## 経緯

以下のプログラムを実行すると、stackoverflowエラーが出てしまいます。  
どのようにすればいいのかがわからないのでわかる方、ご伝授お願いします。

## ソースコード

main.py

```

    # パッケージのインポート
    import os
    
    from keras.layers import BatchNormalization, Dense, Dropout
    from keras.models import Sequential
    from keras.optimizers import SGD
    import matplotlib.pyplot as plt
    from tqdm import tqdm
    
    import load_data
    
    
    train_paths = []
    for root, dirs, files in tqdm(os.walk("./font")):
        train_paths += list(map(lambda n:root+"/"+n,files))
    
    val_count = int(len(train_paths) * 0.2)
    
    train_gen = load_data.Generator(
                                 train_paths[val_count:],
                                 batch_size=64)
    val_gen = load_data.Generator(
                                 train_paths[:val_count],
                                 batch_size=64)
    
    # モデルの作成
    model = Sequential()
    model.add(Dense(512, activation='sigmoid', input_shape=(32**2,)))  # 入力層
    model.add(BatchNormalization())
    model.add(Dense(256, activation='sigmoid'))  # 隠れ層
    model.add(Dropout(rate=0.5))  # ドロップアウト
    model.add(Dense(94, activation='softmax'))  # 出力層
    # コンパイル
    model.compile(loss='categorical_crossentropy', optimizer=SGD(lr=0.1), metrics=['acc'])
    # 学習
    history = model.fit_generator(
               train_gen,
               steps_per_epoch=train_gen.num_batches_per_epoch,
               validation_data=val_gen,
               validation_steps=val_gen.num_batches_per_epoch,
               epochs=100,
               shuffle=True)
    model.save("model.h5")
    # model = load_model("model.h5")
    
    # グラフの表示
    plt.plot(history.history['acc'], label='acc')
    plt.plot(history.history['val_acc'], label='val_acc')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(loc='best')
    plt.show()
    
```

load_data.py

```

    import numpy as np
    import importlib
    from PIL import Image, ImageDraw, ImageFont
    from keras.utils import Sequence
    
    
    class Generator(Sequence):
        """Custom generator"""
    
        def __init__(self, data_paths, batch_size=1, width=32, height=32, font_size=32, num_of_class=94):
            """construction
    
            :param data_paths: List of image file
            :param batch_size: Batch size
            :param width: Image width
            :param height: Image height
            :param num_of_class: Num of classes
            """
    
            self.data_paths = data_paths
            self.length = len(data_paths) * 94 * int(180/5)
            self.batch_size = batch_size
            self.width = width
            self.height = height
            self.font_size = font_size
            self.num_of_class = num_of_class
            self.data_pos = [0, 0, 0]
            self.font_data = ImageFont.truetype(self.data_paths[self.data_pos[0]], self.font_size)
            self.num_batches_per_epoch = int((self.length - 1) / batch_size) + 1
    
        def _load_data(self):
            text = chr(self.data_pos[2] + 33)
            font_path = self.data_paths[self.data_pos[0]]
            font_color = "white"
            rot = self.data_pos[1]*5
    
            # get fontsize
    
            tmp = Image.new('RGBA', (1, 1), (0, 0, 0, 0))  # dummy for get text_size
    
            tmp_d = ImageDraw.Draw(tmp)
            text_size = tmp_d.textsize(text, self.font_data)
            i = self.font_size
            while text_size[0] > self.font_size - 5 or text_size[1] > self.font_size - 5:
                i -= 1
                font_data = ImageFont.truetype(font_path, i)
                text_size = tmp_d.textsize(text, font_data)
            # draw text
    
            img = Image.new('RGBA', [self.font_size] * 2, (0, 0, 0, 0))  # background: transparent
    
            img_d = ImageDraw.Draw(img)
            img_d.text((0, 0), text, fill=font_color, font=self.font_data)
            img = img.rotate(rot)
    
            self.data_pos[2] += 1
            if self.data_pos[2] > 93:
                self.data_pos[1] += 1
                self.data_pos[2] = 0
                if self.data_pos[1] > 180/5:
                    importlib.reload(np)
                    importlib.reload(Image)
                    importlib.reload(ImageDraw)
                    importlib.reload(ImageFont)
                    importlib.reload(importlib)
                    self.data_pos[0] += 1
                    self.font_data = ImageFont.truetype(self.data_paths[self.data_pos[0]], self.font_size)
                    self.data_pos[1] = 0
    
            img = np.array(img)
            img = 0.299 * img[:, :, 2] + 0.587 * img[:, :, 1] + 0.114 * img[:, :, 0]
            return img, self.data_pos[2]
    
        def __getitem__(self, idx) -> np.array:
            """Get batch data
    
            :param idx: Index of batch
    
            :return imgs: numpy array of images
            :return labels: numpy array of label
            """
    
            start_pos = self.batch_size * idx
            end_pos = start_pos + self.batch_size
            if end_pos > self.length:
                end_pos = self.length
            imgs = np.empty((end_pos-start_pos+1, self.height, self.width), dtype=np.float32)
            labels = np.zeros((end_pos-start_pos+1, self.num_of_class), dtype=np.int16)
    
            for i in range(self.batch_size):
                img, label = self._load_data()
                imgs[i, :] = img
                labels[i][label] = 1
            np.save("test.npy", labels)
            # データセットの画像の前処理
            imgs = imgs.reshape((imgs.shape[0], imgs.shape[1] ** 2))
    
            return imgs, labels
    
        def __len__(self):
            """Batch length"""
    
            return self.num_batches_per_epoch
    
        def on_epoch_end(self):
            """Task when end of epoch"""
            pass
    
```

よろしくおねがいします。
CC BY-SA 4.0
2020-07-11T00:09:56.953
68484
2020-07-11T01:16:50.830
35940
question
python python3 numpy keras pillow
kerasのpythonでstackoverflowエラー
自己解決しました。フォントが肥大すぎてtry exceptで挟まなくてはエラーが起きてしまっていました。ご迷惑おかけしました。
CC BY-SA 4.0
2020-07-11T01:16:50.830
68486
2020-07-11T01:16:50.830
35940
68484
answer