From 4cf977a8cdcad479e933d1aca88fb12b82d4459b Mon Sep 17 00:00:00 2001 From: p4w2aybsf <2363061197@qq.com> Date: Thu, 29 Apr 2021 16:57:07 +0800 Subject: [PATCH] =?UTF-8?q?=E6=97=A0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- deep_convnet.py | 136 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 136 insertions(+) create mode 100644 deep_convnet.py diff --git a/deep_convnet.py b/deep_convnet.py new file mode 100644 index 0000000..d974c7b --- /dev/null +++ b/deep_convnet.py @@ -0,0 +1,136 @@ +# coding: utf-8 +import sys, os +sys.path.append(os.pardir) # 为了导入父目录的文件而进行的设定 +import pickle +import numpy as np +from collections import OrderedDict +from common.layers import * + + +class DeepConvNet: + """识别率为99%以上的高精度的ConvNet + + 网络结构如下所示 + conv - relu - conv- relu - pool - + conv - relu - conv- relu - pool - + conv - relu - conv- relu - pool - + affine - relu - dropout - affine - dropout - softmax + """ + 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=========== + # 各层的神经元平均与前一层的几个神经元有连接（TODO:自动计算） + pre_node_nums = np.array([1*3*3, 16*3*3, 16*3*3, 32*3*3, 32*3*3, 64*3*3, 64*4*4, hidden_size]) + wight_init_scales = np.sqrt(2.0 / pre_node_nums) # 使用ReLU的情况下推荐的初始值 + + self.params = {} + pre_channel_num = input_dim[0] + for idx, conv_param in enumerate([conv_param_1, conv_param_2, conv_param_3, conv_param_4, conv_param_5, conv_param_6]): + self.params['W' + str(idx+1)] = wight_init_scales[idx] * np.random.randn(conv_param['filter_num'], pre_channel_num, conv_param['filter_size'], conv_param['filter_size']) + self.params['b' + str(idx+1)] = np.zeros(conv_param['filter_num']) + pre_channel_num = conv_param['filter_num'] + self.params['W7'] = wight_init_scales[6] * np.random.randn(64*4*4, hidden_size) + self.params['b7'] = np.zeros(hidden_size) + self.params['W8'] = wight_init_scales[7] * np.random.randn(hidden_size, output_size) + self.params['b8'] = np.zeros(output_size) + + # 生成层=========== + self.layers = [] + self.layers.append(Convolution(self.params['W1'], self.params['b1'], + conv_param_1['stride'], conv_param_1['pad'])) + self.layers.append(Relu()) + self.layers.append(Convolution(self.params['W2'], self.params['b2'], + conv_param_2['stride'], conv_param_2['pad'])) + self.layers.append(Relu()) + self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) + self.layers.append(Convolution(self.params['W3'], self.params['b3'], + conv_param_3['stride'], conv_param_3['pad'])) + self.layers.append(Relu()) + self.layers.append(Convolution(self.params['W4'], self.params['b4'], + conv_param_4['stride'], conv_param_4['pad'])) + self.layers.append(Relu()) + self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) + self.layers.append(Convolution(self.params['W5'], self.params['b5'], + conv_param_5['stride'], conv_param_5['pad'])) + self.layers.append(Relu()) + self.layers.append(Convolution(self.params['W6'], self.params['b6'], + conv_param_6['stride'], conv_param_6['pad'])) + self.layers.append(Relu()) + self.layers.append(Pooling(pool_h=2, pool_w=2, stride=2)) + self.layers.append(Affine(self.params['W7'], self.params['b7'])) + self.layers.append(Relu()) + self.layers.append(Dropout(0.5)) + self.layers.append(Affine(self.params['W8'], self.params['b8'])) + self.layers.append(Dropout(0.5)) + + self.last_layer = SoftmaxWithLoss() + + def predict(self, x, train_flg=False): + for layer in self.layers: + if isinstance(layer, Dropout): + x = layer.forward(x, train_flg) + else: + x = layer.forward(x) + return x + + def loss(self, x, t): + y = self.predict(x, train_flg=True) + return self.last_layer.forward(y, t) + + def accuracy(self, x, t, batch_size=100): + if t.ndim != 1 : t = np.argmax(t, axis=1) + + acc = 0.0 + + for i in range(int(x.shape[0] / batch_size)): + tx = x[i*batch_size:(i+1)*batch_size] + tt = t[i*batch_size:(i+1)*batch_size] + y = self.predict(tx, train_flg=False) + y = np.argmax(y, axis=1) + acc += np.sum(y == tt) + + return acc / x.shape[0] + + def gradient(self, x, t): + # forward + self.loss(x, t) + + # backward + dout = 1 + dout = self.last_layer.backward(dout) + + tmp_layers = self.layers.copy() + tmp_layers.reverse() + for layer in tmp_layers: + dout = layer.backward(dout) + + # 设定 + grads = {} + for i, layer_idx in enumerate((0, 2, 5, 7, 10, 12, 15, 18)): + grads['W' + str(i+1)] = self.layers[layer_idx].dW + grads['b' + str(i+1)] = self.layers[layer_idx].db + + return grads + + def save_params(self, file_name="params.pkl"): + params = {} + for key, val in self.params.items(): + params[key] = val + with 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] = val + + 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)]