文件名称:BPNN
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前向型神经网络(BPNN)
1.首先使用随机函数对每一层间的连接权值矩阵和偏置向量进行随机初始化.
2.依次使用一个训练样本对网络进行训练，并按照上面的公式计算每个样本的Δti,t 1,...,T− 1
3.训练p个样本后(一次batch)，按照更新方程对W与b进行更新.
4.重复步骤2～3，直到误差小于设定的阈值或者达到设定的batch次数.-Forward neural network (BPNN) 1. First, using a random function of connection weight matrices and bias vectors between each layer of random initialization. 2 in order to use a training sample to train the network, and calculated according to the above formula Δti each sample, t 1, ..., T-1 3. training p samples after (a batch), according to the update equation of W and b are updated. 4. repeat steps 2-3 until the error is less than set threshold or reach the batch number of the set.
1.首先使用随机函数对每一层间的连接权值矩阵和偏置向量进行随机初始化.
2.依次使用一个训练样本对网络进行训练，并按照上面的公式计算每个样本的Δti,t 1,...,T− 1
3.训练p个样本后(一次batch)，按照更新方程对W与b进行更新.
4.重复步骤2～3，直到误差小于设定的阈值或者达到设定的batch次数.-Forward neural network (BPNN) 1. First, using a random function of connection weight matrices and bias vectors between each layer of random initialization. 2 in order to use a training sample to train the network, and calculated according to the above formula Δti each sample, t 1, ..., T-1 3. training p samples after (a batch), according to the update equation of W and b are updated. 4. repeat steps 2-3 until the error is less than set threshold or reach the batch number of the set.
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BPNN.py
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