Home | Trees | Indices | Help |
|
---|
|
Convolve input data with filter banks. The ``filters`` argument specifies a set of 2D filters that are convolved with the input data during execution. Convolution can be selected to be executed by linear filtering of the data, or in the frequency domain using a Discrete Fourier Transform. Input data can be given as 3D data, each row being a 2D array to be convolved with the filters, or as 2D data, in which case the ``input_shape`` argument must be specified. This node depends on ``scipy``.
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
Inherited from Inherited from |
|||
Inherited from Node | |||
---|---|---|---|
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|||
|
|
|||
approach | |||
boundary | |||
filters | |||
input_shape | |||
mode | |||
output_shape | |||
Inherited from |
|||
Inherited from Node | |||
---|---|---|---|
_train_seq List of tuples:: |
|||
dtype dtype |
|||
input_dim Input dimensions |
|||
output_dim Output dimensions |
|||
supported_dtypes Supported dtypes |
|
Input arguments: input_shape -- Is a tuple (h,w) that corresponds to the height and width of the input 2D data. If the input data is given in a flattened format, it is first reshaped before convolution approach -- 'approach' is one of ['linear', 'fft'] 'linear': convolution is done by linear filtering; 'fft': convoltion is done using the Fourier Transform If 'approach' is 'fft', the 'boundary' and 'fillvalue' arguments are ignored, and are assumed to be 'fill' and 0, respectively. (*Default* = 'fft') mode -- Convolution mode, as defined in scipy.signal.convolve2d 'mode' is one of ['valid', 'same', 'full'] (*Default* = 'full') boundary -- Boundary condition, as defined in scipy.signal.convolve2d 'boundary' is one of ['fill', 'wrap', 'symm'] (*Default* = 'fill') fillvalue -- Value to fill pad input arrays with (*Default* = 0) output_2d -- If True, the output array is 2D; the first index corresponds to data points; every output data point is the result of flattened convolution results, with the output of each filter concatenated together. If False, the output array is 4D; the format is data[idx,filter_nr,x,y], with filter_nr: index of convolution filter idx: data point index x, y: 2D coordinates
|
|
Return the list of dtypes supported by this node. Support floating point types with size smaller or equal than 64 bits. This is because fftpack does not support floating point types larger than that.
|
This method contains all pre-execution checks. It can be used when a subclass defines multiple execution methods. In this case, the output dimension depends on the type of convolution we use (padding, full, ...). Also, we want to to be able to accept 3D arrays.
|
Process the data contained in `x`. If the object is still in the training phase, the function `stop_training` will be called. `x` is a matrix having different variables on different columns and observations on the rows. By default, subclasses should overwrite `_execute` to implement their execution phase. The docstring of the `_execute` method overwrites this docstring.
|
|
|
Return True if the node can be inverted, False otherwise.
|
Return True if the node can be trained, False otherwise.
|
|
|
|
approach
|
boundary
|
filters
|
input_shape
|
mode
|
output_shape
|
Home | Trees | Indices | Help |
|
---|
Generated by Epydoc 3.0.1 on Thu Mar 10 15:27:33 2016 | http://epydoc.sourceforge.net |