回答編集履歴
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以下のようにしてみてはどうでしょうか。
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```python
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import os
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import numpy as np
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from keras.preprocessing.image import ImageDataGenerator
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base_dir = (r'C:\Users\HN4-00012\Documents\kosen fike\bunnkasai\loads15_data_small')
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train_dir = os.path.join(base_dir,'train')
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validation_dir = os.path.join(base_dir,'validation')
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test_dir = os.path.join(base_dir,'test')
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datagen = ImageDataGenerator(rescale=1./255)
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def extract_features(dirpath):
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features = []
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labels = []
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generator = datagen.flow_from_directory(
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dirpath, target_size=(150, 150), batch_size=32, class_mode='categorical')
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num_steps = len(generator)
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for step, (img_batch, label_batch) in enumerate(generator, 1):
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print('step: {}, img_batch: {}, label_batch: {}'.format(
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step, img_batch.shape, label_batch.shape))
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feat_batch = conv_base.predict(img_batch)
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features.extend(feat_batch)
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labels.extend(label_batch)
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if step >= num_steps:
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break
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features = np.array(features)
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features = features.reshape(len(features), -1)
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labels = np.array(labels).astype(int)
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return features, labels
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train_features, train_labels = extract_features(train_dir)
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validation_features, validation_labels = extract_features(validation_dir)
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test_features, test_labels = extract_features(test_dir)
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```
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* datagen.flow_from_directory() で3クラス以上の場合は class_mode='categorical' を指定します。
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train_features = np.reshape(train_features, (2000, -1))
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* ステップ数は num_steps = len(generator) で取得できるので、if step >= num_steps で break すればよいです。
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test_features = np.reshape(test_features, (1000, -1))
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```
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