DL之CNN：自定義SimpleConvNet【3層,im2col優(yōu)化】利用mnist數(shù)據(jù)集實(shí)現(xiàn)手寫數(shù)字識(shí)別多分類訓(xùn)練來評(píng)估模型

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目錄

輸出結(jié)果

設(shè)計(jì)思路

核心代碼

更多輸出

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輸出結(jié)果

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設(shè)計(jì)思路

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核心代碼

class Convolution:def __init__(self, W, b, stride=1, pad=0): ……def forward(self, x): FN, C, FH, FW = self.W.shape N, C, H, W = x.shapeout_h = 1 + int((H + 2*self.pad - FH) / self.stride)out_w = 1 + int((W + 2*self.pad - FW) / self.stride)col = im2col(x, FH, FW, self.stride, self.pad) col_W = self.W.reshape(FN, -1).T out = np.dot(col, col_W) + self.b out = out.reshape(N, out_h, out_w, -1).transpose(0, 3, 1, 2)self.x = xself.col = colself.col_W = col_Wreturn out def backward(self, dout): FN, C, FH, FW = self.W.shape dout = dout.transpose(0,2,3,1).reshape(-1, FN) self.db = np.sum(dout, axis=0) self.dW = np.dot(self.col.T, dout)self.dW = self.dW.transpose(1, 0).reshape(FN, C, FH, FW)dcol = np.dot(dout, self.col_W.T) return dx class Pooling:def __init__(self, pool_h, pool_w, stride=1, pad=0): self.pool_h = pool_hself.pool_w = pool_wself.stride = strideself.pad = padself.x = Noneself.arg_max = None……class SimpleConvNet: #def __init__(self, input_dim=(1, 28, 28), conv_param={'filter_num':30, 'filter_size':5, 'pad':0, 'stride':1},hidden_size=100, output_size=10, weight_init_std=0.01):filter_num = conv_param['filter_num']filter_size = conv_param['filter_size']filter_pad = conv_param['pad']filter_stride = conv_param['stride']input_size = input_dim[1]conv_output_size = (input_size - filter_size + 2*filter_pad) / filter_stride + 1pool_output_size = int(filter_num * (conv_output_size/2) * (conv_output_size/2))self.params = {}self.params['W1'] = weight_init_std * \np.random.randn(filter_num, input_dim[0], filter_size, filter_size)self.params['b1'] = np.zeros(filter_num)self.params['W2'] = weight_init_std * \np.random.randn(pool_output_size, hidden_size)self.params['b2'] = np.zeros(hidden_size)self.params['W3'] = weight_init_std * \np.random.randn(hidden_size, output_size)self.params['b3'] = np.zeros(output_size)self.layers = OrderedDict()self.layers['Conv1'] = Convolution(self.params['W1'], self.params['b1'],conv_param['stride'], conv_param['pad']) self.layers['Relu1'] = Relu() self.layers['Pool1'] = Pooling(pool_h=2, pool_w=2, stride=2) self.layers['Affine1'] = Affine(self.params['W2'], self.params['b2']) self.layers['Relu2'] = Relu()self.layers['Affine2'] = Affine(self.params['W3'], self.params['b3'])self.last_layer = SoftmaxWithLoss() ……def save_params(self, file_name="params.pkl"): params = {} for key, val in self.params.items(): params[key] = valwith open(file_name, 'wb') as f: pickle.dump(params, f)def load_params(self, file_name="params.pkl"): with open(file_name, 'rb') as f: params = pickle.load(f)for key, val in params.items(): self.params[key] = valfor i, key in enumerate(['Conv1', 'Affine1', 'Affine2']): self.layers[key].W = self.params['W' + str(i+1)]self.layers[key].b = self.params['b' + str(i+1)]

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更多輸出

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總結(jié)

以上是生活随笔為你收集整理的DL之CNN：自定义SimpleConvNet【3层,im2col优化】利用mnist数据集实现手写数字识别多分类训练来评估模型的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。