affine_forward(x, w, b)[0]
时间: 2024-01-18 19:04:54 浏览: 28
affine_forward(x, w, b) 函数的第一个返回值是 out,即前向传播后的输出值。因此,affine_forward(x, w, b)[0] 表示前向传播后的输出值。
具体来说,affine_forward(x, w, b) 函数实现了全连接层的前向传播,其中 x 是输入数据,w 是权重参数,b 是偏置参数。全连接层的前向传播可以表示为以下公式:
out = x * w + b
其中,* 表示矩阵乘法运算。因此,affine_forward(x, w, b)[0] 就是这个公式的输出结果。
相关问题
全连接层:矩阵变换,获取对应目标相同的行与列 输入x: 2321616 输入x_row: 28192 超参w:8192100 输出:矩阵乘法 28192 ->8192100 =>2100def affine_forward(x, w, b): out = None N = x.shape[0] x_row = x.reshape(N, -1) out = np.dot(x_row, w) + b.reshape(1, -1) cache = (x, w, b) return out, cache def affine_relu_forward(x, w, b): x = x.reshape(x.shape[0], -1) w = np.random.randn(784, 100) b = np.zeros((1, 100)) out, cache = layers.affine_forward(x, w, b) a, fc_cache = layers.affine_forward(x, w, b) out, relu_cache = layers.relu_forward(a) cache = (fc_cache, relu_cache) return out, cacheValueError: shapes (40,6272) and (784,100) not aligned: 6272 (dim 1) != 784 (dim 0)报错
这段代码中的错误是因为在`affine_relu_forward`函数中,使用了一个随机生成的`w`和全零的`b`,而这个`w`的维度是`(784, 100)`,而输入`x`的维度为`(40, 6272)`,所以在调用`layers.affine_forward(x, w, b)`函数时,因为矩阵维度不匹配而出现了错误。
您需要将`affine_relu_forward`函数中的`w`和`b`参数改为输入的参数,而不是随机生成和全零矩阵,这样就可以解决这个错误。
def affine_forward(x, w, b): out = None N = x.shape[0] x_row = x.reshape(N, -1) out = np.dot(x_row, w) + b.reshape(1, -1) cache = (x, w, b) return out, cache def affine_relu_forward(x, w, b): x = x.reshape(x.shape[0], -1) w = np.random.randn(784, 100) b = np.zeros((1, 100)) out, cache = layers.affine_forward(x, w, b) a, fc_cache = layers.affine_forward(x, w, b) out, relu_cache = layers.relu_forward(a) cache = (fc_cache, relu_cache) return out, cacheValueError: shapes (40,6272) and (784,100) not aligned: 6272 (dim 1) != 784 (dim 0)
这段代码中出现了一个错误,错误信息为"ValueError: shapes (40,6272) and (784,100) not aligned: 6272 (dim 1) != 784 (dim 0)",意思是两个矩阵的维度不匹配,无法进行矩阵乘法运算。具体来说,第一个矩阵的第二个维度为6272,而第二个矩阵的第一个维度为784,这两个维度不相等,因此无法进行乘法运算。您需要检查代码中的矩阵维度是否正确,以保证能够进行矩阵乘法运算。