DL之CNN：利用自定义DeepConvNet【7+1】算法对mnist数据集训练实现手写数字识别并预测(超过99%)

DL之CNN：利用自定义DeepConvNet【7+1】算法对mnist数据集训练实现手写数字识别并预测(超过99%)

---

输出结果

准确度都在99%以上

1、出错记录

col = np.zeros((N, C, filter_h, filter_w, out_h, out_w))
MemoryError

设计思路

1、网络结构

2、总体框架

核心代码

class DeepConvNet:

    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=10):
        # 初始化权重===========
            ……

    def predict(self, x, train_flg=False):
            ……

    def loss(self, x, t):
            ……

        return acc / x.shape[0]

    def gradient(self, x, t):
        # forward
        self.loss(x, t)

        # backward
            ……

        return grads

    def save_params(self, file_name="params.pkl"):
            ……

    def load_params(self, file_name="params.pkl"):
            ……

        for i, layer_idx in enumerate((0, 2, 5, 7, 10, 12, 15, 18)):
            self.layers[layer_idx].W = self.params['W' + str(i+1)]
            self.layers[layer_idx].b = self.params['b' + str(i+1)]