DL之NN:NN算法(本地数据集50000张训练集图片)进阶优化之三种参数改进,进一步提高手写数字图片识别的准确率 | 您所在的位置:网站首页 › top1准确率三类 › DL之NN:NN算法(本地数据集50000张训练集图片)进阶优化之三种参数改进,进一步提高手写数字图片识别的准确率 |
思路设计 首先,改变之一: 先在初始化权重的部分,采取一种更为好的随机初始化方法,我们依旧保持正态分布的均值不变,只对标准差进行改动, 初始化权重改变前, def large_weight_initializer(self): self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]] self.weights = [np.random.randn(y, x) for x, y in zip(self.sizes[:-1], self.sizes[1:])] 初始化权重改变后, def default_weight_initializer(self): self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]] self.weights = [np.random.randn(y, x)/np.sqrt(x) for x, y in zip(self.sizes[:-1], self.sizes[1:])] 改变之二: 为了减少Overfitting,降低数据局部噪音影响,将原先的目标函数由 quadratic cost 改为 cross-enrtopy cost class CrossEntropyCost(object): def fn(a, y): return np.sum(np.nan_to_num(-y*np.log(a)-(1-y)*np.log(1-a))) def delta(z, a, y): return (a-y) 改变之三: 将S函数改为Softmax函数 class SoftmaxLayer(object): def __init__(self, n_in, n_out, p_dropout=0.0): self.n_in = n_in self.n_out = n_out self.p_dropout = p_dropout self.w = theano.shared( np.zeros((n_in, n_out), dtype=theano.config.floatX), name='w', borrow=True) self.b = theano.shared( np.zeros((n_out,), dtype=theano.config.floatX), name='b', borrow=True) self.params = [self.w, self.b] def set_inpt(self, inpt, inpt_dropout, mini_batch_size): self.inpt = inpt.reshape((mini_batch_size, self.n_in)) self.output = softmax((1-self.p_dropout)*T.dot(self.inpt, self.w) + self.b) self.y_out = T.argmax(self.output, axis=1) self.inpt_dropout = dropout_layer( inpt_dropout.reshape((mini_batch_size, self.n_in)), self.p_dropout) self.output_dropout = softmax(T.dot(self.inpt_dropout, self.w) + self.b) def cost(self, net): "Return the log-likelihood cost." return -T.mean(T.log(self.output_dropout)[T.arange(net.y.shape[0]), net.y]) def accuracy(self, y): "Return the accuracy for the mini-batch." return T.mean(T.eq(y, self.y_out)) |
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