def pass_backward(self, grad):
input_num, input_depth, input_height, input_width = self.inputs.shape
d_input_col = np.zeros_like(self.input_col)
grad_col = grad.transpose(2, 3, 0, 1).ravel()
d_input_col = self.pool_backward(d_input_col, grad_col,
self.pool_cache)
d_input = col2im_indices(
d_input_col,
(input_num * input_depth, 1, input_height, input_width),
self.pool_size[0],
self.pool_size[1],
padding=(self.pad_height, self.pad_width),
stride=self.strides[0])
return d_input.reshape(self.inputs.shape)
def pass_backward(self, grad):
input_num, input_depth, input_height, input_width = self.input_shape
if self.is_trainable:
dbias = np.sum(grad, axis=(0, 2, 3))
dbias = dbias.reshape(self.filter_num, -1)
doutput_reshaped = grad.transpose(1, 2, 3,
0).reshape(self.filter_num, -1)
dweights = doutput_reshaped @ self.input_col.T
dweights = dweights.reshape(self.weights.shape)
# optimize the weights and bias
self.weights = optimizer(self.weight_optimizer).update(
self.weights, dweights)
self.bias = optimizer(self.weight_optimizer).update(
self.bias, dbias)
# endif self.is_trainable
weight_reshape = self.weights.reshape(self.filter_num, -1)
dinput_col = weight_reshape.T @ doutput_reshaped
pad_height, pad_width = get_pad(self.padding, input_height,
input_width, self.strides[0],
self.strides[1], self.kernel_size[0],
self.kernel_size[1])
dinputs = col2im_indices(dinput_col,
self.input_shape,
self.kernel_size[0],
self.kernel_size[1],
padding=(pad_height, pad_width),
stride=self.strides[0])
return dinputs