def __init__(self, input_dim=(1, 28, 28), conv_param_1 = {'filter_num':16, 'filter_size':3, 'pad':1, 'stride':1}, conv_param_2 = {'filter_num':16, 'filter_size':3, 'pad':1, 'stride':1}, conv_param_3 = {'filter_num':32, 'filter_size':3, 'pad':1, 'stride':1}, conv_param_4 = {'filter_num':32, 'filter_size':3, 'pad':2, 'stride':1}, conv_param_5 = {'filter_num':64, 'filter_size':3, 'pad':1, 'stride':1}, conv_param_6 = {'filter_num':64, 'filter_size':3, 'pad':1, 'stride':1}, hidden_size=50, output_size=2):

这是一个初始化一个卷积神经网络的函数，它有六个卷积层和一个全连接层。其中，input_dim指定了输入数据的维度；conv_param_*是字典类型的参数，包括卷积核数量，大小，填充和步幅；hidden_size指定了全连接层的隐层神经元数量；output_size指定了输出层的神经元数量，通常用于分类任务中。