elektronn2.neuromancer.computations module

elektronn2.neuromancer.computations.apply_activation(x, activation_func, b1=None)[source]

Return an activation function callable matching the name Allowed names: ‘relu’, ‘tanh’,’prelu’, ‘sigmoid’, ‘maxout <i>’, ‘lin’,’abs’,’soft+’, ‘elu’, ‘selu’.

Parameters:
  • x (T.Tensor) – Input tensor.
  • activation_func (str) – Name of the activation function.
  • b1 – Optional b1 parameter for the activation function. If this is None, no parameter is passed.
Returns:

Activation function applied to x.
Return type:

T.Tensor
elektronn2.neuromancer.computations.apply_except_axis(x, axis, func)[source]

Apply a contraction function on all but one axis.

Parameters:
  • x (T.Tensor) – Input tensor.
  • axis (int) – Axis to exclude on application.
  • func (function) – A function with signature func(x, axis=) eg T.mean, T.std …
Returns:

Contraction of x, but of the same dimensionality.
Return type:

T.Tensor
elektronn2.neuromancer.computations.conv(x, w, axis_order=None, conv_dim=None, x_shape=None, w_shape=None, border_mode='valid', stride=None)[source]

Apply appropriate convolution depending on input and filter dimensionality. If input w_shape is known, conv might be replaced by tensordot

There are static assumptions which axes are spatial.

Parameters:
  • x (T.Tensor) –
    Input data (mini-batch).
    Tensor of shape (b, f, x), (b, f, x, y), (b, z, f, x, y) or (b,f,x,y,z).
  • w (T.Tensor) –
    Set of convolution filter weights.
    Tensor of shape (f_out, f_in, x), (f_out, f_in, x, y), (f_out, z, f_in, x, y) or (f_out, f_in, x, y, z).
  • axis_order (str) –
    (only relevant for 3d)
    'dnn' (b,f,x,y(,z)) or 'theano' (b, z, f, x, y).
  • conv_dim (int) – Dimensionality of the applied convolution (not the absolute dim of the inputs).
  • x_shape (tuple) – shape tuple (TaggedShape supported).
  • w_shape (tuple) – shape tuple, see w.
  • border_mode (str) –
    • 'valid': only apply filter to complete patches of the image. Generates output of shape: image_shape -filter_shape + 1.
    • 'full' zero-pads image to multiple of filter shape to generate output of shape: image_shape + filter_shape - 1.
  • stride (tuple) –
    (tuple of len 2)
    Factor by which to subsample the output.
Returns:

Set of feature maps generated by convolution.
Return type:

T.Tensor
elektronn2.neuromancer.computations.dot(x, W, axis=1)[source]

Calculate a tensordot between 1 axis of x and the first axis of W.

Requires x.shape[axis]==W.shape[0]. Identical to dot if x, W 2d and axis==1.

Parameters:
  • x (T.Tensor) – Input tensor.
  • W (T.Tensor) – Weight tensor, (f_in, f_out).
  • axis (int) – Axis on x to apply dot.
Returns:

x with dot applied. The shape of x changes on axis to n_out.
Return type:

T.Tensor
elektronn2.neuromancer.computations.fragmentpool(conv_out, pool, offsets, strides, spatial_axes, mode='max')[source]
elektronn2.neuromancer.computations.fragments2dense(fragments, offsets, strides, spatial_axes)[source]
elektronn2.neuromancer.computations.maxout(x, factor=2, axis=None)[source]

Maxpooling along the feature axis.

The feature count is reduces by factor.

Parameters:
  • x (T.Tensor) – Input tensor (b, f, x, y), (b, z, f, x, y).
  • factor (int) – Pooling factor.
  • axis (int or None) – Feature axis of x (1 or 2). If None, 5d tensors get axis 2 and all others axis 1.
Returns:

x with pooling applied.
Return type:

T.Tensor
elektronn2.neuromancer.computations.pooling(x, pool, spatial_axes, mode='max', stride=None)[source]

Pooling along spatial axes of 3d and 2d tensors.

There are static assumptions which axes are spatial. The spatial axes must be divisible by the corresponding pooling factor, otherwise the computation might crash later.

Parameters:
  • x (T.Tensor) – Input tensor (b, f, x, y), (b, z, f, x, y).
  • pool (tuple) – 2/3-tuple of pooling factors. They refer to the spatial axes of x (x,y)/(z,x,y).
  • spatial_axes (tuple) –
  • mode (str) –

    Can be any of the modes supported by Theano’s dnn_pool(): (‘max’, ‘average_inc_pad’, ‘average_exc_pad’, ‘sum’).

    ’max’ (default): max-pooling ‘average’ or ‘average_inc_pad’: average-pooling ‘sum’: sum-pooling

  • stride (tuple) –
Returns:

x with maxpooling applied. The spatial axes are decreased by the corresponding pooling factors
Return type:

T.Tensor
elektronn2.neuromancer.computations.softmax(x, axis=1, force_builtin=False)[source]

Calculate softmax (pseudo probabilities).

Parameters:
  • x (T.Tensor) – Input tensor.
  • axis (int) – Axis on which to apply softmax.
  • force_builtin (bool) – force usage of theano.tensor.nnet.softmax (more stable).
Returns:

x with softmax applied, same shape.
Return type:

T.Tensor
elektronn2.neuromancer.computations.unpooling(x, pool, spatial_axes)[source]

Symmetric unpooling with border: s_new = s*pool + pool-1.

Insert values strided, e.g for pool=3: 00x00x00x…00x00.

Parameters:
  • x (T.Tensor) – Input tensor.
  • pool (int) – Unpooling factor.
  • spatial_axes (list) – List of axes on which to perform unpooling.
Returns:

x with unpooling applied.
Return type:

T.Tensor
elektronn2.neuromancer.computations.unpooling_nd(x, pool)[source]
elektronn2.neuromancer.computations.upconv(x, w, stride, x_shape=None, w_shape=None, axis_order='dnn')[source]
elektronn2.neuromancer.computations.upsampling(x, pool, spatial_axes)[source]

Upsamling through repetition: s_new = s*p.

e.g for pool=3: aaabbbccc…

Parameters:
  • x (T.Tensor) – Input tensor.
  • pool (int) – Upsampling factor.
  • spatial_axes (list) – List of axes on which to perform upsampling.
Returns:

x with upsampling applied.
Return type:

T.Tensor
elektronn2.neuromancer.computations.upsampling_nd(x, pool)[source]