# chainer.functions.deconvolution_2d¶

chainer.functions.deconvolution_2d(x, W, b=None, stride=1, pad=0, outsize=None, *, dilate=1, groups=1)[source]

Two dimensional deconvolution function.

This is an implementation of two-dimensional deconvolution. In most of deep learning frameworks and papers, this function is called transposed convolution. But because of historical reasons (e.g. paper by Ziller Deconvolutional Networks) and backward compatibility, this function is called deconvolution in Chainer.

It takes three variables: input image x, the filter weight W, and the bias vector b.

Notation: here is a notation for dimensionalities.

• $$n$$ is the batch size.

• $$c_I$$ and $$c_O$$ are the number of the input and output channels, respectively.

• $$h_I$$ and $$w_I$$ are the height and width of the input image, respectively.

• $$h_K$$ and $$w_K$$ are the height and width of the filters, respectively.

• $$h_P$$ and $$w_P$$ are the height and width of the spatial padding size, respectively.

Let $$(s_Y, s_X)$$ be the stride of filter application. Then, the output size $$(h_O, w_O)$$ is estimated by the following equations:

$\begin{split}h_O &= s_Y (h_I - 1) + h_K - 2h_P,\\ w_O &= s_X (w_I - 1) + w_K - 2w_P.\end{split}$

The output of this function can be non-deterministic when it uses cuDNN. If chainer.configuration.config.deterministic is True and cuDNN version is >= v3, it forces cuDNN to use a deterministic algorithm.

Deconvolution links can use a feature of cuDNN called autotuning, which selects the most efficient CNN algorithm for images of fixed-size, can provide a significant performance boost for fixed neural nets. To enable, set chainer.using_config(‘autotune’, True)

Parameters
Returns

Output variable of shape $$(n, c_O, h_O, w_O)$$.

Return type

Variable

Deconvolution2D to manage the model parameters W and b.

Example

>>> n = 10
>>> c_i, c_o = 1, 3
>>> h_i, w_i = 5, 10
>>> h_k, w_k = 10, 10
>>> h_p, w_p = 5, 5
>>> x = np.random.uniform(0, 1, (n, c_i, h_i, w_i)).astype(np.float32)
>>> x.shape
(10, 1, 5, 10)
>>> W = np.random.uniform(0, 1, (c_i, c_o, h_k, w_k)).astype(np.float32)
>>> W.shape
(1, 3, 10, 10)
>>> b = np.random.uniform(0, 1, c_o).astype(np.float32)
>>> b.shape
(3,)
>>> s_y, s_x = 5, 5
>>> y = F.deconvolution_2d(x, W, b, stride=(s_y, s_x), pad=(h_p, w_p))
>>> y.shape
(10, 3, 20, 45)
>>> h_o = s_y * (h_i - 1) + h_k - 2 * h_p
>>> w_o = s_x * (w_i - 1) + w_k - 2 * w_p
>>> y.shape == (n, c_o, h_o, w_o)
True