質問編集履歴
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model.add(Dense(512, activation='relu'))
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model.add(Dropout(0.5))
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model.add(Dense(10, activation='softmax'))
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```
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---
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データ取得部分
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```python
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orig_image, orig_label, class_list = load_imgs(root_dir='kill_me_images/kill_me_baby_datasets/kill_me_baby_datasets/')
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#['agiri', 'botsu', 'others', 'sonya', 'yasuna', 'yasuna&agiri', 'yasuna&sonya']
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```
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---
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関数部分(cv2.COLOR_BGR2RGBでBRGをRGBに調整)
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```python
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def load_imgs(root_dir):
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print(class_list)
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num_class = len(class_list)
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img_paths = []
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labels = []
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images = []
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for cl_name in class_list:
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img_names = os.listdir(os.path.join(root_dir, cl_name))
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for img_name in img_names:
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img_paths.append(os.path.abspath(os.path.join(root_dir, cl_name, img_name)))
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hot_cl_name = get_class_one_hot(cl_name, class_list)
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labels.append(hot_cl_name)
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for img_path in img_paths:
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img = cv2.imread(img_path)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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images.append(img)
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images = np.array(images)
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return np.array(images), np.array(labels), class_list
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```
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---
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onehot
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```python
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def get_class_one_hot(class_str, class_list):
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label = class_list.index(class_str)
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label_hot = tf.one_hot(label, len(class_list))
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return label_hot
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```
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実行部分
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```python
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batch_size = 32
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model.compile(loss='categorical_crossentropy', optimizer=tf.keras.optimizers.Adam(), metrics=['accuracy'])
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tb_cb = tf.keras.callbacks.TensorBoard(log_dir="log_dir")
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ckps = [tb_cb]
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# 学習用データを用意する
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train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255, horizontal_flip=True)
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train_generator = train_datagen.flow_from_directory('kill_me_images/kill_me_baby_datasets/', classes=class_list, target_size=(128, 128), batch_size=batch_size, class_mode='categorical')
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# 学習開始！
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model.fit_generator(train_generator, steps_per_epoch=train_generator.samples//batch_size, epochs=100, callbacks=ckps)
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model.save("models/killme_vgg16.h5")
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sess = tf.keras.backend.get_session()
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saver = tf.train.Saver()
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saver.save(sess, "models/killme_vgg16.ckpt")
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```
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