前面的圖像辨識的練習，都是在二分的歸類，但真實的世界是多元的，所以最後的一片拼圖，就是多項目的分類。這邊的範例是剪刀石頭布三種手型的分類，而三種、跟十種、或多種是同理相通。
而如同之前的範例，要修改的部分只有3個地方：在model的最後一層dense的activation='softmax'，在compile的loss = 'categorical_crossentropy'，以及 ImageDataGenerator的 flow_from_directory中class_mode='categorical'，這些都是很明顯因為現在是多個分類。完整範例code如下：

import tensorflow as tf
model = tf.keras.models.Sequential([
    # Note the input shape is the desired size of the image 150x150 with 3 bytes color
    # This is the first convolution
    tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(150, 150, 3)),
    tf.keras.layers.MaxPooling2D(2, 2),
    # The second convolution
    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    # The third convolution
    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    # The fourth convolution
    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    # Flatten the results to feed into a DNN
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dropout(0.5),
    # 512 neuron hidden layer
    tf.keras.layers.Dense(512, activation='relu'),
    tf.keras.layers.Dense(3, activation='softmax')
])

# Set the training parameters
model.compile(loss = 'categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

from tensorflow.keras.preprocessing.image import ImageDataGenerator
datagen_aug = ImageDataGenerator(
      rescale = 1./255,
	  rotation_range=40,
      width_shift_range=0.2,
      height_shift_range=0.2,
      shear_range=0.2,
      zoom_range=0.2,
      horizontal_flip=True,
      fill_mode='nearest')

datagen = ImageDataGenerator(rescale = 1./255)

train_generator_aug = datagen_aug.flow_from_directory(
	TRAINING_DIR,
	target_size=(150,150),
	class_mode='categorical',
    batch_size=126)
validation_generator = datagen.flow_from_directory(
	VALIDATION_DIR,
	target_size=(150,150),
	class_mode='categorical',
    batch_size=126)

# Train the model
history = model.fit(train_generator_aug, 
                    epochs=25, 
                    steps_per_epoch=20, 
                    validation_data = validation_generator, 
                    verbose = 1, 
                    validation_steps=3)

訓練的accuracy和loss如下圖：

在25次epoch後loss已經低於0.15而accuracy也高於0.94，這是由於訓練和測試的圖庫都是CGI電腦生成的，而且背景非常乾淨：