From 369819ce471828db675a4e1d9a25d9b3718099ef Mon Sep 17 00:00:00 2001 From: Q9ihs38xl <1979755751@qq.com> Date: Wed, 28 Apr 2021 14:19:00 +0800 Subject: [PATCH] Delete 'deep_convnet.py' --- deep_convnet.py | 136 ------------------------------------------------ 1 file changed, 136 deletions(-) delete mode 100644 deep_convnet.py diff --git a/deep_convnet.py b/deep_convnet.py deleted file mode 100644 index d974c7b..0000000 --- a/deep_convnet.py +++ /dev/null @@ -1,136 +0,0 @@ -# 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)]