質問編集履歴
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モデルについてのコードを追加
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```Python
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#coding:utf-8
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import keras
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from keras.utils import np_utils
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from keras.models import Sequential
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from keras.layers.convolutional import Conv2D, MaxPooling2D
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from keras.layers.core import Dense, Dropout, Activation, Flatten
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import numpy as np
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from sklearn.model_selection import train_test_split
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from PIL import Image
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import glob
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folder = ["Not_Skill","with_Skill"]
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image_size = 50
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X = []
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Y = []
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for index, name in enumerate(folder):
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dir = "./" + name
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files = glob.glob(dir + "/*.jpg")
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for i, file in enumerate(files):
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image = Image.open(file)
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image = image.convert("RGB")
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image = image.resize((image_size, image_size))
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data = np.asarray(image)
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X.append(data)
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Y.append(index)
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X = np.array(X)
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Y = np.array(Y)
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X = X.astype('float32')
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X = X / 255.0
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Y = np_utils.to_categorical(Y, 4)
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X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.20)
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model = Sequential()
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model.add(Conv2D(32, (3, 3), padding='same',input_shape=X_train.shape[1:]))
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model.add(Activation('relu'))
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model.add(Conv2D(32, (3, 3)))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Conv2D(64, (3, 3), padding='same'))
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model.add(Activation('relu'))
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model.add(Conv2D(64, (3, 3)))
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model.add(Activation('relu'))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Dropout(0.25))
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model.add(Flatten())
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model.add(Dense(512))
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model.add(Activation('relu'))
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model.add(Dropout(0.5))
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model.add(Dense(4))
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model.add(Activation('softmax'))
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# コンパイル
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model.compile(loss='categorical_crossentropy',optimizer='SGD',metrics=['accuracy'])
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from keras.callbacks import TensorBoard
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tbcb = TensorBoard(log_dir='./graph',histogram_freq=0,write_graph=True)
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history = model.fit(X_train, y_train,batch_size=32,epochs=1000, verbose=1,validation_data=(X_test,y_test),callbacks=[tbcb])
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from keras.utils import plot_model
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model_json = model.to_json()
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with open('model.json', mode='w') as f:
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f.write(model_json)
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model.save_weights('weights.h5')
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import pickle
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with open("history.pickle", mode='wb') as f:
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pickle.dump(history.history, f)
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print(model.evaluate(X_test, y_test))
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```
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### 試したこと
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