:06:38.863217Z","iopub.status.idle":"2024-07-21T11:06:38.893329Z","shell.execute_reply.started":"2024-07-21T11:06:38.863172Z","shell.execute_reply":"2024-07-21T11:06:38.892194Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"# 一応画像サイズ統一しとく","metadata":{}},{"cell_type":"code","source":"# 画像のサイズ統一\nIMG_SIZE = 100","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:06:38.894695Z","iopub.execute_input":"2024-07-21T11:06:38.8951Z","iopub.status.idle":"2024-07-21T11:06:38.904362Z","shell.execute_reply.started":"2024-07-21T11:06:38.89507Z","shell.execute_reply":"2024-07-21T11:06:38.903445Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"# データを読み込む関数作っとく〜\n関数にした方がいいのよ。使いまわせるから。","metadata":{}},{"cell_type":"markdown","source":"load_data関数のなかでデバッグ文何個か書いてるけどコメントアウト（文章の形で残している,codeではない）したままでいいよ
\n別にコメントアウト外してもいいけど長々とファイル名が列挙されるだけやで\n- **デバックとは** ソースコードのエラーやバグを見つけて修正することやで","metadata":{}},{"cell_type":"code","source":"# データを読み込む用の関数\ndef load_data(data_dir):\n images = [] # 写真を入れる用のリストちゃん\n labels = [] # その写真のラベル（わんこかにゃんこか）を入れる用のリストちゃん\n files = os.listdir(data_dir) # listdirはファイルの一覧を得ることができるやつ\n# print(f'（発見） {len(files)} files in {data_dir}') # デバッグ文（見つけたディレクトリを表示）\n for file in files: # for文でくるくる回してく〜\n img_path = os.path.join(data_dir, file) # ディレクトリのパスとファイル名（写真名）をくっつけて（join）、ファイル（写真）のパスを作る。それをimg_pathに入れる\n# print(f'（ファイル読み込み） {img_path}') # デバッグ文（読み込んだファイルを表示）\n img = cv2.imread(img_path) # 写真を取得〜\n if img is not None: # もし、写真がNoneじゃなかったら（つまり、写真がきちんとあったら）\n img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) # その写真のサイズとかを調整〜\n images.append(img) # リストの中に一枚ずつ入れてく〜\n label = 1 if 'dog' in file else 0 # 写真がもし、わんこなら 1, にゃんこなら 0とラベル付して\n labels.append(label) # そのラベルをリストに入れてく〜\n else: # それ以外（つまり、写真がきちんと読み込めてなかったら）\n print(f'（エラー）ファイル読み込めてねーじゃんか！！ {img_path}') # デバッグ文（読み込めてないとエラーを吐く）\n return np.array(images) / 255.0, np.array(labels) # かえりち（写真とラベルのリストたち）\n\n","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:06:38.905394Z","iopub.execute_input":"2024-07-21T11:06:38.905674Z","iopub.status.idle":"2024-07-21T11:06:38.916665Z","shell.execute_reply.started":"2024-07-21T11:06:38.905641Z","shell.execute_reply":"2024-07-21T11:06:38.915746Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"## trainデータとtestデータを読み込んでく","metadata":{}},{"cell_type":"code","source":"# trainデータとtestデータを読み込んでる\nX_train, y_train = load_data(train_dir)\nX_test, y_test = load_data(test_dir)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:06:38.917949Z","iopub.execute_input":"2024-07-21T11:06:38.918278Z","iopub.status.idle":"2024-07-21T11:07:15.712774Z","shell.execute_reply.started":"2024-07-21T11:06:38.918252Z","shell.execute_reply":"2024-07-21T11:07:15.711873Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"## 読み込んだやつの確認（別にしなくていい）","metadata":{}},{"cell_type":"markdown","source":"した4つはデータの形状を確認してるだけやから別に実行しなくてもいいよ。
\n今はコメントアウトしてる","metadata":{}},{"cell_type":"code","source":"# print(f'X_train shape: {X_train.shape}')","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.714079Z","iopub.execute_input":"2024-07-21T11:07:15.714432Z","iopub.status.idle":"2024-07-21T11:07:15.718723Z","shell.execute_reply.started":"2024-07-21T11:07:15.714404Z","shell.execute_reply":"2024-07-21T11:07:15.717855Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"# print(f'y_train shape: {y_train.shape}')","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.722282Z","iopub.execute_input":"2024-07-21T11:07:15.722678Z","iopub.status.idle":"2024-07-21T11:07:15.737053Z","shell.execute_reply.started":"2024-07-21T11:07:15.722645Z","shell.execute_reply":"2024-07-21T11:07:15.73615Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"# print(f'X_test shape: {X_test.shape}')","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.738485Z","iopub.execute_input":"2024-07-21T11:07:15.738833Z","iopub.status.idle":"2024-07-21T11:07:15.751915Z","shell.execute_reply.started":"2024-07-21T11:07:15.738807Z","shell.execute_reply":"2024-07-21T11:07:15.750832Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"# print(f'y_test shape: {y_test.shape}')","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.75355Z","iopub.execute_input":"2024-07-21T11:07:15.754031Z","iopub.status.idle":"2024-07-21T11:07:15.762994Z","shell.execute_reply.started":"2024-07-21T11:07:15.753991Z","shell.execute_reply":"2024-07-21T11:07:15.762011Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"# データ拡張〜\ntrainデータに色々しちゃう。性能上がるかも？かものはしかわいい","metadata":{}},{"cell_type":"markdown","source":"## 何してるかのまとめ\n\n| パラメータ | 説明 |\n| :----: | :----: |\n| rotation_range=20 | Max20°まで回転するよ |\n| width_shift_range=0.2 | 水平方向にMax20%動かすよ |\n| height_shift_range=0.2 | 垂直方向にMax20%動かすよ |\n| shear_range=0.2 | 斜めに引き伸ばすよ |\n| zoom_range=0.2 | Max20%ズームするよ |\n| horizontal_flip=True | 水平方向に反転するよ |\n| fill_mode='nearest' | 変換した後の空白をどうにかするよ |\n\nこの表にあるやつは全部ランダムに行うよ","metadata":{}},{"cell_type":"code","source":"# データ拡張しとこ〜\ndatagen = ImageDataGenerator(\n rotation_range=20,\n width_shift_range=0.2,\n height_shift_range=0.2,\n shear_range=0.2,\n zoom_range=0.2,\n horizontal_flip=True,\n fill_mode='nearest'\n)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.764412Z","iopub.execute_input":"2024-07-21T11:07:15.764753Z","iopub.status.idle":"2024-07-21T11:07:15.77329Z","shell.execute_reply.started":"2024-07-21T11:07:15.764728Z","shell.execute_reply":"2024-07-21T11:07:15.772191Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"# モデルを作ってくよ〜","metadata":{}},{"cell_type":"markdown","source":"## モデルを作る関数作っとく〜\n畳み込みニューラルネットワークだよ。れる〜","metadata":{}},{"cell_type":"code","source":"# モデルを作る\ndef create_model(neuron):\n # ライブラリとか関数とかをインポートするよ\n Dense = keras.layers.Dense\n Conv2D = keras.layers.Conv2D\n MaxPooling2D = keras.layers.MaxPooling2D\n Flatten = keras.layers.Flatten\n Dropout = keras.layers.Dropout\n \n # モデルを定義するよ\n model = keras.models.Sequential()\n \n # ここから畳み込みの醍醐味。レイヤー追加してく〜\n \n # まず、32このフィルターと3x3のカーネルサイズを持ってる畳み込みそう。活性化関数はれるー。あと画像の形状の指定。今回は3だからカラー（あか、あお、緑）だよ\n model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 3)))\n # 2x2のプーリング層。空間サイズを半分に\n model.add(MaxPooling2D((2, 2)))\n \n # 次、64このフィルターと3x3のカーネルサイズを持ってる畳み込みそう。活性化関数はれるー。\n model.add(Conv2D(64, (3, 3), activation='relu'))\n # 2x2のプーリング層。空間サイズを半分に\n model.add(MaxPooling2D((2, 2)))\n \n # 次、128このフィルターと3x3のカーネルサイズを持ってる畳み込みそう。活性化関数はれるー。\n model.add(Conv2D(128, (3, 3), activation='relu'))\n # 2x2のプーリング層。空間サイズを半分に\n model.add(MaxPooling2D((2, 2)))\n \n # 全部の結合そうを追加 \n # Flatten(): 3次元の特徴マップを1次元のベクトルに変換\n model.add(Flatten())\n # neuron数のユニットを持つ全結合層。活性化関数はれるー\n model.add(Dense(neuron, activation='relu'))\n # ドロップアウト正則化。半分のニューロンをランダムに無効。\n model.add(Dropout(0.5))\n # 出力層。シグモイド\n model.add(Dense(1, activation='sigmoid'))\n\n # モデルをコンパイルするよ\n # バイナリクロスエントロピー損失関数。Adam。モデルの評価は精度で\n model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n \n # モデルを返すよ\n return model\n","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.775016Z","iopub.execute_input":"2024-07-21T11:07:15.775487Z","iopub.status.idle":"2024-07-21T11:07:15.786403Z","shell.execute_reply.started":"2024-07-21T11:07:15.77546Z","shell.execute_reply":"2024-07-21T11:07:15.785426Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"# モデルを保存する関数","metadata":{}},{"cell_type":"code","source":"def save_model(model, filename):\n model.save(filename)\n ","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.787854Z","iopub.execute_input":"2024-07-21T11:07:15.788579Z","iopub.status.idle":"2024-07-21T11:07:15.801901Z","shell.execute_reply.started":"2024-07-21T11:07:15.788542Z","shell.execute_reply":"2024-07-21T11:07:15.800905Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"# モデルをロードする関数","metadata":{}},{"cell_type":"code","source":"def load_existing_model(filename):\n return load_model(filename)\n","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.803707Z","iopub.execute_input":"2024-07-21T11:07:15.804285Z","iopub.status.idle":"2024-07-21T11:07:15.811752Z","shell.execute_reply.started":"2024-07-21T11:07:15.804237Z","shell.execute_reply":"2024-07-21T11:07:15.810713Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"# 学習して〜評価して〜その評価の値を表示〜する関数","metadata":{}},{"cell_type":"markdown","source":"fitのところわかりづらいかな〜と思って解説\n\n| コード | 説明 |\n| :----: | :----: |\n| datagen.flow(X_train, y_train, batch_size=batch) | trainデータをバッチごとに作る |\n| steps_per_epoch=len(X_train) // batch | 1エポックあたりのステップ数指定 |\n| validation_data=(X_test, y_test) | 検証データとしてX_test, y_test |\n| epochs=epochs | 学習に使用するエポック数を指定 |\n| verbose=1 | 学習の進行状況を表示 |","metadata":{}},{"cell_type":"code","source":"# 学習し、テストデータで評価し、スコアを表示する関数\ndef fit_epoch(neuron, batch, epochs, initial_epoch=0, model_filename='model.h5'):\n if initial_epoch == 0: # もし、モデルを作るのが初めてなら\n # さっき作ったモデル作る関数を実行（ニューロン数送る）\n model = create_model(neuron)\n else: # もうすでに作ったことがあるなら\n model = load_existing_model(model_filename) #モデルを読み込む\n \n model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n \n # モデルを学習するよ。フィット！！！！！！\n hist = model.fit(datagen.flow(X_train, y_train, batch_size=batch),\n steps_per_epoch=len(X_train) // batch,\n validation_data=(X_test, y_test),\n epochs=epochs,\n initial_epoch=initial_epoch,\n verbose=1)\n \n # モデルを評価するよ。そしてそれをprintするよ\n score = model.evaluate(X_test, y_test, verbose=1)\n print('正解率=', score[1], 'loss=', score[0])\n \n # モデルを保存しとこ〜\n save_model(model, model_filename)\n \n # 学習結果を可視化、グラフ化するよ。ほら、文字全部見るのってだるいじゃん\n \n # trainデータの精度の流れをグラフに\n plt.plot(hist.history['accuracy'])\n # testデータの精度の流れをグラフに\n plt.plot(hist.history['val_accuracy'])\n # グラフのタイトル。あくちゅありー\n plt.title('Accuracy')\n # 凡例\n plt.legend(['train', 'test'], loc='upper left')\n # グラフの表示\n plt.show()\n\n # trainデータの損失値の流れをグラフに\n plt.plot(hist.history['loss'])\n # testデータの損失値の流れをグラフに\n plt.plot(hist.history['val_loss'])\n # グラフのタイトル。ろす\n plt.title('Loss')\n # 凡例\n plt.legend(['train', 'test'], loc='upper left')\n # グラフの表示\n plt.show()\n ","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.81326Z","iopub.execute_input":"2024-07-21T11:07:15.813635Z","iopub.status.idle":"2024-07-21T11:07:15.825938Z","shell.execute_reply.started":"2024-07-21T11:07:15.813607Z","shell.execute_reply":"2024-07-21T11:07:15.824986Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"# 実際にモデル作ろ〜","metadata":{}},{"cell_type":"markdown","source":"neuronは512, batchは32で固定でいいんじゃない？
\nただ、
\n- **epochs（エポック数）** は適切な数で。多くしすぎて動くならいいけど、動かない場合があるからね\n- **initial_epoch** は初めてモデル作るなら0で。\n- **model_filename** は保存してあるモデルファイルの名前で。","metadata":{}},{"cell_type":"code","source":"total_epochs = 50 # 全体のエポック数〜\nneuron = 512 # ニューロンの数〜\nbatch = 16 # バッチサイズ〜\nmodel_filename = 'model.h5' # モデルのファイル名（モデルの拡張子は.h5だよ）","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:07:15.827239Z","iopub.execute_input":"2024-07-21T11:07:15.82764Z","iopub.status.idle":"2024-07-21T11:07:15.84039Z","shell.execute_reply.started":"2024-07-21T11:07:15.827602Z","shell.execute_reply":"2024-07-21T11:07:15.839481Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"# 1回目の学習\nfit_epoch(neuron=neuron, batch=batch, epochs=10, initial_epoch=0, model_filename=model_filename)","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:43:54.854453Z","iopub.execute_input":"2024-07-21T11:43:54.854915Z","iopub.status.idle":"2024-07-21T11:43:55.29575Z","shell.execute_reply.started":"2024-07-21T11:43:54.854879Z","shell.execute_reply":"2024-07-21T11:43:55.294218Z"},"trusted":true},"execution_count":1,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# 1回目の学習\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mfit_epoch\u001b[49m(neuron\u001b[38;5;241m=\u001b[39mneuron, batch\u001b[38;5;241m=\u001b[39mbatch, epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m, initial_epoch\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, model_filename\u001b[38;5;241m=\u001b[39mmodel_filename)\n","\u001b[0;31mNameError\u001b[0m: name 'fit_epoch' is not defined"],"ename":"NameError","evalue":"name 'fit_epoch' is not defined","output_type":"error"}]},{"cell_type":"code","source":"# 2回目の学習\nfit_epoch(neuron=neuron, batch=batch, epochs=20, initial_epoch=10, model_filename=model_filename)","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:13:42.87818Z","iopub.execute_input":"2024-07-21T11:13:42.878589Z","iopub.status.idle":"2024-07-21T11:20:04.232594Z","shell.execute_reply.started":"2024-07-21T11:13:42.878554Z","shell.execute_reply":"2024-07-21T11:20:04.231462Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Epoch 11/20\n\u001b[1m 5/1562\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:03\u001b[0m 41ms/step - accuracy: 0.7535 - loss: 0.4822","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721560429.451424 104 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m 571/1562\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m47s\u001b[0m 48ms/step - accuracy: 0.7439 - loss: 0.5161","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721560456.646353 104 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7466 - loss: 0.5147","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721560504.709422 107 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 49ms/step - accuracy: 0.7466 - loss: 0.5147 - val_accuracy: 0.4435 - val_loss: 1.2737\nEpoch 12/20\n\u001b[1m 1/1562\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m12s\u001b[0m 8ms/step - accuracy: 0.8125 - loss: 0.4548","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721560506.655248 106 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8125 - loss: 0.4548 - val_accuracy: 0.4754 - val_loss: 1.1942\nEpoch 13/20\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 45ms/step - accuracy: 0.7648 - loss: 0.4921 - val_accuracy: 0.5196 - val_loss: 1.3537\nEpoch 14/20\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.7500 - loss: 0.5806 - val_accuracy: 0.5366 - val_loss: 1.2957\nEpoch 15/20\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 45ms/step - accuracy: 0.7731 - loss: 0.4772 - val_accuracy: 0.6593 - val_loss: 0.7568\nEpoch 16/20\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8750 - loss: 0.3186 - val_accuracy: 0.6272 - val_loss: 0.8235\nEpoch 17/20\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 45ms/step - accuracy: 0.7871 - loss: 0.4540 - val_accuracy: 0.4332 - val_loss: 1.6334\nEpoch 18/20\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8750 - loss: 0.3873 - val_accuracy: 0.4408 - val_loss: 1.6214\nEpoch 19/20\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 45ms/step - accuracy: 0.8003 - loss: 0.4366 - val_accuracy: 0.4963 - val_loss: 1.4172\nEpoch 20/20\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.6250 - loss: 0.4440 - val_accuracy: 0.5039 - val_loss: 1.3770\n\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.5124 - loss: 1.3447\n正解率= 0.5039200186729431 loss= 1.3769686222076416\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","image/png":"iVBORw0KGgoAAAANSUhEUgAAAiMAAAGzCAYAAAD9pBdvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABoh0lEQVR4nO3dd3hU1drG4d9k0kMSAgkJkEDovYYOIigKFhSQIqiIBT2KHpSjn2KvcI4FK4gFrCAIgmJDEZQmvffeISEB0kmb2d8fG4JREAJJ9pTnvq65MjPZM/MmgcyTtdZ+l80wDAMRERERi/hYXYCIiIh4N4URERERsZTCiIiIiFhKYUREREQspTAiIiIillIYEREREUspjIiIiIilFEZERETEUgojIiIiYimFEREREbGUwoiIFxs3bhw2m422bdtaXYqIeDGb9qYR8V4dO3bk8OHD7N27lx07dlC7dm2rSxIRL6SREREvtWfPHv744w/GjBlDVFQUkyZNsrqks8rKyrK6BBEpZQojIl5q0qRJREREcN1119G3b9+zhpHU1FQefvhh4uPjCQgIIDY2lsGDB5OSklJ4TE5ODs899xx169YlMDCQypUr06dPH3bt2gXA77//js1m4/fffy/y3Hv37sVms/HJJ58U3jdkyBDKlSvHrl27uPbaawkNDeWWW24BYOHChfTr149q1aoREBBAXFwcDz/8MCdPnvxb3Vu3bqV///5ERUURFBREvXr1ePLJJwH47bffsNlszJw582+Pmzx5MjabjSVLlhT7+ykiF8/X6gJExBqTJk2iT58++Pv7M3DgQN577z1WrFhB69atAcjMzOSyyy5jy5Yt3HnnnbRs2ZKUlBRmzZrFwYMHiYyMxOFwcP311zN37lxuvvlmhg8fTkZGBnPmzGHjxo3UqlWr2HUVFBTQvXt3OnXqxGuvvUZwcDAA06ZNIzs7m/vuu4+KFSuyfPly3nnnHQ4ePMi0adMKH79+/Xouu+wy/Pz8uOeee4iPj2fXrl189913vPzyy3Tp0oW4uDgmTZpE7969//Y9qVWrFu3bt7+E76yIFJshIl5n5cqVBmDMmTPHMAzDcDqdRmxsrDF8+PDCY5555hkDMGbMmPG3xzudTsMwDGPixIkGYIwZM+acx/z2228GYPz2229FPr9nzx4DMD7++OPC+26//XYDMB5//PG/PV92dvbf7hs9erRhs9mMffv2Fd7XuXNnIzQ0tMh9f67HMAxj5MiRRkBAgJGamlp439GjRw1fX1/j2Wef/dvriEjp0jSNiBeaNGkS0dHRdO3aFQCbzcaAAQOYMmUKDocDgK+//ppmzZr9bfTg9PGnj4mMjOTBBx885zEX47777vvbfUFBQYXXs7KySElJoUOHDhiGwZo1awBITk5mwYIF3HnnnVSrVu2c9QwePJjc3FymT59eeN/UqVMpKCjg1ltvvei6ReTiKIyIeBmHw8GUKVPo2rUre/bsYefOnezcuZO2bduSlJTE3LlzAdi1axeNGzf+x+fatWsX9erVw9e35GZ8fX19iY2N/dv9+/fvZ8iQIVSoUIFy5coRFRXF5ZdfDkBaWhoAu3fvBjhv3fXr16d169ZF1slMmjSJdu3a6YwiEQtozYiIl5k3bx5HjhxhypQpTJky5W+fnzRpEldffXWJvd65RkhOj8D8VUBAAD4+Pn879qqrruL48eM89thj1K9fn5CQEA4dOsSQIUNwOp3Frmvw4MEMHz6cgwcPkpuby9KlS3n33XeL/TwicukURkS8zKRJk6hUqRJjx4792+dmzJjBzJkzGT9+PLVq1WLjxo3/+Fy1atVi2bJl5Ofn4+fnd9ZjIiIiAPPMnD/bt2/fBde8YcMGtm/fzqeffsrgwYML758zZ06R42rWrAlw3roBbr75ZkaMGMGXX37JyZMn8fPzY8CAARdck4iUHE3TiHiRkydPMmPGDK6//nr69u37t8sDDzxARkYGs2bN4qabbmLdunVnPQXWONUr8aabbiIlJeWsIwqnj6levTp2u50FCxYU+fy4ceMuuG673V7kOU9ff+utt4ocFxUVRefOnZk4cSL79+8/az2nRUZGcs011/DFF18wadIkevToQWRk5AXXJCIlRyMjIl5k1qxZZGRkcMMNN5z18+3atStsgDZ58mSmT59Ov379uPPOO0lISOD48ePMmjWL8ePH06xZMwYPHsxnn33GiBEjWL58OZdddhlZWVn8+uuv3H///dx4442Eh4fTr18/3nnnHWw2G7Vq1eL777/n6NGjF1x3/fr1qVWrFo888giHDh0iLCyMr7/+mhMnTvzt2LfffptOnTrRsmVL7rnnHmrUqMHevXv54YcfWLt2bZFjBw8eTN++fQF48cUXL/wbKSIly8pTeUSkbPXs2dMIDAw0srKyznnMkCFDDD8/PyMlJcU4duyY8cADDxhVq1Y1/P39jdjYWOP22283UlJSCo/Pzs42nnzySaNGjRqGn5+fERMTY/Tt29fYtWtX4THJycnGTTfdZAQHBxsRERHGvffea2zcuPGsp/aGhIScta7Nmzcb3bp1M8qVK2dERkYaQ4cONdatW/e35zAMw9i4caPRu3dvo3z58kZgYKBRr1494+mnn/7bc+bm5hoRERFGeHi4cfLkyQv8LopISdPeNCLitQoKCqhSpQo9e/ZkwoQJVpcj4rW0ZkREvNY333xDcnJykUWxIlL2NDIiIl5n2bJlrF+/nhdffJHIyEhWr15tdUkiXk0jIyLidd577z3uu+8+KlWqxGeffWZ1OSJeTyMjIiIiYimNjIiIiIilFEZERETEUm7R9MzpdHL48GFCQ0MvaSdQERERKTuGYZCRkUGVKlX+tufUn7lFGDl8+DBxcXFWlyEiIiIX4cCBA2fdjfs0twgjoaGhgPnFhIWFWVyNiIiIXIj09HTi4uIK38fPxS3CyOmpmbCwMIURERERN3O+JRZawCoiIiKWUhgRERERSymMiIiIiKXcYs3IhXA4HOTn51tdhluy2+34+vrqtGkREbGER4SRzMxMDh48iDrbX7zg4GAqV66Mv7+/1aWIiIiXcfsw4nA4OHjwIMHBwURFRemv+2IyDIO8vDySk5PZs2cPderU+cfGNCIiIiXN7cNIfn4+hmEQFRVFUFCQ1eW4paCgIPz8/Ni3bx95eXkEBgZaXZKIiHgRj/kTWCMil0ajISIiYhW9A4mIiIilFEZERETEUgojHiA+Pp4333zT6jJEREQuitsvYHVXXbp0oXnz5iUSIlasWEFISMilFyUiImIBjYy4KMMwKCgouKBjo6KiCA4OLuWKRNxTvsPJRwt3s2rfcatLkVN+3HCEXzcnWV2GuBCPCyOGYZCdV2DJ5UKbrg0ZMoT58+fz1ltvYbPZsNlsfPLJJ9hsNn766ScSEhIICAhg0aJF7Nq1ixtvvJHo6GjKlStH69at+fXXX4s831+naWw2Gx999BG9e/cmODiYOnXqMGvWrJL8Nou4jXfn7eSlH7YweMJydidnWl2O1/tmzSHun7Sauz9bycw1B60uR1yEx03TnMx30PCZny157c0vdCfY//zf0rfeeovt27fTuHFjXnjhBQA2bdoEwOOPP85rr71GzZo1iYiI4MCBA1x77bW8/PLLBAQE8Nlnn9GzZ0+2bdtGtWrVzvkazz//PK+88gqvvvoq77zzDrfccgv79u2jQoUKJfPFiriBjYfSGPvbTgCy8hzcP2k13wzrSKCf3eLKvNPOoxk8MXND4e3Hpm8gLiKYVvH6veTtPG5kxB2Eh4fj7+9PcHAwMTExxMTEYLebvxxfeOEFrrrqKmrVqkWFChVo1qwZ9957L40bN6ZOnTq8+OKL1KpV67wjHUOGDGHgwIHUrl2bUaNGkZmZyfLly8viyxNxCbkFDh6Zto4Cp8HldaOILBfA1sQMnv12k9WleaXsvALun7Sa7DwHHWpVpHujaPIcTu79fBUHjmdbXZ5YzONGRoL87Gx+obtlr32pWrVqVeR2ZmYmzz33HD/88ANHjhyhoKCAkydPsn///n98nqZNmxZeDwkJISwsjKNHj15yfSLu4p25O9mamEHFEH/G9G/GtsQMbpmwjKkrD9C6RgX6JsRaXaLXMAyDp77ZyPakTKJCA3jr5haEBNjp//4SNh5K585PVvD1/R0IC/SzulSxiMeNjNhsNoL9fS25lEQX2L+eFfPII48wc+ZMRo0axcKFC1m7di1NmjQhLy/vH5/Hz6/of2qbzYbT6bzk+kTcwfqDqbw3fxcAL/VqTMVyAXSoHcnD3eoC8NQ3G9iWmGFliV5l2sqDzFh9CB8bvDOwBVGhAQT7+/LR4NZEhwWw42gmD05eQ4FDv6O8lceFEXfh7++Pw+E473GLFy9myJAh9O7dmyZNmhATE8PevXtLv0ARN5Vb4OA/X63D4TTo2awK1zSpXPi5B7rW5rI6keTkO7l/0iqyci/sjDW5eFuOpPP0txsB+M/V9WhXs2Lh52LCA5lwe2uC/OzM357MSz9ssapMsZjCiEXi4+NZtmwZe/fuJSUl5ZyjFnXq1GHGjBmsXbuWdevWMWjQII1wiPyDN3/dwY6jmUSWC+CFGxoV+ZyPj403BzQnJiyQXclZPDFzwwWfBSfFl5GTz/2TVpNb4KRLvSjuu7zW345pXDWcNwY0B+CTP/by2ZK9ZVukuASFEYs88sgj2O12GjZsSFRU1DnXgIwZM4aIiAg6dOhAz5496d69Oy1btizjakXcw5r9J3j/1PTMqN6NiQjx/9sxFcsF8O6gFth9bHy79jCTl//z+iu5OIZh8PjXG9iTkkWV8EDe6N8cH5+zT2X3aBzD//WoB8Dz321mwfbksixVXIDNcIM/C9LT0wkPDyctLY2wsLAin8vJyWHPnj3UqFGDwMBAiyp0f/o+irvLyXdw3dsL2ZWcRe8WVQv/2j6XDxbsYtSPW/G3+zDj/g40rhpeNoV6ic+W7OWZbzfh62Nj6r3tSage8Y/HG4bBI9PW8/Xqg4QG+DLj/g7UiQ4to2qltPzT+/efaWRERDzCmDnb2ZWcRVRoAM/2bHje44deVpNuDSqR53By/6TVpOfkl0GV3mHdgVRe/H4zAI9fU/+8QQTMRfaj+jSmTXwFMnILuPPTFRzLzC3tUsVFKIyIiNtbte84Hy7cDcDo3k0oH/z36Zm/stlsvN6vObERQew/ns3/TVuv9SMlIC07n2GTV5PvMLi6YTR3dapxwY8N8LUz/rYEqlUI5sDxk/zri1XkFpx/ob+4P4UREXFrJ/McPDJtPYYBN7WMpVvD6At+bHiwH2MHtcTPbmP2pkQ+Xry39Ar1AoZh8J9p6zh44iRxFYJ4tV+zYrc8qBDiz8QhrQgN9GXF3hOMnKFFxt5AYURE3NqrP29jT0oW0WEBPHMB0zN/1SyuPE9dZz5u1I9bWL3/REmX6DU+WriHX7ck4W/3YdygBMKDLq6JWe1KoYy7pSV2HxszVh9i3O+7SrhScTUKIyLitpbtPsbHf+wB4L83Nb3oN7/B7atzXZPKFDgNHpy8hhNZ/9xUUP5u5d7j/Hf2VgCe7tmQJrGXtiD4sjpRPH/q1OxXf97GTxuOXHKN4roURkTELWXnFfDodHN6ZkCrOLrWq3TRz2Wz2fjvTU2IrxjModSTjPhqLU6npgYu1LHMXB6YvAaH0+CGZlW4te25N/EsjlvbVWdIh3gAHv5qLesPppbI84rrURgREbf0v5+2sv94NlXCA3ny+gaX/HyhgX6MuyUBf18fftuWzPsLdpdAlZ7P6TR4+Kt1JKbnUDMqhFF9mpTI1hinPXVdA7rUiyIn38nQz1aSmJZTYs8trkNhRETczpJdx/h0yT4A/te3aYltsNawSlhh19bXftnGst3HSuR5Pdm433eyYHsygX4+jLulJeUCSnb/VV+7D+8MbEHd6HIkpedy16cryM5TG39PozAiIm4lK7eAR6evA2BQ22pcVieqRJ9/QOs4+rSoisNp8OCXa0jOUK+Lc/ljVwpj5mwH4MUbG1M/5txNrS5FaKAfE25vTcUQfzYdTuehKZpG8zQKIyLiVkb/tIWDJ05StXwQT1x76dMzf2Wz2Xipd2PqVCrH0YxcHppqroWQoo5m5PDvL9fiNKBfQiz9WsWV6uvFVQjmg8HmNNovm5N45edtpfp6UrYURizSpUsXHnrooRJ7viFDhtCrV68Sez4RV7RoRwpfLDX3knm1b9MSnxI4Ldjfl/dubUmQn53FO4/x9twdpfI67srhNPj3l2tIycylXnQoL9zYuExeN6F6BV7t2xSA8fN38dXKA2XyulL6FEZExC1k5OTz2NfrAbitXXU61I4s1derXSmUUX3MN9m35+1g4Q5t3nbaG3O2s3T3cUL87Yy7tSVB/vYye+0bm1fl31fWAeDJmRtYqnU9HsHzwohhQF6WNZcL7BI4ZMgQ5s+fz1tvvYXNZsNms7F37142btzINddcQ7ly5YiOjua2224jJSWl8HHTp0+nSZMmBAUFUbFiRbp160ZWVhbPPfccn376Kd9++23h8/3++++l9A0WscaoH7dwKNXs7Pn4NfXL5DV7t4hlYJtqGAY8NGWtzuQAftt2lHd/2wnAqD5NqBVVrsxreOjKOlzXtDL5DoN/fbGKvSlZZV6DlKzSGeO0Un42jKpizWs/cRj8Q8572FtvvcX27dtp3LgxL7zwAgB+fn60adOGu+++mzfeeIOTJ0/y2GOP0b9/f+bNm8eRI0cYOHAgr7zyCr179yYjI4OFCxeaO10+8ghbtmwhPT2djz/+GIAKFSqU6pcqUpbmb0/my+XmkPyrfZsRUkrTM2fzbM+GrDuQyuYj6Tz45Wq+HNoOX7vn/R13IQ6nnmTE1LUA3NquGjc2r2pJHT4+Nl7v14yDJ06y7kAqd366gpn3dSQ8uGTOqpKy53lhxA2Eh4fj7+9PcHAwMTExALz00ku0aNGCUaNGFR43ceJE4uLi2L59O5mZmRQUFNCnTx+qV68OQJMmTQqPDQoKIjc3t/D5RDxF2sl8HptuTs8M6RBPu5oVy/T1A/3sjLulJde/s4gVe0/w2i/by2xkxpXkO5w8MHk1J7LzaVw1rLCFvlUC/ex8ODiBXu8uZndyFsMmr+bjO1rj56VB0d15XhjxCzZHKKx67Yu0bt06fvvtN8qV+/uQ565du7j66qu58soradKkCd27d+fqq6+mb9++REScf2tuEXf20vebSUzPIb5iMP/Xo54lNcRHhvBK36bcP2k14+fvonV8BFc2uPAN+TzBK7O3snp/KqGBvowblECgX9mtEzmXSqGBfHR7a/qO/4NFO1N4dtYmXu7VuESbrknZ8LwIabOZUyVWXC7hP0BmZiY9e/Zk7dq1RS47duygc+fO2O125syZw08//UTDhg155513qFevHnv27CnBb56Ia5m3NYlpqw5is8Gr/ZoR7G/d30/XNqlc2Jp8xFfrOHgi27JaytovmxL5cKH5u+a1fs2oVvHi//AqaQ2rhPH2zS2w2WDysv3aedlNeV4YcRP+/v44HI7C2y1btmTTpk3Ex8dTu3btIpeQEHMdis1mo2PHjjz//POsWbMGf39/Zs6cedbnE3F3adn5jJyxAYC7Otagdbz166CeuLYBzeLKk3Yyn2GT15BX4LS6pFK3/1g2/5lmNpm7u1MNujdyvangbg2jefJUz5mXftjMvK1JFlckxaUwYpH4+HiWLVvG3r17SUlJYdiwYRw/fpyBAweyYsUKdu3axc8//8wdd9yBw+Fg2bJljBo1ipUrV7J//35mzJhBcnIyDRo0KHy+9evXs23bNlJSUsjPz7f4KxS5NM9/v4mk9FxqRobwSHdrpmf+yt/Xh3cHtiA8yI91B1IZ/dMWq0sqVbkFDoZNXk1GTgEtq5XnMRdeK3NXpxoMbBOH04AHJ69ha2K61SVJMSiMWOSRRx7BbrfTsGFDoqKiyMvLY/HixTgcDq6++mqaNGnCQw89RPny5fHx8SEsLIwFCxZw7bXXUrduXZ566ilef/11rrnmGgCGDh1KvXr1aNWqFVFRUSxevNjir1Dk4s3ZnMSM1YfwscFr/Zu5xPqE0+IqBDOmfzMAPl68lx89eGv7l3/YwoZDaUQE+/HuoJYuvTjUZrPxwo2NaV+zIll5Du76ZKVa+bsRm2FcYHMMC6WnpxMeHk5aWhphYUX3PsjJyWHPnj3UqFGDwMBAiyp0f/o+iqs4kZXH1W8uIDkjl3svr8nIa0q+5XtJGP3TFt6fv5tyAb58/2An4iPPf1q/O/lu3WEe/HINAB/f0Zqu9SpZXNGFSc3Oo/e4P9iTkkWLauX5cmg7lwqz3uaf3r//zHVjroh4pee+20RyRi61K5Xj4W51rS7nnB65uh6t4yPIzC3g/kmrycn3nDVbu5MzefxUt9thXWu5TRABKB/sz4TbWxEe5Mea/an83/T1uMHf3F5PYUREXMbsjUf4du1hc3qmn2tNz/yVn92Hdwa2pGKIP5uPpPP8d5utLqlE5OQ7uH/SarLyHLStUcGlA+G51Iwqx3u3tsTXx8asdYd5e+5Oq0uS81AYERGXcCwzlydnbgTgX5fXonlceWsLugAx4YG8eXNzbDb4cvl+Zq45aHVJl+zZbzexNTGDyHL+vDOwhdt2m+1QK5KXepl7C73x63ZmrbOo/5RcEPf8VyYiHueZWZs4lpVH3ehyDO9Wx+pyLthldaL49xVmvU/M2MiOpAyLK7p401cdZOrKA9hs8PbNLagU5t7rx25uU417OtcE4JFp61iz/4TFFcm5eEwY0ZzgpdH3T6z0/frD/LD+CHYfG6/3a06Ar+tOz5zNv6+sQ8faFTl5aoojO6/A6pKKbVtiBk99Y/Z1ebhb3VLfFbmsPNajPt0aRJNX4GToZ6s4lHrS6pLkLNw+jNjt5i+tvLw8iytxb9nZZjdJPz9tNCVlKzkjl6e/MadnhnWpRZPYcIsrKj67j403B7SgUmgAO45m8tTMjW4V8LNyC7h/0ipy8p1cVieSB7rWtrqkEmP3sfHWzc1pUDmMlMxc7vpkBZm57hcWPZ3b703j6+tLcHAwycnJ+Pn54ePj9vmqTBmGQXZ2NkePHqV8+fKF4U6kLBiGwdPfbOREdj71Y0J54Ar3mZ75q6jQAN4Z2IKBHy5lxppDtK1ZgQGtq1ld1nkZhsETMzewKzmLmLBA3hzQHB8fz9rbJSTAlwm3t+LGsYvZmpjB8C/X8MHgVtg97Ot0Z27fZwTMUZE9e/bgdHp+a+bSUr58eWJiYrTBlJSpWesO8+8v1+DrY+PbBzrSqIr7jYr81bjfd/LK7G0E+Pow8/6ONKxy7t4KrmDSsn08OXMjdh8bU+9pRysXaLtfWtYeSGXA+0vILXByd6caPHW9tTsPe4ML7TPi9iMjYO7LUqdOHU3VXCQ/Pz+NiEiZO5qRwzPfmtMzD15RxyOCCMC/OtdixZ7j/LYtmWGTVzPrgY6EBrrm9OfGQ2k8P8s8JfmxHvU8OogANI8rz+v9m/HA5DV8tGgPNaPKMait649eeQOPCCMAPj4+6hwq4iYMw+DJmRtJzc6nUZUw7u9ay+qSSoyPj40x/Ztz3dsL2ZOSxeNfb+DdQS1cbtQxPSef+yetJs/hpFuDSgy9rKbVJZWJ65tWYU9yFq/P2c4z326kesVgOnrIYl13pgUWIlLmvll7iDmbk/Cz23i9fzOX3vPkYkSE+PPuLWbTrR82HOGzJfusLqkIwzD4v2nr2X88m9iIIF7v19zlwlJpeuCK2vRqXoUCp8F9X6xiV3Km1SV5Pc/6DSAiLi8pPYdnv90EwEPd6lI/xrXXVFysltUiGPmnbe3XHUi1tqA/+XjxXmZvSsTPbmPsoJaEB7vmNFJpsdls/PempiRUjyA9p4C7PlnBiSxN81tJYUREyoxhGIycsYH0nAKaxoZzb2fPnhq4s2M83RtFk+8wuH/SatKy860uidX7TzDqxy0APHVdQ5q5Qafb0hDoZ+f92xKIjQhi77Fs7pu0irwCnQRhFYURESkz01cdZN7Wo/jbfXi9XzO3bTV+oWw2G6/0bUa1CsEcSj3Jf6attbT/yImsPB6cvIYCp8F1TSozuH11y2pxBZHlAphwe2vKBfiydPdxnvpmg1v1h/Eknv2bQERcxpG0k7xwajO5h6+qS53oUIsrKhvhQX6Mu6Ul/r4+/LrlKB8u3G1JHU6nwYiv1nIo9STxFYP5701NvGqdyLnUiwnlnUEt8LHBVysPWvbz8XYKIyJS6gzD4LGvN5CRW0DzuPIMvayG1SWVqcZVw3m2p9nT4n+zt7Fy7/Eyr+H9Bbv5bVsy/r4+jLslwWVPN7ZC13qVeOZUz5HRP23ll02JFlfkfRRGPISGFsWVTV1xgAXbzTfC17xgeuZsBrWpxo3Nq+BwGjwweQ3HMnPL7LWX7T7Ga79sA+CFGxq5fCM2K9zeIZ7b2lXHMGD4lLVsOpxmdUlexft+I3gYp9PguVmbaPrcL7w7bwcFDi3AEtdyKPUkL/1gLph89Op61K5UzuKKrGGz2RjVuwm1okJITM/hoalrcTpL/4+IlMxcHvxyDQ6nQZ+WVRnQOq7UX9Md2Ww2nu3ZkMvqRHIy38Hdn67kaHqO1WV5DYURN+ZwGjw6fT2f/LGXjNwCXvtlO33HL2G3zpkXF2EYBo9NX09mbgEJ1SO4s5N3Tc/8VUiAL+NuSSDQz4eFO1IY+9vOUn09h9PgoSlrOZqRS51K5XipV2OtE/kHvnYf3h3UklpRIRxJy2HoZys5meewuiyvoDDipvIdTh6aupavVx/E7mPjzo41CA30Ze2BVK59eyGfLN5TJn91ifyTycv3s2hnCoF+Przat6k2JsNcMPlSryYAvPHrdv7YmVJqr/X23B0s2plCkJ+d925tSbC/xzTdLjXhQX5MHNKaiGA/1h1M4z/TymYEy9spjLihvAInD05ew3frDp9qWtSCZ3o25OeHOtOpdiQ5+U6e+24zt05YxqHUk1aXK17qwPFsXj41PfN/3etTM8o7p2fOpm9CLP1bxeI04N9T1pbKdMDCHcm8PW8HAKP6NKZ2Je84e6kkVK8Ywvu3tcLPbuPHDYm88et2q0vyeAojbiYn38G/vljF7E2J+Nt9GH9rAj0aVwagSvkgPruzDS/c2IhAPx/+2HWMHm8sYPqqg1rgKmXK6TT4v+nryc5z0Ca+AkM6xFtdkst5/obG1I8JLVzTUZLrvRLTcnhoyloMAwa2qUbvFrEl9tzeok2NCozu0xSAd+btZOaagxZX5NkURtzIyTwHQz9bybytRwnw9eHD21txZYPoIsf4+NgY3D6en4Z3pmW18mTkFvDItHXc8/kqUspw9b54ty+W7WPJ7mME+dl5tV9TfDQ98zdB/nbG3tKSEH87y/YcL7G/vgscTh78cjXHsvJoWDms8JRiKb6+CbHc38XcxPGx6RssOSXbWyiMuIms3ALu+GQ5C3eY878f39Gay+tGnfP4GpEhTPtXB/6vRz387DbmbE7i6jcWMHvjkTKsWrzRvmNZjP5xKwAjr61P9YohFlfkumpFleO/N5l/fY/9bRe/bTt6yc/52i/bWbH3BOUCfBl3S0sC/eyX/Jze7JGr69GjUQx5Dif3fL6KA8ezrS7JI11UGBk7dizx8fEEBgbStm1bli9f/o/Hv/nmm9SrV4+goCDi4uJ4+OGHycnRKVMXKj0nn8ETl7N093HKBfjy2V1t6FDr/Fte231s3N+lNt8O60T9mFCOZ+Xxry9WM2LqWtJOWr9Hhngep9Pg0WnrOZnvoF3NCtza1rvbjV+Ins2qcFs78/v08NS1HL6EdV5ztyQxfv4uAF7p25T4SAXBS+XjY2PMgGY0qRrO8aw87vxkBek5+v1Z0oodRqZOncqIESN49tlnWb16Nc2aNaN79+4cPXr2RD958mQef/xxnn32WbZs2cKECROYOnUqTzzxxCUX7w3SsvO57aNlrNp3grBAX764uy2t4ysU6zkaVgnj2wc6cn+XWvjYYMaaQ/R4cwELdySXUtXirT75Yy/L9x4n2N/Oq32baXrmAj11fQOaVA0nNTufYZNXX9SGbQdPZDPiq3UADOkQz7VNKpd0mV4r2N+XDwe3IjosgB1HM839fdTTqUQVO4yMGTOGoUOHcscdd9CwYUPGjx9PcHAwEydOPOvxf/zxBx07dmTQoEHEx8dz9dVXM3DgwPOOpggcz8pj4IdLWXcwjYhgPyYPbUfzi9xhM8DXzv/1qM+0f3UgvmIwR9JyuG3Ccp7+ZiPZeQUlW7h4pT0pWbzyszk988S1DYirEGxxRe4jwNfO2EEtCQ30Zc3+VF6ZvbVYj88rcDJs8hrSTubTLK48T1zboJQq9V4x4YFMuL01QX525m9PLmzkJyWjWGEkLy+PVatW0a1btzNP4ONDt27dWLJkyVkf06FDB1atWlUYPnbv3s2PP/7Itddee87Xyc3NJT09vcjF2yRn5HLzB0vYfCSdyHL+fHlPOxpXDb/k502oHsGPwy8r3K3z86X7uPathazap4VZcvEcToNHp60jJ99Jp9qR3NK2mtUluZ1qFYN5vV8zAD5atIfZGy98f5TRP21h3YFUwoP8GDuoBf6+Wg5YGhpXDeeNAc0BcxTwsyV7La3HkxTrX2xKSgoOh4Po6KJncERHR5OYePb/OIMGDeKFF16gU6dO+Pn5UatWLbp06fKP0zSjR48mPDy88BIX513tixPTchjwwRK2J2VSKTSAKfe0p35Mye0lEezvyws3NuaLu9pSOTyQvcey6Td+Cf+bvZXcAnUblOL7ePEeVu4zF01qN9iLd3WjmMJNBB+dvo79x86/WPKnDUf4ePFeAMb0b0ZshEakSlOPxjE81qM+AM9/t5n52zXdXRJKPT7//vvvjBo1inHjxrF69WpmzJjBDz/8wIsvvnjOx4wcOZK0tLTCy4EDB0q7TJdxKPUkAz5Ywu7kLKqEB/LVve1LbS+PTnUimf1QZ/q0rIrTgPd+38WN7y5m82HvG4mSi7fzaCav/mxuwvbUdQ30ZniJ/q9HffO0/JwC7p+8ipz8c/+BsDcli/+bvh6Af11e62+n+kvp+NflNembEHtq08PVpGbnWV2S2ytWGImMjMRut5OUlFTk/qSkJGJiYs76mKeffprbbruNu+++myZNmtC7d29GjRrF6NGjcTrPvgAoICCAsLCwIhdvsP9YNv3HL2HfsWziKgQx9d72pb4aPjzIjzH9m/P+bQlUDPFna2IGN45dxNjfdmqBlpyXw2nwyLR15BY46Vw3SpuwlQC/U/ujRAT7sfFQemEX27/KyXdw/6TVZOQW0Ca+Ao9cXbeMK/Vepzc9rBtdjoycAmatO2x1SW6vWGHE39+fhIQE5s6dW3if0+lk7ty5tG/f/qyPyc7Oxsen6MvY7eZ57+oKesbu5Ez6v7+EQ6knqREZwlf3ti/TBYDdG8Xw88Od6d4omnyHwas/b6Pf+0vYk5JVZjWI+/lw4W7WHkglNNCX/2l6psRUKR9UuDbh86X7zvpm98L3m9l8JJ2KIf68PbAFvnatEylL/r4+DGpjro2ausJ7Ru9LS7H/9Y4YMYIPP/yQTz/9lC1btnDfffeRlZXFHXfcAcDgwYMZOXJk4fE9e/bkvffeY8qUKezZs4c5c+bw9NNP07Nnz8JQ4u22J2XQ//2lJKbnUKdSOabe047K4UFlXkdkuQDG35rA6/2aERpgruq/5q0FfPrHXm0UJX+zIymDMb+YXUOfub6hJf9mPVmXepV4oGttAEZ+vZ5df9qN+5s1h5i8bD82G7x5c3NiwgOtKtOr3di8Kv52HzYdTmfjoTSry3Frxd7CccCAASQnJ/PMM8+QmJhI8+bNmT17duGi1v379xcZCXnqqaew2Ww89dRTHDp0iKioKHr27MnLL79ccl+FG9t8OJ1bJyzjeFYeDSqH8cVdbahYLsCyemw2GzclxNK+VkX+b/p6Fu1M4dlZm5izOYlX+jalSnm94YjZcvw/09aR53ByRf1K9E3Q3iel4aFudVi57zhLdx9n2KTVzLy/I4dSs3li5gYA/n1FHS6rc+5OzFK6IkL8uapRND+sP8L0VQdL5IxHb2Uz3GCuJD09nfDwcNLS0jxq/cj6g6ncNmE5aSfzaVI1nM/vakP5YH+ryyrkdBp8sWwfo37cQk6+k9BAX57r2Yg+LatqON7Ljf1tJ6/+vI2wQF/mjLic6DD9ZV5ajqbncO3bi0jJzKV3i6psPJTGjqOZdKxdkc/ubItdjeUsNX97MrdPXE54kB/LnrhS7ff/4kLfvzXJaJFV+05wy4fLSDuZT4tq5fni7rYuFUTgzKZ7P/77MlqcWt3/n2nruFeb7nm1rYnpvHlqU7fnbmikIFLKKoUF8vbA5vjYYOaaQ+w4ap7y/+aAFgoiLqBT7UiqhAeSdjKfX7cknf8BclYKIxZYuvsYt01YVrgK/vO72hIe5Gd1WedUM6oc0+5tz6PdzU33ftmcRPc3FhSrKZN4hnyHk/98tY58h0G3BtH0blHV6pK8QodakTzczTxbxscG7wxsQVSoddO5cobdx1Y4TfnVyoMWV+O+FEbK2KIdKQz5eDnZeQ461q7IJ3e2plxAsZfulDlfuw/Dup7ZdO9YVh7/+mKVNt3zMuN+28Wmw+mUD/ZjVJ/Gmq4rQ8O61ubFGxvxyR1taFuzotXlyJ/0TTBPaV+4I/mSNjr0ZgojZei3rUe589MV5OQ76VIvigm3tybY3/WDyJ+d3nTvPm2653U2HU7jnXk7AHj+hkZUCtX0TFny8bFxW/t4OtfVglVXU61iMO1rVsQw4OtVGh25GAojZeTnTYnc8/lK8gqcXNUwmvdvS3DbhU4BvnYe61Gfaf9qX2TTvWe+1aZ7niqvwMkj09ZT4DTo0SiGG5pVsbokEZfSv/WpqZpVB9QK4SIojJSB79Yd5v5Jq8l3GFzXpDLjbmlJgK97BpE/S6heocime58tOb3p3gmLK5OS9u5vO9lyJJ0KIf681FvTMyJ/1aNRZUIDfDlw/CRL9xyzuhy3ozBSymasPsjwKWtwOA16t6jKWzc3x8+DOiWe3nTv87vaEBN2etO9P3hFm+55jI2H0hj7204AXryxMZEW9sERcVVB/nZ6NjdHDKdpIWuxec67oguasnw//5m2DqcBA1rF8Vq/Zh7bsvmyOlH8/HBn+rQwN90bd2rTvS1HtOmeO8stcPCfr9bhcBpc17Qy1zWtbHVJIi6rfytzIeuPG46QnqOF/cXhme+MLuCzJXt5fMYGDANua1ed0X2aeHxPgPAgP8YMaM74W1tS4dSmeze8q0333Nnbc3ewLSmDyHL+vHhjY6vLEXFpzWLDqRcdSm6Bk++0eV6xKIyUgo8W7uaZbzcBcFenGrxwYyN8PDyI/FmPxpX5+aHOXNXwzKZ7/bXpnlvZeTSD57/bxHu/7wLgpV5NqBDiWk35RFyNzWajXyv1HLkYCiMlbOxvO3np1Jbf93epxVPXNfDKxX5RoQF8cFsCr53adG/1/lSufWshny3RpnuuKu/UX3M3f7CEbmMW8PHivTgN6JcQS4/GMVaXJ+IWereoiq+PjXUHUtmWmGF1OW5De9OUEMMweOPXHbw91+zD8HC3uvz7ytpeGUT+6lDqSf5v+joW7zRXmHeqHalN91zIgePZfLl8P1+tPEBKZh5gdvm8skE0t7StRuc6UV41sidyqf71+Spmb0rk7k41eOr6hlaXY6kLff9WGCkBhmHwv9nbGD/fHNJ+rEd97utSy+KqXIvTafDZkr38d/bWwk33nr+hEb1baNM9KzicBr9vO8oXS/fx+/ZkTv8WqBQawM1tqnFz6ziFRZGLNG9rEnd+spIKIf4sHXkl/r7eOwlxoe/f7tX+0wUZhsEL32/m48V7AXj6+obc1amGtUW5IB8fG0M61qBz3ShGfLWOtQdSGfHVOn7elMio3k2oqNNFy8TRjBy+WnGAL5cf4NCf2lZ3qh3Jre2qcWWDaI869VzECp3rRFEpNICjGbnM25pEj8Y6C+18NDJyCZxOg6e/3cikZfsBeLFXY25rV93iqlxfgcPJ+wt28+av28l3GFQM8WdUnyZ0b6R1CaXBMAyW7DrGpGX7+XlTIgWn1uyUD/ajX0Isg9pWp0ZkiMVViniW/83eynu/7+KK+pWYOKS11eVYRtM0pczhNHj86/VMW3UQmw3+16cp/VvHWV2WW9l0OI3/fLWOracWed3UMpZnb2hIWKDr7mDsTlKz85i+6iCTl+1n95/OZEqoHsEtbatxbZPKbrslgYir252cyRWvz8fHBktGXkl0mHfu5aQwUooKHE7+M20d3649jI8NxvRvTi9tpX5RcgscvPnrDt6fvwunAZXDA+mXEEvH2pG0qBbh1XOtF8MwDNYeSOWLpfv5fv1hcgvM/i4h/nZ6tajKLW2r07CK9f+HRLxB//FLWL73OP/Xox73d6ltdTmWUBgpJXkFToZPWcNPGxPx9bHx1s0t1JWyBKzad5wRX61j37HswvuC/Oy0qVGBTrUj6Vg7kvoxoTqr4xyycgv4du1hJi3bx6bDZ7re1o8J5dZ21enVoirlArRETKQsTVt5gEenr6dGZAjz/nO5Vy7WVxgpBbkFDoZNWs2vW47ib/dh7C0tuaphtGX1eJrsvAK+X3eERTtTWLwzhWNZeUU+XyHEnw61KhaGk7gKwRZV6jq2JqYzael+Zq45RGauuWOyv68P1zetzC1tq9OyWnmv/AUo4gqycgto8/KvZOU5mPav9rSOr2B1SWVOYaSE5eQ7uOfzVSzYnkyArw/v35ZAl3qVLKnFGzidBtuSMlh8Kpgs23Oc7LyiG+9VqxBMx9qRdKxdkQ61Ir2mQ2hOvoPZGxP5Yuk+Vv5ph+QakSHc0rYaN7WMJcJLvhciru6x6euZuvIA/RJiebVfM6vLKXMKIyUoO6+Auz5ZyZLdxwjys/PR7a3oWDuyzOvwZnkFTtYdTGXRDjOcrDmQiuMvnVwbVQk7FU4iaR0fQbC/Z01L7E3JKmxOdiLb3ITL7mPj6obR3NK2Oh1qVdQ0loiLWbXvODe9t4RgfzvLn+zmddOlCiMlJCMnnzs/WcGKvScI8bfz8R1taFPD+4baXE1mbgHL9xxj0Y5jLN6Zwrakom2X/ew2WlaLMKd06kTStGq4W+6YXOBw8uuWo0xato+FO1IK768cHsjANtUY0DrOa1fpi7gDwzC4csx8didn8b+bmjCgdTWrSypTCiMlIO1kPrdPXM7aA6mEBvry6Z1taFktosxeXy7c0Ywcluw6Vjhycjgtp8jnQwN8aVuzIh1rm2tOalcq59JrKRLTcvhy+X6mrNhPUnouADab2Uzp1nbV6Vovyi3DlYg3Gj9/F//9aSsJ1SP4+r4OVpdTphRGLtGJrDxum7iMjYfSKR/sx+d3tqVJbHiZvLZcGsMw2Hssm0U7U/hjZwp/7DpG2sn8IsdUCg0onNLpWLsilcOtb33udBos2pnCF0v3MXfr0cJpqIoh/vRrFcegNtWoVlGLdkXczdGMHNqPnofDafDriMupXamc1SWVGYWRS5CSmcutHy1ja2IGFUP8+eLutjSorN4M7srhNNh8OL3wLJ0Ve48X9t84rWZUSOFZOu1qViQ8qOwarx3PymPaygNMXr6/yKnNbWpU4Ja21ejROIYAXzUnE3Fnd3+6kl+3JHHv5TUZeU0Dq8spMwojFykpPYdBHy5lV3IWUaEBTL67LXWiQ0v1NaVs5eQ7WL3vhBlOdh1jw8FU/rwW1scGTWLL06l2RTrWiqRl9YgS71RqGAYr951g0tJ9/LghkTyHGY5CA3y5KSGWQW2rUVf/7kQ8xi+bErnn81VElgtgycgrvGYPKIWRi3A49SSDPlzK3mPZVA4PZPLQdtqzwwukZeezZPcx/tiVwqKdKexOziry+QBfH9rUqECHWpF0qh1Jwyph2C/yrJWMnHxmrjnEpKX7iyy6bVI1nFvbVaNnsyoedxaQiEC+w0n70XNJyczjo8Gt6OYlPaoURorpwPFsBn64lIMnThIbEcSXQ9upqZaXOpJ2ksU7zbN0Fu1MITkjt8jnywf70b5mRTrWNsNJ9YrB510Mu/FQGpOW7efbtYcK+6UE+vlwQ7Mq3NquOk1jy5fWlyMiLmLUj1v4YMFurmoYzYeDW1ldTplQGCmGPSlZDPpwKUfScoivGMykoe2oWt76BY1iPcMw2Hk0s3C9ydLdxws7nZ5WtXwQHWub4aRDrUiiQgMAczrou3WHmbRsP2sPpBYeX7tSOW5pW40+LWPLdG2KiFhrR1IGV72xALuPjSUjr6BSqOeflq8wcoF2JGUw6KNlJGfkUisqhMlD26lvg5xTgcPJuoNphZ1hV+8/Qb6j6H+h+jGh1I8JZd7Wo6TnmMHFz26je6MYbm1XnbY1Krj0acUiUnp6j1vMmv2pPHFtfe7pXMvqckqdwsgF2HIknVs/WsaxrDzqx4Tyxd1tiSwXUGLPL54vO6+A5XuO88epHiebj6QX+XzV8kEMaluN/q3iCkdMRMR7fbl8PyNnbKB2pXLMebizx/9hcqHv3167Ui4rt4DbJiznWFYejauG8fmdbbWfhxRbsL8vXepVKtyn6FhmLkt2H2Pz4XRax1egc92oi17sKiKe5/qmlXn+u03sPJrJmgOpaqR5inecW3QWIQG+PHdDQxKqRzDp7nYKIlIiKpYL4PqmVfi/HvXpWr+SgoiIFBEa6Me1TSoDMG3lAYurcR1eG0YArm9ahWn3ttciQhERKTMDWsUB8N26I2TnFZznaO/g1WEE0C6nIiJSptrUqEB8xWAycwv4aUOi1eW4BK8PIyIiImXJZrPR79ToyFRN1QAKIyIiImWuT8uq+Nhg+Z7j7EnJOv8DPJzCiIiISBmrHB5E57pRAExfpdERhREREREL9D81VTN91UEcTpdv+VWqFEZEREQscGWDSkQE+5GUnsuCHclWl2MphRERERELBPja6dWiKqCeIwojIiIiFhnQ2pyqmbM5ieNZeRZXYx2FEREREYvUjwmjaWw4+Q6Db9YcsrocyyiMiIiIWOh0z5GvVh7ADfauLRUKIyIiIha6oVkVAnx92JqYwYZDaVaXYwmFEREREQuFB/nRo3EMYI6OeCOFEREREYud7jny7drD5OQ7LK6m7CmMiIiIWKx9zYpULR9ERk4BP2/yvs3zFEZEREQs5uNjo1+rWMA7p2oURkRERFxA34RYbDZYvPMYB45nW11OmVIYERERcQGxEcF0qh0JmPvVeBOFERERERfRz0s3z1MYERERcRFXN4wmLNCXQ6kn+WNXitXllBmFERERERcR6Hdm87yvVnrPVI3CiIiIiAs53XPk502JpGZ7x+Z5CiMiIiIupFGVMBpUDiOvwMmsdYetLqdMKIyIiIi4EJvNRn8v6zmiMCIiIuJiejWvir/dh42H0tl02PM3z1MYERERcTERIf5c1SgagGlesJBVYURERMQFnV7IOnPNIY/fPE9hRERExAV1qh1J5fBA0k7m8+uWJKvLKVUKIyIiIi7I7mOjb8LphayePVWjMCIiIuKiToeRhTuSOZx60uJqSo/CiIiIiIuqXjGEdjUrYBjwtQdvnqcwIiIi4sJOL2SdtuogTg/dPO+iwsjYsWOJj48nMDCQtm3bsnz58nMe26VLF2w2298u11133UUXLSIi4i2uaVyZ0ABf9h/PZtme41aXUyqKHUamTp3KiBEjePbZZ1m9ejXNmjWje/fuHD169KzHz5gxgyNHjhReNm7ciN1up1+/fpdcvIiIiKcL8rfTs3kVwHM7shY7jIwZM4ahQ4dyxx130LBhQ8aPH09wcDATJ0486/EVKlQgJiam8DJnzhyCg4MVRkRERC7Q6amaHzccIT0n3+JqSl6xwkheXh6rVq2iW7duZ57Ax4du3bqxZMmSC3qOCRMmcPPNNxMSEnLOY3Jzc0lPTy9yERER8VbNYsOpG12O3AIn33ng5nnFCiMpKSk4HA6io6OL3B8dHU1iYuJ5H798+XI2btzI3Xff/Y/HjR49mvDw8MJLXFxcccoUERHxKObmeeZ7oSf2HCnTs2kmTJhAkyZNaNOmzT8eN3LkSNLS0govBw545hyZiIjIherVoiq+PjbWHUhlW2KG1eWUqGKFkcjISOx2O0lJRdvSJiUlERMT84+PzcrKYsqUKdx1113nfZ2AgADCwsKKXERERLxZZLkArmxQCYBpHraQtVhhxN/fn4SEBObOnVt4n9PpZO7cubRv3/4fHztt2jRyc3O59dZbL65SERERL/fnzfPyCpwWV1Nyij1NM2LECD788EM+/fRTtmzZwn333UdWVhZ33HEHAIMHD2bkyJF/e9yECRPo1asXFStWvPSqRUREvNDldaOoFBrAsaw85m09e0sNd+Rb3AcMGDCA5ORknnnmGRITE2nevDmzZ88uXNS6f/9+fHyKZpxt27axaNEifvnll5KpWkRExAv52n24KSGW937fxVcrD9Cj8T8vkXAXNsMwXL63bHp6OuHh4aSlpWn9iIiIeLXdyZlc8fp8fGywZOSVRIcFWl3SOV3o+7f2phEREXEjNaPK0To+AqcBX6/2jNN8FUZERETcTL/Tm+etPIgbTHCcl8KIiIiIm7muSWWC/e3sScli5b4TVpdzyRRGRERE3ExIgC/XN60MwFcr3L/niMKIiIiIGzrdc+SHDUfIzC2wuJpLozAiIiLihhKqR1AzKoTsPAc/rHfvzfMURkRERNyQJ22epzAiIiLipvq0qIrdx8aqfSfYeTTT6nIumsKIiIiIm6oUFkjXelEATFvlvgtZFUZERETc2OmeI1+vOkS+wz03z1MYERERcWNX1K9EZDl/UjJzmb8t2epyLorCiIiIiBvzs/vQu0VVAL5a6Z5TNQojIiIibu70VM28rUdJzsi1uJriUxgRERFxc3WjQ2keV54Cp8HMNe53mq/CiIiIiAcY0PpMzxF32zxPYURERMQDXN+0MoF+Puw8msmaA6lWl1MsCiMiIiIeIDTQj2ubmJvnTXOzhawKIyIiIh7idHv479YdITvPfTbPUxgRERHxEG1rVKB6xWAycwv4aUOi1eVcMIURERERD2Gz2eiXEAu4V88RhREREREPclNCLDYbLNtznL0pWVaXc0EURkRERDxI5fAgOtdxr83zFEZE5OwcBbDqUzi40upKRKSYTvccmb7qIA6n6/ccURgRkbNb+Dp892/46Er49AbYu8jqikTkAl3ZoBIRwX4kpeeyYIfrb56nMCIif5eVAn+8feqGDfbMh0+ug4nXwM654GbdHUW8TYCvnV6nNs9zh54jCiMi8ncLXoW8TKjcHIavhVZ3gt0f9v8BX/SBj7rBttkKJSIurF+COVUzZ3MSx7PyLK7mnymMiEhRJ/bCignm9W7PQUQ8XP8GDF8Hbe8D30A4tBK+HADvXwabvwWn08KCReRsGlYJo0nVcPIdBt+sOWR1Of9IYUREipr3MjjzoWZXqNX1zP1hVeCa/8JDG6DjcPALgcQN8NVgeK89rJ8GTod1dYvI3/RvdabniCtvnqcwIiJnHFkPG6aZ17s9d/ZjylWCq16AhzdC50chIAySt8KMu+Hd1rBmEjjyy6xkETm3G5pVxd/Xh62JGWw8lG51OeekMCIiZ8x9HjCgUR+o0vyfjw2uAFc8ZY6UdH0KgiLg+C749n54pyWsnAgFuWVRtYicQ3iwHz0axQAwdeV+i6s5N4URETHtWQg7fwUfXzNkXKig8nD5o/DQRnPEJCQKUvfD9w/DW81h6XjIP1laVYvIeZzuOfLt2sPk5LvmVKrCiIiYZ8X8+qx5PWEIVKxV/OcIKGeuJRm+Hnr8F0IrQ8ZhmP0YvNkUFr8NuZklWraInF/7mhWpWj6IjJwCft7kmpvnKYyICGyZBYdWmYtSO//fpT2XfzC0u888++a6MRBeDbKOwpyn4c0m5mnDOWklU7eInJePj41+rVx78zyFERFv5yiAuS+Y19sPg9Doknle3wBofRf8ezXcOBYq1ISTx2HeS/BGE/OsnezjJfNaIvKP+p7aPG/xzmMcOJ5tdTl/ozAi4u3WfA7HdkJwRejwYMk/v90PWtwKw1ZAnw8hsh7kpsGCV8yRkjnPQqbrt6sWcWexEcF0rBUJmPvVuBqFERFvlpcNv//XvN75UQgMK73XsvtC0/5w/1Lo9ylENzG7vC5+0wwls0dC+pHSe30RL3d6qmb6qoM4XWzzPIUREW+27D3ITITy1cyW72XBxwca9YJ/LYSBU6BKSyg4CUvHwVtN4fsR5tk4IlKiujeKISzQl0OpJ1m8K8XqcopQGBHxVtnHYdFb5vWuT5lrPMqSzQb1roGh8+DWryGuHTjyYOUEeLsFfPsAHNtVtjWJeLBAPzs3Njc3z/tqpWtN1SiMiHirRWPMtRvRjaFJP+vqsNmgdje4czbc/j3U6AzOAnMty7utYMY9kLzNuvpEPMjpniM/b0okNdt1Ns9TGBHxRqkHYNkH5vVuz5lTJ1az2aDGZXD7d3DnL1D7KjCcsH4qjG0LX90OiRutrlLErTWqEkaDymHkFTiZte6w1eUUcoHfQCJS5n7/LzhyoXonc1TC1VRrC7dOh6G/Qf3rAQM2fwPjO8KXg+DQaqsrFHFLNputyOZ5rkJhRMTbHN0C6yab16963hyRcFVVW8LNk+Bfi6FRb8AG236AD7vCFzfB/mVWVyjidno1r4q/3YeNh9LZdNg1GhAqjIh4m7kvmNMfDXpCbCurq7kwMY2h3ycwbDk0vRlsdnMfnYlXwyfXw54FZkt7ETmviBB/rmpoNjec5iILWRVGRLzJviWw7Uew+cAVz1hdTfFF1YU+78ODK6HlYHNTv70L4dOeMLEH7PhVoUTkApzuOfLN2kPkFli/eZ7CiIi3MAz49TnzeovbzDd2d1WhJtzwDvx7LbS+G+wBcGApTLoJPrwCtv6oUCLyDy6rE0VMWCCp2fnM2ZxkdTkKIyJeY/ts8w3bNxC6PG51NSWjfBxc97q5KV+7YeAbBIdXw5SBML4TbJoJTqfVVYq4HLuPjb4JpxeyWj9VozAi4g2cDvj1efN6u/sgrIq19ZS0sMrQYxQ8tAE6PQz+5SBpI0wbAuPawbqp5oaAIlLo9FTNwh3JHE49aWktCiMi3mDdFEjeAoHloeNDVldTespFmX1THtoAlz8GAeGQsg1m3gOzHrC6OhGXUr1iCO1qVsAw4GuLN89TGBHxdPk58Nso8/pl/4Gg8paWUyaCK0DXJ+DhDWare4D1X0GWa+3HIWK1/q3MjqzTLN48T2FExNOt+BDSD0JYVWhzj9XVlK3AcLj8UajcDAwHbJlldUUiLuWaxpUpF+DL/uPZLNtz3LI6FEZEPNnJVFjwmnm9y0jwC7S0HMs06mN+3DjD2jpEXEyQv51BbasxsE01KoWV8WaZf+Jr2SuLSOlb/BbkpEJUfWg20OpqrNOoN/z6LOxbDBlJEBptdUUiLuOJaxtYXYJGRkQ8VvoRWPqeef3KZ8DuxX97RFSHqq3MzrObv7W6GhH5C4UREU81/79QcBLi2kK9a62uxnqNT03VbNJUjYirURgR8UQpO2D15+b1bi6+GV5ZadjL/Lh/CaQdsrQUESlKYUTEE819wTx7pO41UL291dW4hvCqUO3U92LzN5aWIiJFKYyIeJqDK0+dwmoz14rIGTqrRsQlKYyIeJI/b4bXbCBEN7S0HJfT8AbABodWwol9VlcjIqcojIh4kp1zYe9Ccxfbrk9YXY3rCY2B+E7mdU3ViLgMhRERT+F0nhkVaTPU3NFW/q5Rb/OjpmpEXIbCiIin2DgdkjZAQJi5B42cXcMbwWaHI2vh2C6rqxERFEZEPENBLsx70bzecbi5UZycXUgk1OhsXt8009paRARQGBHxDCs/htT9UC4G2t1ndTWur7ABmsKIiCtQGBFxdznpsOAV83qXx8A/xNp63EH968HHF5I2QvJ2q6sR8XoKIyLubsm7kH0MKtSCFrdZXY17CK4Ata4wr6s9vIjlLiqMjB07lvj4eAIDA2nbti3Lly//x+NTU1MZNmwYlStXJiAggLp16/Ljjz9eVMEi8ieZR+GPd83rVz4Ddj9r63Enf26AZhjW1iLi5YodRqZOncqIESN49tlnWb16Nc2aNaN79+4cPXr0rMfn5eVx1VVXsXfvXqZPn862bdv48MMPqVq16iUXL+L1FrwK+VlQpaV5lohcuPrXgt0fUrbB0c1WVyPi1YodRsaMGcPQoUO54447aNiwIePHjyc4OJiJEyee9fiJEydy/PhxvvnmGzp27Eh8fDyXX345zZo1u+TiRbza8d2w8tT/u6u0GV6xBYZD7avM6+o5ImKpYoWRvLw8Vq1aRbdu3c48gY8P3bp1Y8mSJWd9zKxZs2jfvj3Dhg0jOjqaxo0bM2rUKBwOxzlfJzc3l/T09CIXEfmLeS+DswBqXXnmVFUpnsKzajRVI2KlYoWRlJQUHA4H0dHRRe6Pjo4mMTHxrI/ZvXs306dPx+Fw8OOPP/L000/z+uuv89JLL53zdUaPHk14eHjhJS5OnSRFiji81mxyBtDtOSsrcW91e4BvkDnKdGSd1dWIeK1SP5vG6XRSqVIlPvjgAxISEhgwYABPPvkk48ePP+djRo4cSVpaWuHlwIEDpV2miHuZ+7z5sUk/qNzU2lrcWUA5qHu1eV1n1YhYplhhJDIyErvdTlJSUpH7k5KSiImJOetjKleuTN26dbHb7YX3NWjQgMTERPLy8s76mICAAMLCwopcROSU3b/Drnng4wddn7S6Gvd3eq+aTTM1VSNikWKFEX9/fxISEpg7d27hfU6nk7lz59K+ffuzPqZjx47s3LkTp9NZeN/27dupXLky/v7+F1m2iJcyjDOb4bW6EyrUsLQcj1CnO/iFmB1sD622uhoRr1TsaZoRI0bw4Ycf8umnn7Jlyxbuu+8+srKyuOOOOwAYPHgwI0eOLDz+vvvu4/jx4wwfPpzt27fzww8/MGrUKIYNG1ZyX4WIt9j8DRxeA/7loPOjVlfjGfyDoV4P87qmakQs4VvcBwwYMIDk5GSeeeYZEhMTad68ObNnzy5c1Lp//358fM5knLi4OH7++WcefvhhmjZtStWqVRk+fDiPPfZYyX0VIt7AkQ9zXzCvd3gQykVZW48nadQHNn5tTtVc9SL4qDm1SFmyGYbrT5Kmp6cTHh5OWlqa1o+I91oxAX4YAcGRMHwtBIRaXZHnyM+B1+pAbjrc+TNUa2d1RSIe4ULfvxX/RdxBXhbM/595/fLHFERKml8g1LvWvK4GaCJlTmFExB0sHQeZSRARDwlDrK7GM51ugLb5G3CeuymjiJQ8hRERV5d1DBa9ZV7v+hT46iy0UlGzKwSWN0Pfvj+srkbEqyiMiLi6ha9DXgbENIHGN1ldjefy9YcG15vXdVaNSJlSGBFxZan7YcWH5vVuz+ksj9LW6PRUzSxwFFhbi4gX0W82EVf22yhw5Jkb4dW60upqPF+NyyG4ImSnwN4FVlcj4jUURkRcVdImWDfFvN7tObDZLC3HK9h9ocEN5nWdVSNSZhRGRFzVr88DBjS8EaomWF2N9zh9Vs2W76Dg7PtniUjJUhgRcUX7/oAdP4PNDlc8Y3U13qV6RygXDTmp5qaEIlLqFEZEXI1hwJxnzestB0NkbWvr8TY+dnM0CnRWjUgZURgRcTVbf4CDy8EvGLo8bnU13qlRb/Pj1h/MVvEiUqoURkRciaPgzGZ47e6D0Bhr6/FWce0gtIq5V82ueVZXI+LxFEZEXMm6yZCyDYIioONwq6vxXj4+0KiXeV1TNSKlTmFExFXkn4TfRpvXL3sEAsOtrcfbnW6Atu0n82cjIqVGYUTEVSx7HzIOQ1gstL7b6mokthWEV4O8TNjxi9XViHg0hRERV3DyBCwaY16/4klzS3uxls12ZqpGDdBESpXCiIgrWPQm5KRBpYbQdIDV1chppxugbf8ZcjOtrUXEgymMiFgt7RAsG29ev/JZs8+FuIbKzSGiBhSchO2zra5GxGMpjIhYbf5/oSAHqrWHut2trkb+zGY7Mzqyaaa1tYh4MIURESslb4M1X5jXuz2vzfBc0emzanbMgZx0a2sR8VAKIyJWmvsCGE6odx1Ua2t1NXI20Y0gsi44cmHbj1ZXI+KRFEZErHJgOWz9Hmw+cKU2w3NZNtuZ0RGdVSNSKhRGRKxgGPDrc+b15oOgUn1Ly5HzOL1uZNc88zRsESlRCiMiVtgxB/YtBnsAdBlpdTVyPlH1oFIjcObDlu+trkbE4yiMiJQ1p+PMqEjbeyE81tJy5AKd3slXe9WIlDiFEZGytmEaHN1k7j3T6WGrq5ELdXqqZvd8yDpmbS0iHkZhRKQsFeTCvJfN650ehuAK1tYjF65iLYhpCoYDtsyyuhoRj6IwIlKWVkyAtP0QWhna3Gt1NVJchQ3QNFUjUpIURkTKSk4aLHjVvN7lcfAPtrYeKb7T60b2LoLMo9bWIuJBFEZEysof78DJ41CxDjS/1epq5GJExEPVBLNR3eZvra5GxGMojIiUhYwkWDLWvH7lM2D3tbYeuXhqgCZS4hRGRMrC/P9BfjZUbQUNelpdjVyKRr3Mj/uXQPphS0sR8RQKIyKl7dguWP2pef0qbYbn9sJjIa4dYMCmb6yuRsQjKIyIlLZ5L4KzAOpcDfGdrK5GSoLOqhEpUQojIqXp0GrYNBOwwZXPWl2NlJSGNwI2OLgCUvdbXY2I21MYESlNp9u+N+0PMY0tLUVKUGjMmVGuTTOtrUXEAyiMiJSWXfNgz3yw+0PXJ62uRkra6Z4jOqtG5JIpjIiUBqfzzKhIq7sgorql5UgpaHgj2HzgyFpzkbKIXDSFEZHSsGkGHFkH/qHQ+RGrq5HSEBIJNTqb1zVVI3JJFEZESlpBHsx7ybze8d/mm5Z4ptMN0BRGRC6JwojIpTIMSN4Gyz+EqbfC63XhxB4IqQTt7re6OilNDXqCjy8kbYTk7VZXI+K21JNa5GKc2At7Fpy5ZCYV/XxAGPR8EwLKWVGdlJXgClCzK+ycY46OdHnM6opE3JLCiMiFSD8MexaawWPvgr/3lvANhLi25hqCGpdDleZg97OkVCljjfucCiMzFEZELpLCiMjZZB2DvQvPjHwc21H08z6+5j4zNTqbl9jW4BdoTa1irfrXmadvJ2+FpM0Q3dDqikTcjsKICEBOGuxbciZ8JG34ywE2c7SjRmeI7wzV2mkKRkyB4VC7G2z70RwdURgRKTaFEfFOedlwYOmZ8HF4DRjOosdUanhm5KN6BwiKsKZWcX2N+phhZOMMs8GdNkMUKRaFEfEOBXlwaOWZ8HFgOTjzix5ToeaZ8BF/GZSrZE2t4n7q9TDXDR3fBYnroXIzqysScSsKI+KZHAVm07E98821H/uWQMHJoseEVTUXm9a4zAwf5eOsqVXcX0CouSvzllnm6IjCiEixKIyIZ3A64ejmMyMf+xZDbnrRY4Ijz4x81OhsjoRoOF1KSuM+ZhjZNAO6Pad/WyLFoDAi7skw4NhOc+Rjz0Jz9CP7WNFjAsLNnVVPh49KDfQGIaWnTnfwCzFP+z60GmITrK5IxG0ojIj7SN1ftNFYxpGin/cLNheanl7zUbkZ+NitqVW8j3+wuXZk49fm6IjCiMgFUxgR15WRdKrXx3wzfJzYW/Tzdv8/NRrrDFVagq+/JaWKAOZZNRu/NruxXvUi+GjHDZELoTAirqMgD3b8DLtPLTpN3lr08zY7VG15JnzEtQW/IGtqFTmb2t3MnZrTD8HB5WY/GhE5L4URcQ1H1sE395sbjhWyQUyTMy3Wq7WDwDDLShQ5L79AqH8trJ9qnlWjMCJyQRRGxFoFebDwdVj4GjgLIKgCNL7p1LqPTuZGZCLupFEfM4xs/gZ6jNa6JZELoDAi1kncAN/cZ34EaHADXDcGykVZW5fIpah1hdkiPjMJ9i8xQ7WI/COtrpKy58iH+a/AB13NIBIUATdNgP6fKYiI+/P1h/o9zesbZ1hbi4ibUBiRspW0GT66En572WzHXv96uH8ZNOmrHiDiORr3Nj9u/tbsBiwi/0jTNFI2HAWw+E34/b9mCAksD9e+phAinqnG5eb6p+wU88ywWl2trkjEpWlkRErf0a0woRvMe9EMInWvgWHLoGk/BRHxTHY/aHiDeX2TpmpEzkdhREqPowAWvQHvXwaH15iL+nqNh4FfQmiM1dWJlK5GfcyPW74z10mJyDlpmkZKR/I280yZQ6vM23Wuhp5vQVgVa+sSKSvxnSCkEmQdhd2/Q52rrK5IxGVpZERKltMBi9+C8ZeZQSQgDG4cB4O+UhAR7+Jjh4Y3mtd1Vo3IP9LIiJSclB1mF9WDy83btbtBz7chvKq1dYlYpXEfWPEhbP0BCnLBN8DqikRcksKIXDqnA5aNh7kvQEGOuTdHj1HQ4jYtUBXvFtcOQqtAxmHYOddsFS/iKnIzIO0QpB2E9IPmOieLtty4qDAyduxYXn31VRITE2nWrBnvvPMObdq0Oeuxn3zyCXfccUeR+wICAsjJybmYlxZXc2yXORpyYKl5u2ZXuOEdKB9nbV0irsDHBxr1gqXjzLNqFEakrDjyIf2wGTROh420g0XDR05a0cfENDU3I7VAscPI1KlTGTFiBOPHj6dt27a8+eabdO/enW3btlGpUqWzPiYsLIxt27YV3rbpr2X353TC8vfh1+eh4CT4l4OrX4KEIRoNEfmzRn3MMLLtJ8g/qZ2m5dIZBmQl/yloHDpz/fTtjETAOP9zBYZDWCyEx4KPdZMlxX7lMWPGMHTo0MLRjvHjx/PDDz8wceJEHn/88bM+xmazEROjUzk9xvHd8O0DsG+xebvG5XDju1C+mrV1ibii2FYQHgdpB2DHL2cWtYqcy5+nT9IOnCVsHAZH7vmfx+4PYVXNoPHny+nwEV4VAkJL/+u5AMUKI3l5eaxatYqRI0cW3ufj40O3bt1YsmTJOR+XmZlJ9erVcTqdtGzZklGjRtGoUaNzHp+bm0tu7plvdHp6enHKlNLidMKKj+DXZyE/G/xC4OoXodWdGg0RORebzZyq+eMd86wahRHvVpBnriH683TJ+aZPzspm9mv6a9j48+3gSHOq0A0UK4ykpKTgcDiIjo4ucn90dDRbt24962Pq1avHxIkTadq0KWlpabz22mt06NCBTZs2ERsbe9bHjB49mueff744pUlpO7HXHA3Zu9C8HX+ZORoSEW9lVSLuoVEfM4xs/xlyMyGgnNUVSWlwOs0tANIO/ClcHCp6OzOJYk+fhFf9y4hGLIRWNjdl9BClPkHUvn172rdvX3i7Q4cONGjQgPfff58XX3zxrI8ZOXIkI0aMKLydnp5OXJwWRFrC6YRVE+GXZyA/C/yC4aoXoNVdbpO4RSxXpYUZ3E/shR0/Q+ObrK5ISsKmmWbALPb0SYAZMMKqmlN4fwsbrjN9UlaKFUYiIyOx2+0kJSUVuT8pKemC14T4+fnRokULdu7cec5jAgICCAjQ+fiWO7EPZj0AexaYt6t1gF5joUJNa+sScTc2mzk6smiMOVWjMOLeHAXwy5NmS4O/Od/0SRyERGpq+y+KFUb8/f1JSEhg7ty59OrVCwCn08ncuXN54IEHLug5HA4HGzZs4NprdYqbyzIMWPUJ/PIU5GWCbxB0ew7a3KPREJGL1fhUGNkxB3LSLevnIJcoJx2m3wk755i32/4LqiacCRseNn1SVoo9TTNixAhuv/12WrVqRZs2bXjzzTfJysoqPLtm8ODBVK1aldGjRwPwwgsv0K5dO2rXrk1qaiqvvvoq+/bt4+677y7Zr0RKRuoBmPUg7P7NvB3XDnqNg4q1rK1LxN1FN4aKdeDYDvM032YDrK5IiuvEPpg8AJK3mH+k9R5vLk6WS1bsMDJgwACSk5N55plnSExMpHnz5syePbtwUev+/fvx+dNfzydOnGDo0KEkJiYSERFBQkICf/zxBw0bNiy5r0IunWHAms9h9hOQlwG+gXDlM2bq97FbXZ2I+7PZzNGR+f8zG6ApjLiX/ctgyiBzgWq5GHP3cYsahHkim2EYF7Cs11rp6emEh4eTlpZGWJiGNktc2iH47t+w81fzdmwbczQkso61dYl4mqNbYVxb8PGDR3dAUITVFcmFWP8VfDsMHHkQ0wQGTtWeWxfoQt+/tQDAmxkGrPkCxrU3g4g9AK56Ee6crSAiUhoq1YdKDcGZb26eJ67N6YR5L8OMoWYQqX893DFbQaQUaKM8b5V+GL4bbnaEBKjaCnq9B1F1ra1LxNM16gNHN5tn1bS41epq5FzyT8I395mn7wJ0HA5XPqdF/KVE31VvYxiw9ksY184MInZ/6PY83PmzgohIWWjcx/y4+3fIOmZpKXIOGYnwyXVmEPHxgxvHmv2VFERKjUZGvElGInz3EGz/ybxdpaU5GlKpvqVliXiVirXM3VET18OWWdDqjvM/RsrOkfXw5c1m59SgCBjwBcR3sroqj6eY5w0Mw1yANbatGUR8/MwzZe6aoyAiYoXToyObZlhbhxS19UeY2MMMIhXrwN1zFUTKiMKIp8s8ClNvNRdg5aRC5eZw7wK47D9g18CYiCUa9TY/7l1k/h8VaxkGLH7bPHU3PwtqdoG756i/UhlSGPFUhgEbppujIVu/N0dDuj4Fd/8K0erxImKpiHhzmtRwwuZvra7GuxXkmY0e5zwNGJBwB9wyXaddlzH9aeyJMpPhhxHmfDSY58X3Gg8xja2tS0TOaNwHDq82z6ppM9TqarxT9nH4arC5G7nNB7qPMhs9at+YMqeREU+zaabZVGnLLPDxhS4jYehvCiIirub0VM3+Jeap9lK2UnbCR93MIOIfajYya3efgohFFEY8RVYKfHU7TBsC2cfMfTCG/gZdHge7n9XVichfhcdCXFvA0FRNWds9Hz66Ao7vgvBqcNfPUPdqq6vyagoj7q4gD1Z/Zq4N2fwN2OzQ+f/MIFK5qdXVicg/aXTqrJqNOqumzKz6BL7oAzlpENsahs6F6EZWV+X1tGbEXeXnmBvbLXoT0g+a91VqaO4pU6WFpaWJyAVqeCPMfhwOLjd3zC4fZ3VFnsvpgDnPwJJ3zduN+5rNzPwCra1LAIUR95OXBSs/hj/ehswk875y0dDh3+YiON8Aa+sTkQsXVhmqd4R9i8z1Xh3/bXVFnik3A76+G7bPNm93fRI6P6r1IS5EYcRd5KTB8g9gyTg4edy8LzzO3C+hxW1K9yLuqnHvU2FkhsJIaUg9AJMHwNFN4Btodp0+3XROXIbCiKvLPg5L34Nl70NumnlfhZrQaQQ0HQC+/tbWJyKXpsGN8OOjcHgNHN9t/v+WknFwJXw5ELKOQkglGDgFYhOsrkrOQmHEVWUkmXObKyaYHQEBourDZY+YpwSqe6qIZygXBTU6mxvnbZppdkeWS7dhOnxzPzhyzbMLB07RmhwXpnc0V5N20GxLvPpTKMgx74tpas5v1r9eu0aKeKJGfcwwslFh5JIZBsz/H/w+2rxd9xq46UMICLW2LvlHCiOu4vgeWPQGrJ0MznzzvtjW5mm6da7SQisRT9agp9k1OWkDpOyAyDpWV+Se8nPg22Gwcbp5u/0DcNUL4GO3ti45L4URqyVvg4VjYMM0MBzmffGXmSMhNTorhIh4g+AKULMr7Jxj9hzp8pjVFbmfzKPmRncHV5jdp68bAwm3W12VXCCFEaskboAFr53qvGiY99W+Cjo/AtXaWVqaiFigcR8zjGxSGCm2pE3mGTNpByCwPAz43PxjTtyGwkhZO7jSDCHbfzpzX/3rzRCiZmUi3qvetWD3h+StkLRZu2tfqO0/w/Q7IS8TKtSCQV9BZG2rq5JiUhgpK3sXw4JXYfdv5m2bj7lo7bL/6JeOiEBQeah1pfmHyqYZ+r1wPoZhtj345UkwnOb0dv/PzCkvcTsKI6XJMGDXPHMkZP8f5n02OzS72ewTovQuIn/WuI8ZRjbOMLuEas3Y2Tnyzd4sqz42b7ccbK4R0aagbkthpDQYBmz7yRwJObzavM/uDy1uhY4PQUR1S8sTERdV7xqzS+jxXea6Mm12+XcnT5g7lO+ZD9jg6peg/TAFNzenMFKSnA5zQerC1yFpo3mfbxC0ugM6PAhhVaytT0RcW0CoeSr/lu/MqRqFkaKO7YLJ/eHYTvALgb4TzAAnbk9hpCQ48s1ufwtfh2M7zPv8y5kb17UbZnZYFBG5EI36mGFk4wy48ln9xX/anoUw9VbISYWwWBg0BWKaWF2VlBCFkUtRkGs2KVv0BqTuM+8LLA/t7oM292ghlYgUX93u4Bds/k45vBqqai8VVn8O3z8EzgLz+3HzlxAabXVVUoIURi5GXjas/gwWvwUZh837giOhwwPQ6i4IDLO2PhFxX/4hULeHOU2zcYZ3hxGnA359Dv5427zdqLe5665fkKVlSclTGCmO3Axz47ol70JWsnlfaGXoOBxa3g7+wdbWJyKeoXEfM4xs+gauetE796TKzYQZ98C2H8zblz8Glz/und8LL6AwciFOnoBlH8DSceZ8JUD5atDpYWh+C/gGWFqeiHiY2leBfyikHzTbm1dra3VFZSvtIEy+2dyrxx4AN46Fpv2srkpKkcLIP8lKgSVjYfmHkJdh3lexttmorEk/ndMuIqXDLxDqXwvrp5ojJN4URg6tgi8HQmYShETBzZMhro3VVUkpUxg5m/Qj8Mc7ZkOd/GzzvkoNzZbtDXtpB0gRKX2N+pwKI99A91He8Xtn00yY+S8oyDF/5w6cor5MXkJh5M9S95uLUld/Do5c874qLcwddOteo7lKESk7ta6AwHDITIT9SyC+k9UVlR7DMDtV//aSebvO1XDTBJ0M4EUURsBspLNoDKybYp46BhDXDi5/1NwrQuf5i0hZ8/WH+j1h7RfmWTWeGkbyc2DWg7DhK/N2u/vNrqreMBIkhbw7jBzdYjYq2/i1udESQM0u5khI9Y4KISJirUa9zTCy+Vu45hWwe9iv7MxkmHoLHFhm7tt13WvQ6k6rqxILeNi/7GIoyINPe545RbduD7jsEYhrbW1dIiKn1bwcgipAdgrsXQi1ulpdUck5usVs7Z66HwLCof+nnvX1SbF4bxjx9Tc3Vzq02lyYWrmZ1RWJiBRl94MGPWH1p+ZZNe78Zm0YZmuEtINwZD389Jh5lmJEDRj0FUTVtbpCsZD3hhEwd9DVVIyIuLLGfcwwsuU7uG6M67YUyD8J6Ych7QCkHTJDR/pB8+Pp2/lZRR9TvSMM+EJbZ4iXhxEFERFxddU7mf02spJh93yo063sa3A6zL4faQfPXNIPFb2dnXJhzxVcEcJjoWZX6PqkOUotXs+7w4iIiKuz+0LDG2HFR+ZUTUmHkT9Pn5w1bBwy9+A6fabhP/ELNoNGeCyEVYXwOAiveup2LIRV0bYZclYKIyIirq5RHzOMbPkern+jeFtQXMz0ydnY7GaYKAwasUUvYVUhKEIjznJRFEZERFxdtfbmppwZR2DXPKh3jXl/iU6fRJ4axYj7U9j40+3QGPX+kFKjMCIi4up8fMytKJa9B788ZXaKLtb0ScifwsWpKZMiYaMK+AWV+pchci4KIyIi7qBJXzOMHNtpXk6z2U+NZFQ99xRKYHlNn4hLUxgREXEHsa2gz0fmVM2fg0a5aE2fiNtTGBERcRdN+1ldgUip0Da0IiIiYimFEREREbGUwoiIiIhYSmFERERELKUwIiIiIpZSGBERERFLKYyIiIiIpRRGRERExFIKIyIiImIphRERERGxlMKIiIiIWEphRERERCylMCIiIiKWcotdew3DACA9Pd3iSkRERORCnX7fPv0+fi5uEUYyMjIAiIuLs7gSERERKa6MjAzCw8PP+Xmbcb644gKcTieHDx8mNDQUm81WYs+bnp5OXFwcBw4cICwsrMSeVy6Ofh6uRz8T16Kfh2vRz+P8DMMgIyODKlWq4ONz7pUhbjEy4uPjQ2xsbKk9f1hYmP4huRD9PFyPfiauRT8P16Kfxz/7pxGR07SAVURERCylMCIiIiKW8uowEhAQwLPPPktAQIDVpQj6ebgi/Uxci34erkU/j5LjFgtYRURExHN59ciIiIiIWE9hRERERCylMCIiIiKWUhgRERERSymMiIiIiKW8OoyMHTuW+Ph4AgMDadu2LcuXL7e6JK80evRoWrduTWhoKJUqVaJXr15s27bN6rLklP/+97/YbDYeeughq0vxWocOHeLWW2+lYsWKBAUF0aRJE1auXGl1WV7L4XDw9NNPU6NGDYKCgqhVqxYvvvjieTeDk3Pz2jAydepURowYwbPPPsvq1atp1qwZ3bt35+jRo1aX5nXmz5/PsGHDWLp0KXPmzCE/P5+rr76arKwsq0vzeitWrOD999+nadOmVpfitU6cOEHHjh3x8/Pjp59+YvPmzbz++utERERYXZrX+t///sd7773Hu+++y5YtW/jf//7HK6+8wjvvvGN1aW7La/uMtG3bltatW/Puu+8C5mZ8cXFxPPjggzz++OMWV+fdkpOTqVSpEvPnz6dz585Wl+O1MjMzadmyJePGjeOll16iefPmvPnmm1aX5XUef/xxFi9ezMKFC60uRU65/vrriY6OZsKECYX33XTTTQQFBfHFF19YWJn78sqRkby8PFatWkW3bt0K7/Px8aFbt24sWbLEwsoEIC0tDYAKFSpYXIl3GzZsGNddd12R/ydS9mbNmkWrVq3o168flSpVokWLFnz44YdWl+XVOnTowNy5c9m+fTsA69atY9GiRVxzzTUWV+a+3GLX3pKWkpKCw+EgOjq6yP3R0dFs3brVoqoEzBGqhx56iI4dO9K4cWOry/FaU6ZMYfXq1axYscLqUrze7t27ee+99xgxYgRPPPEEK1as4N///jf+/v7cfvvtVpfnlR5//HHS09OpX78+drsdh8PByy+/zC233GJ1aW7LK8OIuK5hw4axceNGFi1aZHUpXuvAgQMMHz6cOXPmEBgYaHU5Xs/pdNKqVStGjRoFQIsWLdi4cSPjx49XGLHIV199xaRJk5g8eTKNGjVi7dq1PPTQQ1SpUkU/k4vklWEkMjISu91OUlJSkfuTkpKIiYmxqCp54IEH+P7771mwYAGxsbFWl+O1Vq1axdGjR2nZsmXhfQ6HgwULFvDuu++Sm5uL3W63sELvUrlyZRo2bFjkvgYNGvD1119bVJE8+uijPP7449x8880ANGnShH379jF69GiFkYvklWtG/P39SUhIYO7cuYX3OZ1O5s6dS/v27S2szDsZhsEDDzzAzJkzmTdvHjVq1LC6JK925ZVXsmHDBtauXVt4adWqFbfccgtr165VECljHTt2/Nup7tu3b6d69eoWVSTZ2dn4+BR9+7Tb7TidTosqcn9eOTICMGLECG6//XZatWpFmzZtePPNN8nKyuKOO+6wujSvM2zYMCZPnsy3335LaGgoiYmJAISHhxMUFGRxdd4nNDT0b+t1QkJCqFixotbxWODhhx+mQ4cOjBo1iv79+7N8+XI++OADPvjgA6tL81o9e/bk5Zdfplq1ajRq1Ig1a9YwZswY7rzzTqtLc1+GF3vnnXeMatWqGf7+/kabNm2MpUuXWl2SVwLOevn444+tLk1Oufzyy43hw4dbXYbX+u6774zGjRsbAQEBRv369Y0PPvjA6pK8Wnp6ujF8+HCjWrVqRmBgoFGzZk3jySefNHJzc60uzW15bZ8RERERcQ1euWZEREREXIfCiIiIiFhKYUREREQspTAiIiIillIYEREREUspjIiIiIilFEZERETEUgojIiIiYimFEREREbGUwoiIiIhYSmFERERELPX/6q4GjnSMkWEAAAAASUVORK5CYII="},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# 3回目の学習\nfit_epoch(neuron=neuron, batch=batch, epochs=30, initial_epoch=20, model_filename=model_filename)","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:20:04.235732Z","iopub.execute_input":"2024-07-21T11:20:04.236084Z","iopub.status.idle":"2024-07-21T11:26:32.472987Z","shell.execute_reply.started":"2024-07-21T11:20:04.236057Z","shell.execute_reply":"2024-07-21T11:26:32.471994Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Epoch 21/30\n\u001b[1m 5/1562\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m55s\u001b[0m 36ms/step - accuracy: 0.8969 - loss: 0.3088","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721560810.468278 107 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m 509/1562\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m50s\u001b[0m 48ms/step - accuracy: 0.7952 - loss: 0.4423","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721560834.759763 104 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1561/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.7990 - loss: 0.4355","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721560888.910393 106 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 52ms/step - accuracy: 0.7991 - loss: 0.4355 - val_accuracy: 0.4404 - val_loss: 1.5950\nEpoch 22/30\n\u001b[1m 1/1562\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m12s\u001b[0m 8ms/step - accuracy: 0.8125 - loss: 0.4138","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721560890.927402 104 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8125 - loss: 0.4138 - val_accuracy: 0.4304 - val_loss: 1.6646\nEpoch 23/30\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 45ms/step - accuracy: 0.8102 - loss: 0.4191 - val_accuracy: 0.4763 - val_loss: 1.8323\nEpoch 24/30\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8125 - loss: 0.3503 - val_accuracy: 0.4749 - val_loss: 1.8352\nEpoch 25/30\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m74s\u001b[0m 47ms/step - accuracy: 0.8200 - loss: 0.4031 - val_accuracy: 0.5209 - val_loss: 1.3671\nEpoch 26/30\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.7500 - loss: 0.4936 - val_accuracy: 0.5057 - val_loss: 1.4351\nEpoch 27/30\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 45ms/step - accuracy: 0.8188 - loss: 0.3970 - val_accuracy: 0.4645 - val_loss: 1.7862\nEpoch 28/30\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.7500 - loss: 0.3806 - val_accuracy: 0.4735 - val_loss: 1.7232\nEpoch 29/30\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 46ms/step - accuracy: 0.8282 - loss: 0.3843 - val_accuracy: 0.4934 - val_loss: 1.4707\nEpoch 30/30\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.2127 - val_accuracy: 0.4986 - val_loss: 1.4548\n\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.5066 - loss: 1.4196\n正解率= 0.4986400008201599 loss= 1.4548012018203735\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# 4回目の学習\nfit_epoch(neuron=neuron, batch=batch, epochs=40, initial_epoch=30, model_filename=model_filename)","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:26:32.475516Z","iopub.execute_input":"2024-07-21T11:26:32.475928Z","iopub.status.idle":"2024-07-21T11:32:57.136402Z","shell.execute_reply.started":"2024-07-21T11:26:32.475891Z","shell.execute_reply":"2024-07-21T11:32:57.135188Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"Epoch 31/40\n\u001b[1m 5/1562\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m57s\u001b[0m 37ms/step - accuracy: 0.7987 - loss: 0.4935","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721561198.862170 104 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1348/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m9s\u001b[0m 46ms/step - accuracy: 0.8297 - loss: 0.3865","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721561260.644486 106 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1561/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.8298 - loss: 0.3858","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721561277.265947 107 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 51ms/step - accuracy: 0.8298 - loss: 0.3858 - val_accuracy: 0.4634 - val_loss: 1.7810\nEpoch 32/40\n\u001b[1m 1/1562\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m13s\u001b[0m 9ms/step - accuracy: 0.6250 - loss: 0.5431","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721561279.232186 107 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.6250 - loss: 0.5431 - val_accuracy: 0.4747 - val_loss: 1.6953\nEpoch 33/40\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 45ms/step - accuracy: 0.8271 - loss: 0.3819 - val_accuracy: 0.4308 - val_loss: 1.9780\nEpoch 34/40\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 1.0000 - loss: 0.1685 - val_accuracy: 0.4317 - val_loss: 2.0006\nEpoch 35/40\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 45ms/step - accuracy: 0.8412 - loss: 0.3703 - val_accuracy: 0.6060 - val_loss: 1.3432\nEpoch 36/40\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8750 - loss: 0.7347 - val_accuracy: 0.5841 - val_loss: 1.4580\nEpoch 37/40\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 45ms/step - accuracy: 0.8347 - loss: 0.3675 - val_accuracy: 0.4882 - val_loss: 1.6196\nEpoch 38/40\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.9375 - loss: 0.1174 - val_accuracy: 0.4890 - val_loss: 1.6094\nEpoch 39/40\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m70s\u001b[0m 45ms/step - accuracy: 0.8412 - loss: 0.3593 - val_accuracy: 0.4362 - val_loss: 1.9383\nEpoch 40/40\n\u001b[1m1562/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - accuracy: 0.8125 - loss: 0.3317 - val_accuracy: 0.4418 - val_loss: 1.8756\n\u001b[1m391/391\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.4479 - loss: 1.8245\n正解率= 0.4418399930000305 loss= 1.875610589981079\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","image/png":"iVBORw0KGgoAAAANSUhEUgAAAiwAAAGzCAYAAAAMr0ziAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABlzUlEQVR4nO3dd3gU5d7G8e+mF5JAgDQIvfceKSoKUkQUC02UoqgHEUWsHI8gNhTLCwgiNrDRLICiIhil9xJ6bwkljZJK2u6+fwwEIzWQZDbZ+3Ndc2V3dnb2t4Rk7zzzFIvdbrcjIiIi4sBczC5ARERE5GoUWERERMThKbCIiIiIw1NgEREREYenwCIiIiIOT4FFREREHJ4Ci4iIiDg8BRYRERFxeAosIiIi4vAUWERERMThKbCISKGbPn06FouFDRs2mF2KiBRTCiwiIiLi8BRYRERExOEpsIiIQ9i8eTNdu3bF39+fUqVK0aFDB9asWZPnmOzsbMaMGUPNmjXx8vKibNmytGvXjsWLF+ceExsby6BBg6hYsSKenp6EhoZyzz33cPjw4SJ+RyJSkNzMLkBEZMeOHdx88834+/vz4osv4u7uztSpU2nfvj1Lly4lIiICgNdee42xY8cyePBgWrVqRXJyMhs2bGDTpk3ccccdANx///3s2LGDYcOGUaVKFeLj41m8eDHR0dFUqVLFxHcpIjfCYrfb7WYXISIl2/Tp0xk0aBDr16+nRYsWFz1+77338ttvv7Fr1y6qVasGwIkTJ6hduzZNmzZl6dKlADRp0oSKFSuyYMGCS77OmTNnKFOmDO+99x7PP/984b0hESlyuiQkIqayWq0sWrSIHj165IYVgNDQUB588EFWrFhBcnIyAKVLl2bHjh3s27fvkufy9vbGw8ODJUuWcPr06SKpX0SKhgKLiJgqISGB9PR0ateufdFjdevWxWazERMTA8Drr7/OmTNnqFWrFg0bNuSFF15g69atucd7enry7rvv8vvvvxMcHMwtt9zCuHHjiI2NLbL3IyKFQ4FFRIqNW265hQMHDvDll1/SoEEDPv/8c5o1a8bnn3+ee8zw4cPZu3cvY8eOxcvLi1dffZW6deuyefNmEysXkRulwCIipipfvjw+Pj7s2bPnosd2796Ni4sL4eHhufsCAwMZNGgQM2fOJCYmhkaNGvHaa6/leV716tV57rnnWLRoEdu3bycrK4sPPvigsN+KiBQiBRYRMZWrqyudOnVi/vz5eYYex8XFMWPGDNq1a4e/vz8AJ0+ezPPcUqVKUaNGDTIzMwFIT08nIyMjzzHVq1fHz88v9xgRKZ40rFlEisyXX37JwoULL9r/2muvsXjxYtq1a8eTTz6Jm5sbU6dOJTMzk3HjxuUeV69ePdq3b0/z5s0JDAxkw4YN/PDDDzz11FMA7N27lw4dOtCrVy/q1auHm5sbc+fOJS4ujj59+hTZ+xSRgqdhzSJS6M4Pa76cmJgYEhISGDlyJCtXrsRmsxEREcFbb71F69atc4976623+Pnnn9m7dy+ZmZlUrlyZhx9+mBdeeAF3d3dOnjzJ6NGjiYyMJCYmBjc3N+rUqcNzzz1Hz549i+KtikghUWARERERh6c+LCIiIuLwFFhERETE4SmwiIiIiMNTYBERERGHp8AiIiIiDk+BRURERBxeiZg4zmazcfz4cfz8/LBYLGaXIyIiItfAbreTkpJCWFgYLi5XbkMpEYHl+PHjedYaERERkeIjJiaGihUrXvGYEhFY/Pz8AOMNn19zRERERBxbcnIy4eHhuZ/jV1IiAsv5y0D+/v4KLCIiIsXMtXTnUKdbERERcXgKLCIiIuLwFFhERETE4ZWIPizXwm63k5OTg9VqNbuUYsnV1RU3NzcNGxcREVM4RWDJysrixIkTpKenm11Ksebj40NoaCgeHh5mlyIiIk6mxAcWm83GoUOHcHV1JSwsDA8PD7US5JPdbicrK4uEhAQOHTpEzZo1rzrBj4iISEEq8YElKysLm81GeHg4Pj4+ZpdTbHl7e+Pu7s6RI0fIysrCy8vL7JJERMSJOM2fyWoRuHH6NxQREbPoE0hEREQcngKLiIiIOLx8BZaxY8fSsmVL/Pz8CAoKokePHuzZs+eqz/v++++pU6cOXl5eNGzYkN9++y3P43a7nVGjRhEaGoq3tzcdO3Zk3759+XsnckVVqlRh/PjxZpchIiJyXfIVWJYuXcrQoUNZs2YNixcvJjs7m06dOpGWlnbZ56xatYq+ffvy6KOPsnnzZnr06EGPHj3Yvn177jHjxo1j4sSJfPLJJ6xduxZfX186d+5MRkbG9b+zEqB9+/YMHz68QM61fv16Hn/88QI5l4iISFGz2O12+/U+OSEhgaCgIJYuXcott9xyyWN69+5NWloaCxYsyN1300030aRJEz755BPsdjthYWE899xzPP/88wAkJSURHBzM9OnT6dOnz1XrSE5OJiAggKSkpIsWP8zIyODQoUNUrVq12I1sad++PU2aNLlsy4jdbsdqteLmVjSDvYrzv6VpcrIg/eS5LdH4mnbutqs7tHka3DzNrlJExBRX+vz+txv6pEtKSgIgMDDwssesXr2aESNG5NnXuXNn5s2bB8ChQ4eIjY2lY8eOuY8HBAQQERHB6tWrLxlYMjMzyczMzL2fnJycr7rtdjtns82Z8dbb3fWa5oEZOHAgS5cuZenSpUyYMAGAadOmMWjQIH777Tf+97//sW3bNhYtWkR4eDgjRoxgzZo1pKWlUbduXcaOHZvn37RKlSoMHz48t8XGYrHw2Wef8euvv/LHH39QoUIFPvjgA+6+++5Ced8lgt0OmSnngscpSEv8Rwg5ty/P/ZOQeZX/my7u0G54kZQvIlKcXXdgsdlsDB8+nLZt29KgQYPLHhcbG0twcHCefcHBwcTGxuY+fn7f5Y75t7FjxzJmzJjrLZ2z2Vbqjfrjup9/I3a+3hkfj6v/s0+YMIG9e/fSoEEDXn/9dQB27NgBwMsvv8z7779PtWrVKFOmDDExMdx555289dZbeHp68vXXX9O9e3f27NlDpUqVLvsaY8aMYdy4cbz33nt89NFH9OvXjyNHjlwxgJYo1hw4e+pCuEhPvEzw+Md9a1b+X8fiAj5lz23lwCcQcjJh3x+w/nNo/RS4lvgpkUREbsh1/5YcOnQo27dvZ8WKFQVZzzUZOXJknlab5ORkwsPDi7yOwhQQEICHhwc+Pj6EhIQAsHv3bgBef/117rjjjtxjAwMDady4ce79N954g7lz5/Lzzz/z1FNPXfY1Bg4cSN++fQF4++23mThxIuvWraNLly6F8ZYKl90OWWn/CB7/vgzzj+BxPqBknLm+13L3uRA8fMtdCCK+/wwlZS885lUa/j2HTfZZ+LAeJMXA3t+hbvcb/RcQESnRriuwPPXUUyxYsIBly5ZRsWLFKx4bEhJCXFxcnn1xcXG5H8Lnv8bFxREaGprnmCZNmlzynJ6ennh6Xv91f293V3a+3vm6n38jvN1db/gcLVq0yHM/NTWV1157jV9//ZUTJ06Qk5PD2bNniY6OvuJ5GjVqlHvb19cXf39/4uPjb7i+QnVsI2z7AdIS8l56ST8JOdfTSdsC3mX+ETzK/iNsnA8e/woiHgUwY7K7NzQfACv+D9ZOVWAREbmKfAUWu93OsGHDmDt3LkuWLKFq1apXfU7r1q2JjIzMM9pl8eLFtG7dGoCqVasSEhJCZGRkbkBJTk5m7dq1DBkyJD/lXTOLxXJNl2Ucla+vb577zz//PIsXL+b999+nRo0aeHt788ADD5CVdeXLF+7u7nnuWywWbDZbgddbYPb/CTMfBGvm5Y9x9cwbPq7WAuJdBlxuPERelxaPwsqJcHg5xO2E4Hrm1CEiUgzk61N76NChzJgxg/nz5+Pn55fbxyQgIABvb28A+vfvT4UKFRg7diwAzzzzDLfeeisffPAB3bp1Y9asWWzYsIFPP/0UMD4khw8fzptvvknNmjWpWrUqr776KmFhYfTo0aMA32rx4+HhgdV69c7BK1euZODAgdx7772A0eJy+PDhQq6uiO2PvBBWqrWHGh0v3QLi4QvFZXHL0uFQpxvs+hnWTYXuE8yuSETEYeUrsEyZMgUwhtv+07Rp0xg4cCAA0dHRedacadOmDTNmzOB///sf//3vf6lZsybz5s3L01H3xRdfJC0tjccff5wzZ87Qrl07Fi5c6PRDZ6tUqcLatWs5fPgwpUqVumzrR82aNfnpp5/o3r07FouFV1991bFbSvLrwF8w61xYqd0Nek4HNw+zqyoYEU8YgWXrHOj4mtHiIyIiF8n3JaGrWbJkyUX7evbsSc+ePS/7HIvFwuuvv547GkYMzz//PAMGDKBevXqcPXuWadOmXfK4Dz/8kEceeYQ2bdpQrlw5XnrppXwP9XZYB5fAzL5G/5RaXUtWWAGo3BaCG0Dcdtj8LbQZZnZFIiIO6YYmjnMUJXXiOEdT5P+WB5fCjN6QcxZqdYFeX5fMSdY2fgW/PA2lK8HTUeb1qRERKWL5mThOix+KYzq07EJYqdm55IYVgIY9jUtBZ6JhrznzA4mIODoFFnE8h5bDd73OhZVO0PubkhtWwBgm3ay/cXvdVHNrEZGCY7fDzp9h53xIPmF2NcVe8R3bKyXT4RUw41xYqdERepXwsHJey8Gw6iOjz078bgiqY3ZFInKjVo6HP1+7cD8gHCq2hPBWULEVhDQsWX3yCpkCiziOwyvhu56QnQ7VO0Dv78DdSfodla4Ete+E3Qtg3adw14dmVyQiN2LHvAthpWxNOHXAmNk6KQZ2/GTsd/OCsKZ5Q4xf8OXO6PQUWMQxHFn9j7ByO/SZ4Txh5byIJ4zAsmUmdBgF3qXNrkhErsfRjTD3CeN2xH+g67vGwqnHNsHRdRCz3vh69jRErza280pXMoJLeCsjyIQ0NFZ2FwUWcQDRa+C7ByA7zZgUzhnDCkCVmyGoHsTvhKjvoPVQsysSkfw6Ew0z+xhTMdTsDJ3fNvZ7+kG1W40NjP4tJw9AzNoLISZ+p/H8M9Gw/QfjODdvqNAsbytMqfLmvDeTKbCIuaLXwrf3Q1YqVL0V+sw01tlxRhYLtHocFgw3LgtF/EdDnEWKk4wkY8BAWjwEN4QHvrj8z7DFAuVqGFvTfueen2ysl3Z0/bkgs94455GVxnZemSoXWmHCW0FQfadY8b3kv0NxXDHr/hFWboG+swpmYcHirFEv+HM0nD4M+xZD7WK4craIM7LmwPeDIGEXlAqBB2cbrSr54eUP1W8zNgCbDU7uM35Xnm+FSdht/H44fRi2zTGOc/e9uBXGt2xBvjuHoMAi5ohZD9/cB1kpxqWQvrMVVsBYC6npw7B6kjHEWYFFxPHZ7fD7C3AgEtx94MFZEFDhxs/r4gLlaxtbs4eNfWfPGK0w50PM0Y2QmWQsonp4+YXnBlb7VytMvWLfYqvAIkXv6Ab49lxYqdzO+EtEYeWCVo/B6snGGkoJe6F8LbMrEpErWfMxbPgSsMD9nxsjfwqLd2mo0cHYwGiFSdyTtxUmcQ+cOmhsW2cZx3mUOtcK848OvT6BhVdnIVBgcWDt27enSZMmjB8/vkDON3DgQM6cOcO8efMK5HzX5ehG+OZeyEw21tHpN8doVZALylSB2l1hz29GX5Zu75tdkYhczu5f4Y9XjNud3jRWYC9KLi4QVNfYmg8w9qWfurgVJivFmEH80LILzy1bI28rTPk6Dt0Ko8AiRefYpgthpVIbeFBh5bIinjACy/khzl5XXmNDRExwPAp+HAzYocUjjjOyzycQat5hbAA2q9H3JWbdhRBzcv+FbcsM4zgPP6jY/B+tMC0cagV55wwsdrsx34cZ3H2M3uFXMXDgQJYuXcrSpUuZMGECAIcOHSI1NZUXXniB5cuX4+vrS6dOnfi///s/ypUrB8APP/zAmDFj2L9/Pz4+PjRt2pT58+fz3nvv8dVXXwHG6tgAf//9N+3bty+c9/lvxzfDNz2Ma62VWkO/78GzVNG8dnFU9Vbjr52E3RA1A276j9kVicg/JR0zhi+fnzuq63vX9LvdFC6uEFzf2FoMMvalnzo3GulfrTAHlxjbeeVqXejIG94KytU2WnVM4JyrNWelwdthJlQK/Pf4NbUqJCUl0bVrVxo0aMDrr78OgLu7O3Xr1mXw4MH079+fs2fP8tJLL5GTk8Nff/3FiRMnqFSpEuPGjePee+8lJSWF5cuX07+/sU7No48+SnJyMtOmTQMgMDAQD49rnxb6uldrPh4FX99tDM8Lvwke+iH/veed0frP4dfnjM5zT2007ZeEiPxLZip82QXitkH5uvDoH+AVYHZVN8aaY8wD88+J7U4dvPi4l44U6KSW+Vmt2TlbWIqBgIAAPDw88PHxISQkBIA333yTpk2b8vbbb+ce9+WXXxIeHs7evXtJTU0lJyeH++67j8qVKwPQsGHD3GO9vb3JzMzMPV+ROLEFvr7nXFiJUFjJj0Z94M/XjV8aByIvNO+KiHlsVvjxUSOs+JY3+uEV97ACxjwuoY2MreVgY19a4oU5YWLWG61JJs7A7ZyBxd3HaOkw67Wv05YtW/j7778pVeriSykHDhygU6dOdOjQgYYNG9K5c2c6derEAw88QJkyJl2DPLH1XFg5Y/RI76ewki+epaDpQ7BmMqz9RIFFxBH88V/Yu9BYB6jvLGMq/ZLKt5wxAKB2V+O+yRdknDOwWCzFsrNnamoq3bt35913373osdDQUFxdXVm8eDGrVq1i0aJFfPTRR7zyyiusXbuWqlWrFm2xsduMy0BnT0OFFvDQj+o4ej1aDTaGTO7/ExL3G7Niiog51n5q/PEAcO9Uo1OqMzG5j44uijswDw8PrFZr7v1mzZqxY8cOqlSpQo0aNfJsvr5GALNYLLRt25YxY8awefNmPDw8mDt37iXPV2hit8NX58NKc3j4p5LRZGqGwGpQq7Nxe/1n5tYi4sz2LoKFLxm3O74G9XuYWY1TUmBxYFWqVGHt2rUcPnyYxMREhg4dyqlTp+jbty/r16/nwIED/PHHHwwaNAir1cratWt5++232bBhA9HR0fz0008kJCRQt27d3PNt3bqVPXv2kJiYSHZ2dsEXHbfjXMvKKQhrBg8prNywVo8bXzd/Z6z4KiJFK3Y7/DAI7DZjJuq2w82uyCkpsDiw559/HldXV+rVq0f58uXJyspi5cqVWK1WOnXqRMOGDRk+fDilS5fGxcUFf39/li1bxp133kmtWrX43//+xwcffEDXrsb1x8cee4zatWvTokULypcvz8qVK69SQT7F7YSvukP6SWOmx4fnmtpBq8SofrsxtDArBaJmml2NiHNJiYUZvS+sedbtQ9MvjTgr5xzWLNfliv+W8btg+l2QngihTaD/PIeacKjYW/cZ/PY8lK0JQ9dpiLNIUchKg2l3woko44+GRxfp91oBy8+wZv3WkxsXv/tcy0oihDZWWCkMjfsYs1Ce3AcH/zK7GpGSz2aDnx43wopPWWPNM/1eM5UCi9yYhD1GWElLgJCG8PA8/VAXBk8/Y4gzGCMVRKRw/TkKdi8AVw/oM8PoAC+mUmCR65ew17gMlBYPwQ2h/8/FbvXPYqXVY8bXfYvg5AFzaxEpyTZMg1UfGbd7TIFKN5lbjwAKLHK9EvfBV+fDSgPoP19hpbCVrQ417gDsxrT9IlLwDvxlLIkBcNsr0PABc+uRXE4TWEpA32LT5f4bnj5itKykxkFQfaNlxbesucU5i4hziyBu/tZYz0RECk78LpgzAOxWY2mMW14wuyL5hxIfWNzd3QFITzdpdeYSJD09HazZuM/uDamxEFQPBiisFKnqt0NgdchMhi0a4ixSYFLj4btexs9WpTZw90QNX3YwJX5qfldXV0qXLk18fDwAPj4+WPSfMF/sdjvp6enExx6n9K7vcD1zyFihtP/PxloTUnRcXIyJ5Ba+ZAx1bjlYv1RFblT2WZjZF5Kijc61fb4DN0+zq5J/KfGBBchdnfh8aJHrYM2m9K4ZhOyYCuXrwIBfoFR5s6tyTk0ehL/egMQ9cHAJVL/N7IpEii+bDeb+B45tAK/S8OD36o/noJwisFgsFkJDQwkKCsr/dPRRMyEg3Fht2N1JE3dSDO6z+uF6+gCUq62wYjYvfyO0rPsU1k5VYBG5EX+/CTvngYu70bKiBUYdllMElvNcXV1xdXW99idkJMOiF8CWDe6+UON2qH0n1OzsPP02Th2C73pA8lFjpscBv0CpILOrklaPG4Fl70LjexRYxKtxi5QEm7+D5R8Yt+/+CKq0M7ceuaIS3+n2hmSfhWb9wS8MstNg1y8wbwi8XwO+7AIrJxjDe0uq04eNSeGSjxpTwg/4BfyCza5KAMrVhOod0BBnket0aDn88oxx+5YXoElfc+uRq8p3YFm2bBndu3cnLCwMi8XCvHnzrnj8wIEDsVgsF23169fPPea111676PE6derk+80UOL9guOtDGLETHl8Kt74MIY2MFTujV8PiUTCpBXzUHBa9CkdWg81qdtUF4/QRmN4dkmKgbA0YuAD8QsyuSv4p4gnj6+ZvjDVPROTaJO6D2Q8Zref174P2/zW7IrkG+Q4saWlpNG7cmMmTJ1/T8RMmTODEiRO5W0xMDIGBgfTs2TPPcfXr189z3IoVK/JbWuGxWCCsCdw2Ev6zHIZvhzvfN4aYurjDyf2waiJM6wLv1YC5Q2Dnz8V3nowz0cY8K0nRxhDaAQorDqnGHVCmKmQkwdbZZlcjUjyknYTvekLGGajYypjJVouJFgv57sPStWtXunbtes3HBwQEEBAQkHt/3rx5nD59mkGDBuUtxM0tdzSPwysdbkyT3uoxo5/LgUjY8zvs/QPOnoItM4zN1QOq3gq1uxqbf5jZlV/dmRiY3u1CWBm4APxDza5KLuX8EOc/RhrrCzUfpCHOIleSkwmz+8HpQ1C6srFGkLvX1Z8nDqHIY+UXX3xBx44dqVy5cp79+/btIywsjGrVqtGvXz+io6Mve47MzEySk5PzbKbx8of698J9n8ILB2Dgr9D6KeMvX2sW7F8Mv46AD+vCp+1h6TiI3QaOOPNu0lEjrJw5NxfBwAXFI2Q5s6b9jA7hCbvg0DKzqxFxXHY7zH/KuJzvGQD9vtdox2KmSAPL8ePH+f333xk8eHCe/REREUyfPp2FCxcyZcoUDh06xM0330xKSsolzzN27NjclpuAgADCw8OLovyrc3Uzepl3fgue3gxProUOo41mRyxwfDP8/RZ80g7GN4TfXjDWrcjJMrvyf4SVI0bYGqCwUix4BVzoLLhOqziLXNbSd2HbHHBxg95fQ/naZlck+WSx38AiOxaLhblz59KjR49rOn7s2LF88MEHHD9+HA8Pj8sed+bMGSpXrsyHH37Io48+etHjmZmZZGZm5t5PTk4mPDycpKQk/P398/0+ikRqvHHJaM/v50LK2QuPefpDjY7nhkx3BO8yRVtb0jEjrJw+BGWqGK1EARWLtga5fgl7YHIrsLjA01FQpvJVnyLiVLbOgZ/OrXbefSI0H2BuPZIrOTmZgICAa/r8LrJ5WOx2O19++SUPP/zwFcMKQOnSpalVqxb79++/5OOenp54ehazSdxKBUGzh40t+ywcXAp7foU9C40Vj3f8ZGwWV6jcxggvtbsW/vwayceNVZfPX9MdsEBhpbgpXxuqtTdmvV3/OXR6w+yKRBzHkdUwf6hxu+0zCivFWJFdElq6dCn79++/ZIvJv6WmpnLgwAFCQ0toZ093b6jdxZio6Lk9MDgSbn7OWEzQboXDy42OlBObwOSb4M8xcHSDMYV0QUo+bowGOnUQSlcy+qyUdpDLa5I/51dx3vQ1ZGmhTxEATh6AWQ8a/QnrdocOr5ldkdyAfLewpKam5mn5OHToEFFRUQQGBlKpUiVGjhzJsWPH+Prrr/M874svviAiIoIGDRpcdM7nn3+e7t27U7lyZY4fP87o0aNxdXWlb18nmMjHxQUqtjC2DqOMWUv3LoTdv8KRVUZnyoRdsOJD8A0ygk7tO43RRx4+1/+6ySeMSeFOHYCASkbLSulKBfe+pGjV7GS0kJ05Ylynbz7Q7IpEzHX2NMzoZYzcDGsK936q4cvFXL4Dy4YNG7jttgtrl4wYMQKAAQMGMH36dE6cOHHRCJ+kpCR+/PFHJkyYcMlzHj16lL59+3Ly5EnKly9Pu3btWLNmDeXLO2EP7sCqcNMQYzt7Gvb9CXt+g/1/GpeONn1tbG7exhoytbtCrS75my4/JdYIKyf3G+skDVygfg/FnYurMcR50SvGEOdmAzTEWZxXThbMftj4HedfEfrOurE/8MQh3FCnW0eRn047xVZOFhxZaXTa3fObMQNtLovRQlP7TmMrX/vyH1YpcUaflcS9xg/yoF+NjrZS/J09Ywyfz043Ok5rXRRxRna70Wcl6jvw8INH/4Dg+ld/npgiP5/fCizFkd0OcdsvhJfjm/M+XqbqhU67lVobw63BGKk0vdu5sFLB+FDTonklyy/DYeM043p972/Nrkak6C3/ACJfN0bNPfi9MfJSHJYCi7NJPm70e9nzuzH6yHphyDdepY3+DTXvMH6QE3YbizkO+tWYHE5Klvhd8PFNxi/rZ7aqE7U4l+0/wQ/nZlG/831jNnJxaAosziwzFQ7+fW6pgIWQfjLv435hRp+VstXNqU8K31fdjVlv2z0LHV8zuxqRohGz3mhBtmbCTU9Cl7FmVyTXQIFFDDYrxKwzLhvtXWjs6ztLYaWk27XAWC/FuwyM2GUMoxcpyU4fgc87QFoC1OoKfb4zOqKLw3PIiePEBC6uULm1sWkyMedRu6sxVD0pGrb9YExWKFJSZSQZw5fTEiCkIdz/ucJKCaVB6SIljYsrtDq3XtfaqY650KZIQbBmw5wB5/rmhULf2eBZyuyqpJAosIiURE0fNubqidtmrE4rUtLY7fDb80afPXdfeHA2BFQwuyopRAosIiWRTyA06mXcXjvV3FpECsPqSbBxOmCBB76A0MZmVySFTIFFpKSKeML4uusXSDpqbi0iBWnXAlj0qnG789tGvy0p8RRYREqq4PpQ5WZjQc0NX5pdjUjBOL4ZfhwM2KHlYGMZE3EKCiwiJVmrx42vG6dDdoappYjcsKSjMKMP5JyFGh2hy7taM8uJKLCIlGS17zTWjEo/Cdt/NLsakeuXmQIzekNqLATVhwemXVh2RJyCAotISebqdmGI8zoNcZZiypoD3w8y1lDzDTJGBHlpklBno8AiUtI1GwBuXnBiizHzsUhx88dI2L/YGKr/4CytkeWkFFhESjqfQGj4gHF77Sfm1iKSX2unwrpPjdv3fQoVmptbj5hGFwBFnEGrJ2Dzt7DrZ2N1b/8wsyuSwnDqEGz6CnKywN3LaJFw9/7XbW+jxS339rnH3X0u7HeUqe33/gELXzZudxwD9e42tx4xlQKLiDMIbQSV2kD0KmOI8+3/M7siKUiZKbD8A1g9GaxZN34+F3cjwLh7XSXc5DMI5Xn8H/suNdLnxFaj34rdBs36Q9tnbvx9SbGmwCLiLCKeMALLxulwywvg5ml2RXKjbDbYMhMix0BqnLGv6q0Q1hRyMiD77Lmv6caw9jy3zxpfs9ON/Tn/GPZuy4bMJGMrdJYL4eef4SbpKGSnGe+n24caviwKLCJOo85d4F8Bko/BjrnQuI/ZFcmNiF5jXC45vtm4H1gNOr1lzPp6PR/uNhtYM42Qc1HQ+Ve4yT3m7D8eP3uNx557zJZz7oXtxmM5Z+Hs6bw1lasNvb4GV/cb+qeSkkGBRcRZuLpBi0fgrzeMzreNeuuv1uLoTAz8OfrCvDqe/kaLWcQTN9Zq5uICLucu1xQFa86Vw43NBlXaFl094vAUWEScSfOBsHSc8Vf50Q0Q3tLsiuRaZaXDygnGlnMWsECzh+H2V6FUkNnV5Z+rG7j6gaef2ZVIMaHAIuJMfMsZQ5yjvjMmklNgcXx2u9GasniUcTkPoHJb6DJWKxSLU9E8LCLO5vz6QjvmQkqsubXIlR3bCF92hh8fNcJKQCXo+RUM/FVhRZyOAouIswlrAuE3GZ0eN0wzuxq5lJRYmDsEPrsdYtaCu68xFP2pdVC/h/oeiVNSYBFxRhHnWlk2fGlMMiaOITvDmE/lo+awZYaxr1EfGLbB6FirDqjixNSHRcQZ1b0b/EIh5QTsnAeNepldkXOz241ZiBe9CmeOGPsqtoQu70DFFubWJuIg1MIi4oxc3aHFo8bttVPNrcXZxW6Dr7rDnP5GWPELg/s+g0cWKayI/IMCi4izaj4QXD3g2AY4utHsapxPWiL88gxMvQUOLzdmeL3lRePyT6NexrwoIpJLPxEizqpUeWhwv3F7nVpZikxOFqyaBBObGcsk2G1Q/z54aj3c/gp4+JpdoYhDUmARcWbnhzhv/wlS4sytpaSz22HPQpjSGha9YqzTE9oYBv0OPadB6UpmVyji0BRYRJxZhWZG505btvHXvhSO+N3w7f0wszec3A++5eHuSfDY31C5jdnViRQLCiwizi7iP8ZXDXEueOmn4LcXYUobOBBp9Blq+wwM22RMq+/ianaFIsWGhjWLOLu6d0OpYEiNNYbWNnzA7IqKP2sObJwGf791YQXiOndBpzeMVZVFJN/UwiLi7Nw8jFWcQUOcC8KBv+CTdvDb80ZYCaoH/edDn+8UVkRuQL4Dy7Jly+jevTthYWFYLBbmzZt3xeOXLFmCxWK5aIuNzbuGyeTJk6lSpQpeXl5ERESwbt26/JYmIter+SBwcYej6+DYJrOrKZ5OHoCZfeGbeyFhF3gHQrcP4InlUK292dWJFHv5DixpaWk0btyYyZMn5+t5e/bs4cSJE7lbUNCF5dBnz57NiBEjGD16NJs2baJx48Z07tyZ+Pj4/JYnItfDLxjq32vcXvepubUUNxlJsOh/MDkC9vwGFleIGAJPb4KWg8FVV95FCoLFbrfbr/vJFgtz586lR48elz1myZIl3HbbbZw+fZrSpUtf8piIiAhatmzJpEmTALDZbISHhzNs2DBefvnlq9aRnJxMQEAASUlJ+Pv7X89bEZGjG+Hz242Ooc/uNOZpkcuzWWHzt/DXG5CWYOyr0RE6vw3la5tbm0gxkZ/P7yLrw9KkSRNCQ0O54447WLlyZe7+rKwsNm7cSMeOHS8U5eJCx44dWb169SXPlZmZSXJycp5NRG5QxeZQoTlYs2DTdLOrcWyHV8Knt8IvTxthpWxNePB7eOhHhRWRQlLogSU0NJRPPvmEH3/8kR9//JHw8HDat2/Ppk3GdfLExESsVivBwcF5nhccHHxRP5fzxo4dS0BAQO4WHh5e2G9DxDm0esL4uv4LsGabW4sjOn3EWPNn+p3GGkCeAUaLypBVUKuT2dWJlGiFfnG1du3a1K594S+ONm3acODAAf7v//6Pb7755rrOOXLkSEaMGJF7Pzk5WaFFpCDU72H0x0g5Abt+gQb3mV2RY8hMhRX/B6s+AmsmWFyMtZhuewV8y5ldnYhTMKU3WKtWrVixYgUA5cqVw9XVlbi4vNOCx8XFERIScsnne3p64unpWeh1ijgdN09oMQiWvmt0vnX2wGKzwbY58OdrRogDqHIzdHkHQhqYWpqIszFlHpaoqChCQ0MB8PDwoHnz5kRGRuY+brPZiIyMpHXr1maUJ+Lcmg8CFzeIXg0ntphdjXli1sMXHWHuE0ZYKVMFen8LA35RWBExQb5bWFJTU9m/f3/u/UOHDhEVFUVgYCCVKlVi5MiRHDt2jK+//hqA8ePHU7VqVerXr09GRgaff/45f/31F4sWLco9x4gRIxgwYAAtWrSgVatWjB8/nrS0NAYNGlQAb1FE8sU/FOr1gO0/wNpPoUf+pjAo9pKOGS0q2+YY9z1Kwc3PwU1PgruXqaWJOLN8B5YNGzZw22235d4/35dkwIABTJ8+nRMnThAdHZ37eFZWFs899xzHjh3Dx8eHRo0a8eeff+Y5R+/evUlISGDUqFHExsbSpEkTFi5ceFFHXBEpIhFPGIFl2/dwx+vgW9bsigpf9lmjj8qK/4PsdMACTfpBh1fB79KXp0Wk6NzQPCyOQvOwiBQwux0+bQ8noqDDKKOFoaQ6ewa2zILVkyApxtgXfhN0GWusZi0ihSY/n9+aglFELmaxGKs4z/uPMcS5zTMlb8bWY5tgwxew7UfIOWvs868Id4yBBvcb/wYi4jBK2G8gESkwDe4zhjgnH4PdC4whz8VdVhps/9EIYSeiLuwPqmcsANmkH3j4mFaeiFyeAouIXNr5Ic7L3jOGOBfnwBK/GzZ8aVz6yUwy9rl6GJ2LWzwClW5Si4qIg1NgEZHLa/GI0Qn1yEpjZteQhmZXdO1ysmDXz0ZQOXJhORDKVDGGbjd9SJO+iRQjCiwicnn+YVC3O+yYC2unwj2TzK7o6k4fgY3TjIUJzy9KaHGB2ncaLUbVbgcXU6agEpEboMAiIlcW8R8jsJwf4uwTaHZFF7NZYd8iozVl32Lg3ODHUiHQfAA0GwABFUwtUURujAKLiFxZeASENILYrbDpa2g33OyKLkiJM2raOB2Sj17YX+0243JW7a7g6m5aeSJScBRYROTKLBZjIrn5Q2H959D6KXOHONvtcHi5MdJn9wKw5Rj7vcsYo3xaPAJlq5tXn4gUCgUWEbm6Bg/A4lHGxGp7fzf6tRS1s6chaqZx2efkvgv7K7aClo8aI340db5IiaXAIiJX5+5l9ANZ8aHR+baoAovdfmGCt+0/Qk6Gsd+jFDTqZbSmFKeRSyJy3RRYROTatHwUVk4wLsfE7YDg+oX3WllpRiff9V8YfWfOC25ghJRGvcDTr/BeX0QcjgKLiFybgIpQ9y7YOd+YSK77hIJ/jbidxiWfrbMhM9nY5+oJ9e81gkp4K03wJuKkFFhE5Nq1esIILFtmQ4fRBTPEOScTdv5sXPaJXn1hf2A1I6Q0ftA5VosWkStSYBGRa1e5DQQ3hLhtxsRsbZ++/nOdOnRhgrf0k8Y+iyvUudMIKlXba4I3EcmlwCIi185igYjH4edhsP4zaD0UXFyv/fnWHNj3h3HZZ38kuRO8+YVB84HQ7GFjdl0RkX9RYBGR/GnY0xjifCYa9i6EOt2u/pzkE8YEb5u+MlZ/Pq96B6M1pVYXc+d2ERGHp98QIpI/7t7GEOeV440hzpcLLDYbHFpqtKbs/hXsVmO/d6Cx8GCLQUY/FRGRa6DAIiL51/JRWDXRCCTxuyGozoXH0k9B1AwjqJw6cGF/pdZGa0rduzXBm4jkmwKLiORf6UrG6se7F8C6qdDtQzi64dwEbz+BNdM4zsMPGvc2gkphztsiIiWeAouIXJ+I/xiBZcssiFlvjBw6L6QhtHjU6O/iWcq8GkWkxFBgEZHrU6UdBNWD+J1GWHHzgvr3GZeLKjTXBG8iUqAUWETk+lgs0O0DWPY+1OgAjfsWzERyIiKXoMAiItevcht4+CezqxARJ6BpJEVERMThKbCIiIiIw1NgEREREYenwCIiIiIOT4FFREREHJ4Ci4iIiDg8BRYRERFxeAosIiIi4vAUWERERMThKbCIiIiIw1NgEREREYeX78CybNkyunfvTlhYGBaLhXnz5l3x+J9++ok77riD8uXL4+/vT+vWrfnjjz/yHPPaa69hsVjybHXq1MlvaSIiIlJC5TuwpKWl0bhxYyZPnnxNxy9btow77riD3377jY0bN3LbbbfRvXt3Nm/enOe4+vXrc+LEidxtxYoV+S1NRERESqh8r9bctWtXunbtes3Hjx8/Ps/9t99+m/nz5/PLL7/QtGnTC4W4uRESEnJN58zMzCQzMzP3fnJy8jXXIyIiIsVPkfdhsdlspKSkEBgYmGf/vn37CAsLo1q1avTr14/o6OjLnmPs2LEEBATkbuHh4YVdtoiIiJioyAPL+++/T2pqKr169crdFxERwfTp01m4cCFTpkzh0KFD3HzzzaSkpFzyHCNHjiQpKSl3i4mJKaryRURExAT5viR0I2bMmMGYMWOYP38+QUFBufv/eYmpUaNGREREULlyZebMmcOjjz560Xk8PT3x9PQskppFRETEfEUWWGbNmsXgwYP5/vvv6dix4xWPLV26NLVq1WL//v1FVJ2IiIg4siK5JDRz5kwGDRrEzJkz6dat21WPT01N5cCBA4SGhhZBdSIiIuLo8t3Ckpqamqfl49ChQ0RFRREYGEilSpUYOXIkx44d4+uvvwaMy0ADBgxgwoQJREREEBsbC4C3tzcBAQEAPP/883Tv3p3KlStz/PhxRo8ejaurK3379i2I9ygiIiLFXL5bWDZs2EDTpk1zhySPGDGCpk2bMmrUKABOnDiRZ4TPp59+Sk5ODkOHDiU0NDR3e+aZZ3KPOXr0KH379qV27dr06tWLsmXLsmbNGsqXL3+j709ERERKAIvdbrebXcSNSk5OJiAggKSkJPz9/c0uR0RERK5Bfj6/tZaQiIiIODwFFhEREXF4CiwiIiLi8BRYRERExOEpsIiIiIjDU2ARERERh6fAIiIiIg5PgUVEREQcngKLiIiIODwFFhEREXF4CiwiIiLi8BRYRERExOEpsIiIiIjDU2ARERERh6fAIiIiIg5PgUVEREQcngKLiIiIODwFFhEREXF4CiwiIiLi8BRYRERExOEpsIiIiIjDU2ARERERh6fAIiIiIg5PgUVEREQcngKLiIiIODwFFhEREXF4CiwiIiLi8BRYRERExOEpsIiIiIjDU2ARERERh6fAIiIiIg5PgUVEREQcngKLiIiIOLx8B5Zly5bRvXt3wsLCsFgszJs376rPWbJkCc2aNcPT05MaNWowffr0i46ZPHkyVapUwcvLi4iICNatW5ff0kRERKSEyndgSUtLo3HjxkyePPmajj906BDdunXjtttuIyoqiuHDhzN48GD++OOP3GNmz57NiBEjGD16NJs2baJx48Z07tyZ+Pj4/JYnIiIiJZDFbrfbr/vJFgtz586lR48elz3mpZde4tdff2X79u25+/r06cOZM2dYuHAhABEREbRs2ZJJkyYBYLPZCA8PZ9iwYbz88stXrSM5OZmAgACSkpLw9/e/3rcjIiIiRSg/n9+F3odl9erVdOzYMc++zp07s3r1agCysrLYuHFjnmNcXFzo2LFj7jH/lpmZSXJycp5NRERESq5CDyyxsbEEBwfn2RccHExycjJnz54lMTERq9V6yWNiY2Mvec6xY8cSEBCQu4WHhxda/SIiImK+YjlKaOTIkSQlJeVuMTExZpckIiIihcitsF8gJCSEuLi4PPvi4uLw9/fH29sbV1dXXF1dL3lMSEjIJc/p6emJp6dnodUsIiIijqXQW1hat25NZGRknn2LFy+mdevWAHh4eNC8efM8x9hsNiIjI3OPEREREeeW78CSmppKVFQUUVFRgDFsOSoqiujoaMC4XNO/f//c4//zn/9w8OBBXnzxRXbv3s3HH3/MnDlzePbZZ3OPGTFiBJ999hlfffUVu3btYsiQIaSlpTFo0KAbfHsiIiJSEuT7ktCGDRu47bbbcu+PGDECgAEDBjB9+nROnDiRG14Aqlatyq+//sqzzz7LhAkTqFixIp9//jmdO3fOPaZ3794kJCQwatQoYmNjadKkCQsXLryoI66IiIg4pxuah8VRaB4WERGR4seh5mERERERuVEKLCIiIuLwFFhERETE4SmwiIiIiMNTYBERERGHp8AiIiIiDk+BRURERByeAouIiIg4PAUWERERcXgKLCIiIuLwFFhERETE4SmwiIiIiMNTYBERERGHp8AiIiIiDk+BRURERByeAouIiIg4PAUWERERcXgKLCIiIuLwFFhERETE4SmwiIiIiMNTYBERERGHp8AiIiIiDk+BRURERByeAouIiIg4PAUWERERcXgKLCIiIuLwFFhERETE4SmwiIiIiMNTYBERERGHp8AiIiIiDk+BRURERByeAouIiIg4PAUWERERcXgKLCIiIuLwriuwTJ48mSpVquDl5UVERATr1q277LHt27fHYrFctHXr1i33mIEDB170eJcuXa6nNBERESmB3PL7hNmzZzNixAg++eQTIiIiGD9+PJ07d2bPnj0EBQVddPxPP/1EVlZW7v2TJ0/SuHFjevbsmee4Ll26MG3atNz7np6e+S1NRERESqh8t7B8+OGHPPbYYwwaNIh69erxySef4OPjw5dffnnJ4wMDAwkJCcndFi9ejI+Pz0WBxdPTM89xZcqUub53JCIiIiVOvgJLVlYWGzdupGPHjhdO4OJCx44dWb169TWd44svvqBPnz74+vrm2b9kyRKCgoKoXbs2Q4YM4eTJk5c9R2ZmJsnJyXk2ERERKbnyFVgSExOxWq0EBwfn2R8cHExsbOxVn79u3Tq2b9/O4MGD8+zv0qULX3/9NZGRkbz77rssXbqUrl27YrVaL3mesWPHEhAQkLuFh4fn522IiIhIMZPvPiw34osvvqBhw4a0atUqz/4+ffrk3m7YsCGNGjWievXqLFmyhA4dOlx0npEjRzJixIjc+8nJyQotInLdDiak8uXKQ/RvXYVawX5mlyMil5CvFpZy5crh6upKXFxcnv1xcXGEhIRc8blpaWnMmjWLRx999KqvU61aNcqVK8f+/fsv+binpyf+/v55NhGR65F0NpuB09bz7ZpoBk1bz5n0rKs/SUSKXL4Ci4eHB82bNycyMjJ3n81mIzIyktatW1/xud9//z2ZmZk89NBDV32do0ePcvLkSUJDQ/NTnohIvthsdp6bE0X0qXQAjp05y3NztmCz2U2uTET+Ld+jhEaMGMFnn33GV199xa5duxgyZAhpaWkMGjQIgP79+zNy5MiLnvfFF1/Qo0cPypYtm2d/amoqL7zwAmvWrOHw4cNERkZyzz33UKNGDTp37nydb0tE5Oo+WXaAP3fF4+Hmwrj7G+Hh5kLk7ng+X3HQ7NJE5F/y3Yeld+/eJCQkMGrUKGJjY2nSpAkLFy7M7YgbHR2Ni0veHLRnzx5WrFjBokWLLjqfq6srW7du5auvvuLMmTOEhYXRqVMn3njjDc3FIiKFZtWBRN7/Yw8AY+6uT6+W4WTbbLwydzvvLtxD88plaF450OQqReQ8i91uL/Ztn8nJyQQEBJCUlKT+LCJyVbFJGdz10XISU7N4oHlF3nugERaLBbvdztOzovhly3FCA7z49embCfT1MLtckRIrP5/fWktIRJxKttXG0BmbSEzNom6oP2/c0wCLxQKAxWJh7H0NqVbOlxNJGYyYE6X+LCIOQoFFRJzK2N92s/HIafy83JjSrxneHq55Hi/l6cbkfs3wdHNhyZ4Epi5TfxYRR6DAIiJOY8HW43y58hAAH/RsTJVyvpc8rm6oP2Purg/A+4v2sP7wqSKrUUQuTYFFRJzC/vgUXvphKwBD2lenU/0rzx3Vu2U4PZqEYbXZeWrGJk6mZhZFmSJyGQosIlLipWXm8J9vN5GWZaV1tbI8d0etqz7HYrHw1r0NqV7el7jkTJ7V/CwiplJgEZESzW638/JP29gfn0qwvycT+zbFzfXafvX5errxcb/meLm7sGxvAlOWHijkakXkchRYRKRE+2rVYX7Zchw3FwuTH2xGeb/8ze9UO8SP1+9pAMAHi/aw5uDlV5IXkcKjwCIiJdbGI6d567ddAIy8sy4tqlzfRHA9m1fkvmYVsNnh6ZmbSUhRfxaRoqbAIiIl0snUTIZ+t4lsq51uDUN5pG2V6z6XxWLhzR4NqBlUiviUTJ6dHYVV/VlEipQCi4iUOFabnadnbSY2OYNq5X1599xMtjfCx8ONj/s1w9vdlRX7E5n896VXkxeRwqHAIiIlzv8t3svK/Sfx8XBl6kPNKeWZ72XTLqlmsB9v9jD6s4z/cy+rDiQWyHlF5OoUWESkRIncFcekc60fY+9rSM1gvwI9//3NK9KzecVz/VmiiE/JKNDzi8ilKbCISIkRfTKdZ2dHATCwTRXuaVKhUF7n9XsaUDvYj8TUTIbPUn8WkaKgwCIiJUJGtpUh320kOSOHppVK89876xbaa3l7uDK5XzN8PFxZdeAkEyP3FdpriYhBgUVESoTR83ew43gygb4eTH6wGR5uhfvrrUZQKd661+jPMvGvfazYp/4sIoVJgUVEir0562OYvSEGiwUm9mlKWGnvInnde5tWpE/LcOx2GD57M/HJ6s8iUlgUWESkWNt+LIlX528H4Lk7atGuZrkiff3X7q5PnRA/ElOzGDZzMzlWW5G+voizUGARkWIrKT2bJ7/bRGaOjdvrBPFk+xpFXoOXu9GfxdfDlbWHTjFB/VlECoUCi4gUSzabnee+jyL6VDoVy3jzf72a4OJyY5PDXa/q5Uvx9n0NAZj0936W7U0wpQ6RkkyBRUSKpSlLD/Dnrng83Fz45KHmBPi4m1rPPU0q8GBEpXP9WaKITVJ/FpGCpMAiIsXOyv2JfLBoDwBv3FOfBhUCTK7IMOquetQL9edUWhZPqz+LSIFSYBGRYiU2KYOnZ27GZjdWUe7dspLZJeU635+llKcb6w6f4sPFe80uSaTEUGARkWIjK8fGk99t5GRaFnVD/Xnj3Lo+jqRqOV/eud/oz/LxkgP8vSfe5IpESgYFFhEpNsb+votN0Wfw83Ljk4ea4eXuanZJl3RXozAevqkyACNmR3Ei6azJFYkUfwosIlIs/LLlONNWHgbgw15NqFzW19yCruJ/d9WlQQV/TqdnM2zGZrLVn0XkhiiwiIjD2x+fwks/bgXgyfbVuaNesMkVXZ2nmyuTH2yGn6cbG46c5v1znYRF5PoosIiIQ0vNzOGJbzaSnmWlTfWyjLijltklXbPKZX0Z90AjAKYuPUjkrjiTKxIpvhRYRMRh2e12Xv5xKwcS0gj292Ri36a4uRavX1tdG4YysE0VAJ77fgvHzqg/i8j1KF4/+XJd7HY7i3bE8t3aI2RkW80uR+SaTV91mAVbT+DmYuHjfs0oV8rT7JKuy8g769C4YgBn0rMZNmOT+rOIXAcFlhJu+7Ekek9dw+PfbOSVudvp9H/LWLg9FrvdbnZpIle08cgp3vp1FwD/vbMuzSsHmlzR9fN0c2XSg83w83JjU/QZxi3cbXZJIsWOAksJlZCSycs/bqX7pBWsO3wKL3cXypXyJPpUOv/5diP9Pl/L7thks8sUuaTE1EyGfreZHJudbo1CGdS2itkl3bDwQB/ee6AxAJ8tP8TinerPIpIfCiwlTFaOjU+XHeC295cwa30Mdjvc3TiMv55rz9IX2vPUbTXwcHNh1YGT3DlhOf+bt41TaVlmly2Sy2qz8/TMzcQmZ1C9vC/v3t8Ii8WcRQ0LWpcGITzStioAz3+/haOn002uSKT4sNhLwLWB5ORkAgICSEpKwt/f3+xyTGG324ncFc+bv+7k8Enjl2DDCgGM7l6PFlXyNqXHnErn7d928fv2WAD8vdx49o5aPHRTZdyLWYdGKXne+2M3k/8+gI+HK/OHtqVmsJ/ZJRWorBwbPaeuZkvMGRqHl+b7J1rj4aafO3FO+fn8vq6fksmTJ1OlShW8vLyIiIhg3bp1lz12+vTpWCyWPJuXl1eeY+x2O6NGjSI0NBRvb286duzIvn37rqc0p7Q3LoX+X65j8NcbOHwynfJ+nox7oBHzh7a9KKyA0TQ95aHmzHzsJuqE+JGckcOYX3bSdcJylu5NMOEdiBj+3BnH5L8PAPDO/Y1KXFgB8HBzYVLfpvh7ubEl5gzv/K7+LCLXIt+BZfbs2YwYMYLRo0ezadMmGjduTOfOnYmPv/x6Gf7+/pw4cSJ3O3LkSJ7Hx40bx8SJE/nkk09Yu3Ytvr6+dO7cmYwMLc9+JWfSsxg9fztdJyxn+b5EPFxdGNK+On8/355eLcJxcblyM3rr6mX59embefvehgT6erA/PpUBX67j0enrOZiQWkTvQsQQfTKdZ+dEATCwTRXubhxmbkGFKDzQhw96NQHgy5WH+GNHrLkFiRQD+b4kFBERQcuWLZk0aRIANpuN8PBwhg0bxssvv3zR8dOnT2f48OGcOXPmkuez2+2EhYXx3HPP8fzzzwOQlJREcHAw06dPp0+fPletydkuCeVYbXy3NpoPF+8l6Ww2AJ3rB/PfO+te93TlSWezmRi5j69WHSbHZsfd1cLANlUY1qEm/l7uBVm+yEUysq3c9/Eqdp5Iplml0sx63Dkuk7z1604+W34IPy83fnv6ZsIDfcwuSaRIFdoloaysLDZu3EjHjh0vnMDFhY4dO7J69erLPi81NZXKlSsTHh7OPffcw44dO3IfO3ToELGxsXnOGRAQQERExGXPmZmZSXJycp7NWSzfl8CdE5cz+ucdJJ3Npk6IHzMGRzD14RY3tLZKgLc7r95Vj4XDb+G22uXJttr5bPkhbntvCbPWRWO1FfuuTuLARs3fzs4TyZT19WByv2ZOEVYAXuxSh6aVSpOSkcPQGZvIzNE8SSKXk6/fComJiVitVoKD867jERwcTGzspZs0a9euzZdffsn8+fP59ttvsdlstGnThqNHjwLkPi8/5xw7diwBAQG5W3h4eH7eRrF0KDGNwV9t4OEv1rE3LpUyPu680aMBC4a1o02NcgX2OjWCSjFtUCumDWxJtfK+nEzL4uWfttH9oxWsPXiywF5H5LzZ66OZs+EoLhaY2LcpoQHeZpdUZNxdXZj0YDNK+7iz9WgSY39TfxaRyyn0P2Nat25N//79adKkCbfeeis//fQT5cuXZ+rUqdd9zpEjR5KUlJS7xcTEFGDFjiUlI5uxv+2i0/8t5c9dcbi5WBjUtgpLnr+Nh2+qXGjTlN9WJ4g/ht/Cq3fVw8/LjZ0nkun96RqGztikoZhSYLYfS+LV+UaL63OdatO2AMN3cVGhtDcf9jLmZ5m+6jC/bzthckUijilfn3blypXD1dWVuLi8Ex7FxcUREhJyTedwd3enadOm7N+/HyD3efk5p6enJ/7+/nm2ksZqszN7fTS3vb+EqcsOkm21c2ut8iwcfjOju9cnwKfw+5W4u7rwaLuqLHm+PQ9GVMLFAr9uPUGHD5by4aI9pGflFHoNUnIlpWcz5LuNZOXY6FAniCG3Vje7JNPcXieYJ26tBsCLP2zlyMk0kysScTz5CiweHh40b96cyMjI3H02m43IyEhat259TeewWq1s27aN0NBQAKpWrUpISEiecyYnJ7N27dprPmdJs+7QKe6etIKXftxGYmoW1cr5Mm1gS756pBU1gop+mGfZUp68fW9DFgy7mZuqBZKZY2PiX/u5/f2lzNt8TNP8S77ZbHZGzIki5tRZwgO9+bBXk6uOaivpnu9UmxaVy5CSafRn0bpfInnl+3rCiBEj+Oyzz/jqq6/YtWsXQ4YMIS0tjUGDBgHQv39/Ro4cmXv866+/zqJFizh48CCbNm3ioYce4siRIwwePBgAi8XC8OHDefPNN/n555/Ztm0b/fv3JywsjB49ehTMuywmjp5OZ+iMTfSaupodx5Px83Ljf93qGh1h6wSZXR71wvyZ+dhNTOnXjIplvIlNzmD47Cjun7KKLTFnzC5PipEpSw8QuTseDzcXpvRrXiQtho7O3dWFjx5sShkfd7YfS85dR0lEDG75fULv3r1JSEhg1KhRxMbG0qRJExYuXJjbaTY6OhoXlws56PTp0zz22GPExsZSpkwZmjdvzqpVq6hXr17uMS+++CJpaWk8/vjjnDlzhnbt2rFw4cKLJpgrqdKzcvhkyQGmLjtIZo4NFwv0aVWJ5+6oRVkHW53WYrHQtWEot9UJ4osVh5j89342RZ/hnskreaB5RV7sXJsgf+f4vsn1WbEvkQ8W7QHgzXsa0KBCgMkVOY7QAG8+7N2EQdPW882aI0RUC+SuRiV3PhqR/NDU/Cay2+3MjzrOO7/vJjbZmCTvpmqBjLqrPvXCisf7iEvO4N2Fu/lp0zEAfD1cGXp7DR5pWxUvd1eTqxNHcyLpLN0mruBUWha9W4Tz7gONzC7JIY1buJuPlxyglKcbvwxrR9Vy1z9lgYgjy8/ntwKLSaJizjDmlx1sjj4DQMUy3rxyZ126NAgplgu9bY4+zZhfdhJ17tJQpUAfXulWl071govl+5GCl5Vjo/enq9kcfYb6Yf78OKSNQu1l5FhtPPjZWtYdPkW9UH9+elL/VlIyKbA4sH+3SPh4uDL0tho82q74t0jYbHbmRR3j3YW7iUvOBKBN9bKM6l6POiGO/X2RwvfazzuYvuow/l5uLBh2M5XKalbXK4lNyqDbxOWcTMuiX0Ql3rq3odkliRQ4BRYHlJFtze3zkZ5l9P6/r1kFXupSh+AS1ucjLTOHKUsO8Onyg2Sd65PTL6Iyz95Ri0BfD7PLExP8vOU4T8/cDMDn/VvQsV7wVZ4hAMv2JjBg2jrsdpjQpwn3NKlgdkkiBUqBxYHY7XYWbo/lrd92cfT0WQCaVirN6O71aRJe2tziClnMqXTG/r6L37YZMxb7e7nx7B21eOimyrgX0oR34nj2xaVwz+SVpGdZGXpbdV7oXMfskoqVDxbt4aO/9uPr4crPw9pRvXwps0sSKTAKLA5i5/FkXl+wgzUHTwEQ4u/Fy13rcE+TMKfq17Hm4EnG/LKTXSeMNZ9qBJXi1bvqcWut8iZXJoUtNTOHeyat4EBCGm1rlOXrRyJwdfL5VvLLarPT7/M1rDl4ijohfswb2rbYXz4WOU+BxWQnUzP5YPFeZq2LxmYHTzcXnrilGv9pXx0fj3yPJC8RjJl7Y3h/0R5OpWUB0KFOEK90q0s1/cVYItntdp6auZlft54gxN+LBU+3o5yDDdMvLuKTM7hz4nISU7Po2yqcsfdpdJWUDAosJsnKsfH16sNMiNxHSoYxbX23RqGM7FqHimXUwRAg6Ww2EyP38dWqw+TY7Li7WhjYpgrDOtTE30uTh5UkX644xOsLduLmYmH2E61pXrmM2SUVayv3J/LQF2ux22F87yb0aKr+LFL8KbCY4O/d8byxYCcHE401QOqH+TO6e31aVQ00pR5HdyAhlTcX7OTvPQkAlPX14IXOtenZIlyXDEqADYdP0efTNeTY7LzWvR4D21Y1u6QS4f8W72VC5D58PFz5+al21AhS66QUbwosRWh/fApvLNjF0r3GB2+5UsYH7wPN9cF7Lf7ecy7oJSjolRSJqZl0m7icuORMujcOY2KfJk7VZ6swWW12Hv5iLasOnKR2sNGfxdtD/Vmk+FJgKQJJ6dmMj9zLN6uP5F7aGNS2Kk/dXkOXNvIp22rj69VHGP/nXl1KK+ZyrDYe/mIdqw+epEZQKeYPbYuvp3P22yos8SkZ3DlhBYmpmfRqUZFxDzQ2uySR66bAUohyrDZmro/hw0V7OJ2eDUDHusG80q2ups++QSdTM/lw8V5mqrNysXV+SnnjkkVbU1YXdwarDiTy0Odrsdnhg56Nub95RbNLErkuCiyFZNX+RF5fsJPdsSkA1Awqxaju9bi5pobnFqRLDQcfeWcd7m7sXMPBi5vFO+N47OsNAHzUtyndG2vRvsI0MXIfHy7ei7e7EQ5rBiscSvGjwFLAjpxM461fd7FoZxwAAd7ujLijFv0iKuGmCdAKhd1u548dsbz564UJ95pXLsOou+rRuIRPuFccHTmZxl0frSAlI4dBbaswunt9s0sq8aw2OwO+XMeK/YnUDCrF/KfaqiVSih0FlgKSmpnD5L/388XyQ2RZbbi6WHgoohLDO9aijKaYLxKXWtKgZ/OKvNq9nvoKOYiMbCv3fryKXSeSaV65DDMfuwkPNwX5opCYmsmdE5YTn5LJ/c0q8kEv9WeR4iU/n9/6rXIFCSmZfL78IFlWGzfXLMfvz9zMmHsaKKwUIS93Y3HIv59vz33NjHknvt94lO4frWD7sSSTqxO73c7/5m1n14lkyvp6MPnBZgorRahcKU8m9m2KiwV+3HSUORtizC5JpNCoheUqPl9+kCplfelQN0j9JxzAxiOneHpmFMfOnMXDzYXR3evxYKtK+t6YwGazM/GvfYz/cx8uFvj20Qja1ChndllOafLf+3nvjz14ubswf2g7aoeoP4sUD7okJCVaUno2z30fxZ+74gG4p0kYb9/bUMNni1BccgbPzo5i1YGTALzYpTZPtq9hclXOy2azM3D6epbtTaB6eV9+fqqdfh6kWFBgkRLPbrfz+fJDvLNwN1abnerlffm4X3P9ZVkE/twZxws/bOF0ejbe7q6Mubs+PVtUVCuXyU6mZtJt4gpikzO4uWY5pjzUnFIKLaay2YzBA4mpmZT38zS2Ul6U9/PUhH/nKLCI09hw+BRPzdhMbHIGXu4uvNmjIQ9oTopCkZFt5e3fdvH16iOAMSvxxL5Nqa7FKx3GhsOnePiLdZzNtlI31J9pA1sSEuBldllOKSPbynNztvDrthOXfLyUpxvl/TwpV8rjXJDxvBBq/hFsypbywL0Ej0ZVYBGncjI1k2fnbGHZueURerWoyJi7G+gvmAK0Ny6FYTM2syfOmINocLuqvNClNp5u+jd2NFuPnuGR6RtITM0kxN+LaYNaUjdUvxeLUmJqJo99vYHN0Wdwd7Vwa63ynErLIiE1k/jkTDJzbPk6Xxkf938EmbzBplypC/vL+HjgUsyWhFFgEadjs9n5eMl+Ply8F5sdagf78fFDzfTX/w2y2+18uzaaNxfsJDPHRrlSHrzfszHtaweZXZpcQcypdAZNX8/++FRKebrxcb9m3FJLE1wWhX1xKQyavp6jp88S4O3OJw81p3X1srmP2+12UjNzSEjJNLbUTBLPff3nvoSUTBJTs7Darv0j2tXFclGLTbl/Bpx/3C7l6eYQl3EVWMRprTqQyNMzo0hMzcTXw5W372vIPU0qmF1WsXQ6LYuXftyaO2HirbXK837PxpT38zS5MrkWSenZPPHtBtYcPIWri4Wx9zakV8tws8sq0VbsS2TIdxtJycihclkfvhzY8ob+aLLZ7JxOzyIxNetckMm4EGpyw47RcnMqLStf5/Zyd7kQaC66HJW3BcfLvfBaUhVYxKnFp2Tw9MzNuVP794uoxKt31SvUH7qSZvWBkzw7O4rY5AzcXS281KUOj7StWuyam51dZo6Vl3/cxtzNxwB46rYaPNeplkP8ZV3SzFoXzf/mbSfHZqdF5TJ82r8FgUU4Z1e21cbJSwSb3LDzj5ab1MycfJ3bz8stN8h882hEgc61pMAiTs9qszP+z71M+ns/drvRQfTjfs2oXFYLVF5JttXG+D/38vGSA9jtUK28LxP7NKVBhQCzS5PrZLfb+XDxXj76az8APZqE8e4DjdT/qIDYbHbeW7SHKUsOAMY0C+/e38ih/0BKz8rJbZlJ+PflqH9dpsr6R38bXw9XdrzepUBrUWAROWfp3gSenR3FqbQs/DzdeK9nI7o0CDW7LIcUfTKdp2dtJirmDAC9W4Qz+u56Wp+mhJizPob/zt1Gjs1ORNVAPn24BQE+Wt7iRvx7JNDTHWrybMeaJaYFy263k5xxob9NWmYOHesFF+hrKLCI/MOJpLMMm7GZDUdOA/BI26q83LWOppD/h/lRx3hl7nZSM3Pw83Jj7H0NuauRVlsuaZbtTeDJ7zaRmplD9fK+TB/UivBAH7PLKpYSUoyRQFExxkigd+5rxP2aUiHfFFhE/iXbauP9P/YwddlBAJqEl2Zyv2ZUKO1tcmXmSs3MYdT87fy0yejj0KJyGcb3aULFMvoQK6l2xyYzaNp6TiRlUK6UB18MaKkV0PPp3yOBpj7cnJuqlb36E+UiCiwil7F4ZxzPzYkiOSOH0j7ufNirMbfXKdgmzuJi69EzPD1zM4dPpuNigWG312TY7TVwK8GTVIkhNimDQdPXs+tEMt7urkzs25Q7Cripv6RasS+RId9uJCUzhyrnRgJV0/QJ102BReQKYk6l89SMTWw5aqz2PKR9dZ67o5bTfFDbbHY+XX6Q9//YQ47NTliAF+P7NKVV1UCzS5MilJqZw9DvNrF0bwIuFhjdvT4D2lQxuyyH9s+RQC2rlGHqw0U7EqgkUmARuYrMHCtjf9vN9FWHAWhVJZCPHmxKsH/JnsY8PjmDEXO2sGJ/IgB3Ngxh7L2N1PnSSWVbbYyav52Z62IAeLRdVV65s66Gr/+LzWbn3T92M3WpcUlZI60KjgKLyDX6desJXvpxK6mZOZT19WB8nybcXLNkzggauSuOF37Yyqm0LLzdXRndvR69W4aXmBENcn3sdjtTlh5g3MI9AHSpH8L4Pk0celhuUTqbZWXEnCh+3x4LwPCONXmmQ8kZCWQ2BRaRfDiUmMaT321i14lkLBZ4+vaaPN2hJq4l5K/MjGwr7/x+oTWpXqixaGGNIF13lwvmRx3jhe+3kmW10bRSaT7v34KypZx7VuP4lAwe+3ojW2LO4OHqwrsPNOTephoJVJAUWETyKSPbyphfdjJzXTQAbWuUZXzvpsV+Gvp9cSkMm7mZ3bHGooWPtK3KS121aKFc2tqDJ3n8m40knc2mUqAP0wc5b4fSvXEpDJq2nmNnzlLax51PH26hfl6FQIFF5DrN3XyU//60nbPZVoL8PPmob1MiiuFwRbvdzox10byxYCcZ2TbK+hqLFt5WR4sWypXtj09l0PR1xJwyPqg/69+CllWc64N6+b4Envx2EymZOVQt58uXA1tStZxmyS4M+fn8vq5hEZMnT6ZKlSp4eXkRERHBunXrLnvsZ599xs0330yZMmUoU6YMHTt2vOj4gQMHYrFY8mxduhTs9L8i1+LephX5+am21AwqRXxKJn0/W8PHS/Zjy8eKqWY7k57FkG838crc7WRk27i5Zjl+H36zwopckxpBpZj7ZFsah5fmTHo2/T5fyy9bjptdVpGZsTaagdPWk5KZQ6uqgfw0pI3CioPId2CZPXs2I0aMYPTo0WzatInGjRvTuXNn4uPjL3n8kiVL6Nu3L3///TerV68mPDycTp06cezYsTzHdenShRMnTuRuM2fOvL53JHKDagb7Mf+pttzXtAI2O4xbuIdHv1rP6XyuhmqGNQdP0nXCchbuiMXd1cIrd9blq0GtCPIr2aOfpGCVK+XJrMduolO9YLJybAybuZkpSw5QAhrkL8tms/P2b7v479xtWG127m1agW8ebUUZDVt2GPm+JBQREUHLli2ZNGkSADabjfDwcIYNG8bLL7981edbrVbKlCnDpEmT6N+/P2C0sJw5c4Z58+bl/x2gS0JSOOx2O3M2xDBq/g4yc2yEBXgxqV8zmlUqY3ZpF8mx2pgQuY/Jf+/HZoeq5YxFCxtW1KKFcv2sNjtv/rqTaSsPA8bK52Purl/i5iw6m2Vl+OzN/LEjDtBIoKJUaJeEsrKy2LhxIx07drxwAhcXOnbsyOrVq6/pHOnp6WRnZxMYmPea6JIlSwgKCqJ27doMGTKEkydPXvYcmZmZJCcn59lECprFYqF3y0rMfbItVcv5cjwpg16frObz5Qcd6i/NmFPp9Jq6mo/+MsJKz+YVWTCsncKK3DBXFwuju9dn1F31sFjgu7XRPPb1BtIyc8wurcDEp2TQ59PV/LEjDg9XF8b3bsLwjrUUVhxQvgJLYmIiVquV4OC8UzgHBwcTGxt7Ted46aWXCAsLyxN6unTpwtdff01kZCTvvvsuS5cupWvXrlit1kueY+zYsQQEBORu4eHh+XkbIvlSL8yfn59qS7dGoeTY7Lz56y7+860xksJsP285zp0TlrMp+gx+nm581Lcp7/VsjK+nVliWgvNIu6p88lBzvNxd+HtPAr2mriYuOcPssm7YntgU7p28ii1Hkyjj4863gyPo0bSC2WXJZeTrktDx48epUKECq1atonXr1rn7X3zxRZYuXcratWuv+Px33nmHcePGsWTJEho1anTZ4w4ePEj16tX5888/6dChw0WPZ2ZmkpmZmXs/OTmZ8PBwXRKSQmW32/l2zRHeWLCLLKuN8EBvPn6wuSktGWmZOYz+eQc/bDwKQLNKpZnQp6lW3pVCtTn6NIO/2sDJtCzCAryYNqgVtUP8zC7ruizdm8DQcytXVy3ny7SBLamizrVFrtAuCZUrVw5XV1fi4uLy7I+LiyMkJOSKz33//fd55513WLRo0RXDCkC1atUoV64c+/fvv+Tjnp6e+Pv759lECpvFYuHh1lX4YUhrKpbxJubUWe6fsopvVh8u0ktE244mcddHK/hh41FcLPD07TWY80RrhRUpdE0rlWHuk22pVt64RPrAlFWsPLfMQ3Hy3dojPDJ9Pan/GAmksOL48hVYPDw8aN68OZGRkbn7bDYbkZGReVpc/m3cuHG88cYbLFy4kBYtWlz1dY4ePcrJkycJDQ3NT3kiRaJRxdL8Ouxm7qgXTJbVxqvzdzBs5mZSC/m6vs1m59NlB7hvykoOJaYRGuDFzMduYkSn2iWuE6Q4rkplffhpSBtaVQ0kJTOHAV+u4/sNMWaXdU1sNjtv/bqTV+Zux2qzc59GAhUr+R4lNHv2bAYMGMDUqVNp1aoV48ePZ86cOezevZvg4GD69+9PhQoVGDt2LADvvvsuo0aNYsaMGbRt2zb3PKVKlaJUqVKkpqYyZswY7r//fkJCQjhw4AAvvvgiKSkpbNu2DU/Pq880qlFCYga73c4XKw7xzu+7ybHZqVbOl8n9mlE3tOD/D8anZPDcnC0s32f8Ndulfgjv3N+Q0j76RSvmyMyx8sL3W/n53BwtT3eoybMdHXdkzb9HAo24oxbDbq/hsPU6i/x8fue7Z17v3r1JSEhg1KhRxMbG0qRJExYuXJjbETc6OhoXlwt/7U2ZMoWsrCweeOCBPOcZPXo0r732Gq6urmzdupWvvvqKM2fOEBYWRqdOnXjjjTeuKayImMVisTD45mo0rVSap2Zs5mBiGj0mr+SNexrQs0XFAvtF+PfueJ7/fgsn07Lwcndh1F316dtKixaKuTzdXBnfuwnhgd5M/vsAEyP3cfR0Ou/c1wgPN8dq8YtPzmDw1xvYejQJD1cX3uvZiHuaqHNtcaOp+UUKwKm0LJ6dHcXSvQkA3N+sIm/0qI+Px/WP1snMMRYtPD8HRp0QPz7q25SawcWzk6OUXDPXRfO/ecZlljbVyzLloeYEeLubXRYAu2OTeWTaeo4nZVDm3FIDLZxsqQFHprWERExgs9mZsvQAHyzag80OtYJL8XG/ZtQIyn/A2B+fwrCZUew6YcwxNLBNFV7uWgcvdy1aKI5pyZ54hn63ibQsK7WCS/HlwJZULGNuR/Ale+J5aobRv6zauTWB1LnWsSiwiJho9YGTPD1rMwkpmfh4uPL2vQ2veW4Hu93OrPUxjPllBxnZNgJ9PXi/ZyNurxN89SeLmGzH8SQemb6euORMyvt58uWAlqZNYPjNmiO89vMOrDY7N1UL5JOHmqvPlwNSYBExWUJKJs/M2syqA8aMzX1bVWJ093pXbCFJSs9m5Nyt/LbNmISxXY1yfNirMUH+WgdIio/jZ87yyPT17I5NwdvdlUkPNqVD3aIL3FabnbG/7eLzFYcA4/Ls2PsaOly/GjEosIg4AKvNzoTIfXz01z7sdqgX6s/H/Zpdskl6/eFTPDNzM8eTMnBzsfBC59o8dnM1XFzUsVaKn5SMbJ78bhPL9yXiYoEx9zTg4ZsqF/rrpmfl8MysKBbvNEYCPd+pFkNv00ggR6bAIuJAlu1NYPjsKE6lZVHK041xDzTizobGHEM5Vhsf/bWfj/7ah80OVcr6MLFvUxpVLG1u0SI3KNtq45W525izwZiN+fFbqvFylzqFFsLjkjMY/NUGth1LwsPNhfd7NubuxmGF8lpScBRYRBxMbFIGw2ZuYv3h04DRiXZAmyq88P0WNhwx9t3frCJj7qlPKa0DJCWE3W5n0l/7+WDxXgC6NQzlg16NC7zz+K4TyTwyfT0nkjII9PXgs/7NaV5ZI4GKAwUWEQeUbbXx/qI9TF16MM9+P0833ry3geaFkBJr7uajvPjDVrKtdppXLsNn/VsQWECzy/69J56nzo1OqlbeWBOoclmNBCouCm0tIRG5fu6uLozsWpfP+7fInaOiaaXS/PbMzQorUqLd27QiXz8Sgb+XGxuPnOa+j1dyODHths/7zerDPDp9PWlZVlpXK8vcIW0VVkowtbCImCA2KYMtR89we50g3LUOkDiJ/fEpDJy2nqOnz1LGx53PB7S4rks3Vpudt37dxZcrjZFADzSvyNv3aiRQcaQWFhEHFxLgRef6IQor4lRqBPnx05NtaFQxgNPp2fT9bC2/bTuRr3OkZ+XwxDcbc8PKC51r894DjrccgBQ8fYdFRKTIBPl5Mevxm+hYN4isHBtDZ2zis2UHuZbG/rjkDHpNXc2fu+LwcHPho75NNWzZiSiwiIhIkfLxcGPqwy0Y0Loydju89dsuRs3fQY7Vdtnn7DyeTI/JK9l+LJlAXw9mPhZBdw1bdioKLCIiUuRcXSy8dnd9/tetLhaLMZX+E99sJD0r56Jj/94dT89PVnEiKYPq5X2Z92RbDVt2QgosIiJiCovFwuCbq/Hxg83wdHMhcnc8vaeuIT4lI/eYr1cf5tGvjJFAbaqX5achbalU1txFFcUcCiwiImKqrg1DmfHYTQT6erDtWBL3Tl7F7thkxvyyg1Hzd2CzQ68WFZk+qBUBPu5mlysm0bBmERFxCIcT0xg0fT2HEtNwsYDt3KfTC51r82T76upcWwJpWLOIiBQ7Vcr58uOQNrSoXAabHTzcXJj0oEYCiUGLloiIiMMI9PXg28ERfL/xKC0ql6FuqFrNxaDAIiIiDsXL3ZWHb6psdhniYHRJSERERByeAouIiIg4PAUWERERcXgKLCIiIuLwFFhERETE4SmwiIiIiMNTYBERERGHp8AiIiIiDk+BRURERByeAouIiIg4PAUWERERcXgKLCIiIuLwFFhERETE4ZWI1ZrtdjsAycnJJlciIiIi1+r85/b5z/ErKRGBJSUlBYDw8HCTKxEREZH8SklJISAg4IrHWOzXEmscnM1m4/jx4/j5+WGxWAr03MnJyYSHhxMTE4O/v3+BnlvyT98Px6Lvh+PR98Sx6PtxZXa7nZSUFMLCwnBxuXIvlRLRwuLi4kLFihUL9TX8/f31n82B6PvhWPT9cDz6njgWfT8u72otK+ep062IiIg4PAUWERERcXgKLFfh6enJ6NGj8fT0NLsUQd8PR6Pvh+PR98Sx6PtRcEpEp1sREREp2dTCIiIiIg5PgUVEREQcngKLiIiIODwFFhEREXF4CiwiIiLi8BRYrmLy5MlUqVIFLy8vIiIiWLdundklOaWxY8fSsmVL/Pz8CAoKokePHuzZs8fssuScd955B4vFwvDhw80uxWkdO3aMhx56iLJly+Lt7U3Dhg3ZsGGD2WU5JavVyquvvkrVqlXx9vamevXqvPHGG9e0wJ9cngLLFcyePZsRI0YwevRoNm3aROPGjencuTPx8fFml+Z0li5dytChQ1mzZg2LFy8mOzubTp06kZaWZnZpTm/9+vVMnTqVRo0amV2K0zp9+jRt27bF3d2d33//nZ07d/LBBx9QpkwZs0tzSu+++y5Tpkxh0qRJ7Nq1i3fffZdx48bx0UcfmV1asaZ5WK4gIiKCli1bMmnSJMBYZDE8PJxhw4bx8ssvm1ydc0tISCAoKIilS5dyyy23mF2O00pNTaVZs2Z8/PHHvPnmmzRp0oTx48ebXZbTefnll1m5ciXLly83uxQB7rrrLoKDg/niiy9y991///14e3vz7bffmlhZ8aYWlsvIyspi48aNdOzYMXefi4sLHTt2ZPXq1SZWJgBJSUkABAYGmlyJcxs6dCjdunXL83MiRe/nn3+mRYsW9OzZk6CgIJo2bcpnn31mdllOq02bNkRGRrJ3714AtmzZwooVK+jatavJlRVvJWK15sKQmJiI1WolODg4z/7g4GB2795tUlUCRkvX8OHDadu2LQ0aNDC7HKc1a9YsNm3axPr1680uxekdPHiQKVOmMGLECP773/+yfv16nn76aTw8PBgwYIDZ5Tmdl19+meTkZOrUqYOrqytWq5W33nqLfv36mV1asabAIsXO0KFD2b59OytWrDC7FKcVExPDM888w+LFi/Hy8jK7HKdns9lo0aIFb7/9NgBNmzZl+/btfPLJJwosJpgzZw7fffcdM2bMoH79+kRFRTF8+HDCwsL0/bgBCiyXUa5cOVxdXYmLi8uzPy4ujpCQEJOqkqeeeooFCxawbNkyKlasaHY5Tmvjxo3Ex8fTrFmz3H1Wq5Vly5YxadIkMjMzcXV1NbFC5xIaGkq9evXy7Ktbty4//vijSRU5txdeeIGXX36ZPn36ANCwYUOOHDnC2LFjFVhugPqwXIaHhwfNmzcnMjIyd5/NZiMyMpLWrVubWJlzstvtPPXUU8ydO5e//vqLqlWrml2SU+vQoQPbtm0jKioqd2vRogX9+vUjKipKYaWItW3b9qJh/nv37qVy5comVeTc0tPTcXHJ+/Hq6uqKzWYzqaKSQS0sVzBixAgGDBhAixYtaNWqFePHjyctLY1BgwaZXZrTGTp0KDNmzGD+/Pn4+fkRGxsLQEBAAN7e3iZX53z8/Pwu6j/k6+tL2bJl1a/IBM8++yxt2rTh7bffplevXqxbt45PP/2UTz/91OzSnFL37t156623qFSpEvXr12fz5s18+OGHPPLII2aXVrzZ5Yo++ugje6VKleweHh72Vq1a2desWWN2SU4JuOQ2bdo0s0uTc2699Vb7M888Y3YZTuuXX36xN2jQwO7p6WmvU6eO/dNPPzW7JKeVnJxsf+aZZ+yVKlWye3l52atVq2Z/5ZVX7JmZmWaXVqxpHhYRERFxeOrDIiIiIg5PgUVEREQcngKLiIiIODwFFhEREXF4CiwiIiLi8BRYRERExOEpsIiIiIjDU2ARERERh6fAIiIiIg5PgUVEREQcngKLiIiIOLz/Bzvwbf72/Pj+AAAAAElFTkSuQmCC"},"metadata":{}}]},{"cell_type":"code","source":"# 5回目の学習\nfit_epoch(neuron=neuron, batch=batch, epochs=50, initial_epoch=40, model_filename=model_filename)","metadata":{"execution":{"iopub.status.busy":"2024-07-21T11:32:57.140322Z","iopub.execute_input":"2024-07-21T11:32:57.140656Z"},"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"Epoch 41/50\n\u001b[1m 5/1562\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m55s\u001b[0m 35ms/step - accuracy: 0.8183 - loss: 0.3167","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721561583.450825 107 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m 230/1562\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:13\u001b[0m 55ms/step - accuracy: 0.8338 - loss: 0.3671","output_type":"stream"},{"name":"stderr","text":"W0000 00:00:1721561595.988383 106 graph_launch.cc:671] Fallback to op-by-op mode because memset node breaks graph update\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m1561/1562\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - accuracy: 0.8397 - loss: 0.3587","output_type":"stream"}]},{"cell_type":"markdown","source":"あの、初めエポック数100で一気にしようとしたら
\n`your notebook tried to allocate more memory than is available. it has restarted.`
\n↑このエラーが出て詰んだのでエポック数10×10にシフトチェンジしたんだけど、それでも出るのはなんで。\n\n一気にやりたかったらコード書き直せ😑\n\n学習の回数は自由に決めてね。","metadata":{}},{"cell_type":"markdown","source":"**変更点**\n- 画像サイズを126→64に\n- バッチサイズを32→16に","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}