may be last version

By completing the thesis, we have corrected the deficiencies and fixed some bugs.

some-test/keras_use.py

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+import numpy as np
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras.optimizers import SGD
+
+# 生成虚拟数据
+x_train = np.random.random((100, 100, 100, 3))
+y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
+x_test = np.random.random((20, 100, 100, 3))
+y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)
+
+model = Sequential()
+# 输入: 3 通道 100x100 像素图像 -> (100, 100, 3) 张量。
+# 使用 32 个大小为 3x3 的卷积滤波器。
+model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
+model.add(Conv2D(32, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+
+model.add(Flatten())
+model.add(Dense(256, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(10, activation='softmax'))
+
+sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(loss='categorical_crossentropy', optimizer=sgd)
+
+model.fit(x_train, y_train, batch_size=32, epochs=10)
+score = model.evaluate(x_test, y_test, batch_size=32)