小编Sud*_*dey的帖子

我已经使用Google Colab在Keras中训练了序列模型来处理Google Doodle数据集。我在这里做一些简单的图像分类。

以下函数定义了我的模型的体系结构：

def create_model(input_shape):
   model = keras.Sequential()  
   model.add(layers.Conv2D(16, (3, 3), padding = 'same', input_shape = input_shape, activation = 'relu'))
   model.add(layers.BatchNormalization(axis = 3)) 
   model.add(layers.MaxPooling2D(pool_size = (2, 2)))

   model.add(layers.Conv2D(32, (3, 3), padding = 'same', activation = 'relu'))
   model.add(layers.BatchNormalization(axis = 3))
   model.add(layers.MaxPooling2D(pool_size = (2, 2)))

   model.add(layers.Conv2D(64, (3, 3), padding = 'same', activation = 'relu'))
   model.add(layers.BatchNormalization(axis = 3))
   model.add(layers.MaxPooling2D(pool_size = (2,2)))

   model.add(layers.Flatten())
   model.add(layers.Dense(128, activation = 'relu'))
   model.add(layers.Dense(28, activation = 'softmax')) 

   return model

下面的代码创建，编译和适合它：

doodle_model = create_model((image_size, image_size, 1)) #image_size = 28
doodle_model.compile …