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お手数をおかけしますが、よろしくお願い致します。
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以下コードになります。
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```Python
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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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import matplotlib.pyplot as plt
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#画像が入った入力データ
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X = X.astype('float32')
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X = X / 255.0
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#正解ラベルの形式を変換
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Y = np_utils.to_categorical(Y, 4)
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#学習用データとテストデータ
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2,train_size=0.8)
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```
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モデルの構築
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```Python
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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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#訓練
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history = model.fit(X_train, Y_train, epochs=100)
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```
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これだとメモリ不足に陥るため、ミニバッチ学習で小分けに学習していきたいです。
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（ちなみに画像サイズは700×500です。）
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