#print erations, ': ' + ', '.join(, targarr) for i in range(len(self.targname))]) Np.array(,1] for i, j in self.elnet_layout.keys()]).T, 'y-', linewidth=1) otel = self.shape_ot(np.array(,0] for i, j in self.elnet_layout.keys()]).T, Mpl.figure(figName+' Total') mpl.clf() mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap) mpl.title('%s Total Absolute Input Dependency' % layer.name) mpl.colorbar() PerColumn = int(math.ceil(fieldsN/float(perRow))) PerRow = int(math.floor(math.sqrt(fieldsN))) Mpl.imshow(tiled,cmap=cmap) mpl.title('%s Output' % layer.name) mpl.colorbar() ĭef plotFields(layer,fieldShape=None,channel=None,maxFields=25,figName='ReceptiveFields',cmap=None,padding=0.01): Temp = np.zeros(np.product(fieldShape)) temp] = wp.ravel()įields2 = np.vstack(] + list(fields.shape))])įor i in range(0,perColumn*perRow,perColumn): Mpl.figure(figOffset+1) mpl.clf() mpl.imshow(np.sum(np.abs(fields),0),cmap=cmap) mpl.title('%s Total Absolute Input Dependency' % layer.name) mpl.colorbar()ĭef plotOutput(layer,feed_dict,fieldShape=None,channel=None,figOffset=1,cmap=None): # mpl.figure(figOffset) mpl.clf() mpl.imshow(tiled,cmap=cmap) mpl.title('%s Receptive Fields' % layer.name) mpl.colorbar() # for i in range(0,perColumn*perRow,perColumn): # fields2 = np.vstack(] + list(fields.shape))]) Mpl.title('%s Receptive Fields' % layer.name) Grid = ImageGrid(fig,111,nrows_ncols=(perRow,perColumn),axes_pad=padding,cbar_mode='single')
PerColumn = int(math.ceil(fields.shape/float(perRow)))įrom mpl_toolkits.axes_grid1 import ImageGrid PerRow = int(math.floor(math.sqrt(fields.shape))) If len(np.shape(wp)) < 4: # Fully connected layer, has no shapeįields = np.reshape(wp,list(wp.shape)+fieldShape)Įlse: # Convolutional layer already has shapeįeatures, channels, iy, ix = np.shape(wp)įields = np.reshape(wp,) Pylab.ylabel('Total Resources Generated')ĭef plotFields(layer,fieldShape=None,channel=None,figOffset=1,cmap=None,padding=0.01): Pylab.legend(('Data Set no.1', 'Data Set no.2')) # plot in pylab (total resources over time)
Helper function to gain insight on provided data sets background,ĭata1 =, ,, ,, ,, ,, ]ĭata2 =, ,, ,, ,, ,, ,, ,, ,, ,, ,, ]
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