質問編集履歴
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質問の具体化
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可能性としては
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① rgbaのrの要素が使われる。
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② rgbaの画像がグレースケールに変換されて学習される。
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の二つを考えています。どちらだと思いますか？
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の二つを考えています。どちらだと思いますか？
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・参考にしたサイト
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[VGG16を転移学習させて「まどか☆マギカ」のキャラを見分ける](https://qiita.com/God_KonaBanana/items/2cf829172087d2423f58)
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・全文
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```ここに言語を入力
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#model&train
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from keras.models import Model
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from keras.layers import Dense, GlobalAveragePooling2D,Input
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from keras.applications.vgg16 import VGG16
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from keras.preprocessing.image import ImageDataGenerator
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from keras.optimizers import SGD
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from keras.callbacks import CSVLogger
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import matplotlib.pyplot as plt
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import os
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classes = ['hituji','buta','usi']
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label=['hituji','buta','usi']
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img_height=256
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img_width=256
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batch_size=16
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num_epochs=50
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n_categories=3
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seed=1
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file_name = 'doubutu_bunrui'
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print(file_name)
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train_dir==os.path.join('D:','train')
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#model作成(グレースケール)
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base_model=VGG16(weights=None,include_top=False,
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input_shape=(img_width,img_height,1),
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input_tensor=Input(shape=(img_width,img_height,1)),
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)
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#add new layers instead of FC networks
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x=base_model.output
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x=GlobalAveragePooling2D()(x)
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x=Dense(1024,activation='relu')(x)
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prediction=Dense(n_categories,activation='softmax')(x)
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model=Model(inputs=base_model.input,outputs=prediction)
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#fix weights
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for layer in base_model.layers[:0]:
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layer.trainable=False
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model.compile(optimizer=SGD(lr=0.0001,momentum=0.9),
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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#save model
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json_string=model.to_json()
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open(file_name+'.json','w').write(json_string)
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#学習(train)
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train_datagen=ImageDataGenerator()
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train_generator=train_datagen.flow_from_directory(
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train_dir,
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target_size=(img_width,img_height),
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batch_size=batch_size,
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classes=classes,
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class_mode='categorical',
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color_mode='grayscale'
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shuffle=True,
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seed=seed
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)
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#history
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history=model.fit_generator(train_generator,
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epochs=num_epochs,
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verbose=0,
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callbacks=[CSVLogger(file_name+'.csv')])
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#save weights
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model.save(file_name+'.h5')
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```
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