examples/utils.py
def plot_layers(weights, tied_weights=False, channels=1):
    '''Create a plot of weights, visualized as "bottom-level" pixel arrays.'''
    if hasattr(weights[0], 'get_value'):
        weights = [w.get_value() for w in weights]
    k = min(len(weights), 9)
    imgs = np.eye(weights[0].shape[0])
    for i, weight in enumerate(weights[:-1]):
        imgs = np.dot(weight.T, imgs)
        plot_images(imgs,
                    100 + 10 * k + i + 1,
                    channels=channels,
                    title='Layer {}'.format(i+1))
    weight = weights[-1]
    n = weight.shape[1] / channels
    if int(np.sqrt(n)) ** 2 != n:
        return
    if tied_weights:
        imgs = np.dot(weight.T, imgs)
        plot_images(imgs,
                    100 + 10 * k + k,
                    channels=channels,
                    title='Layer {}'.format(k))
    else:
        plot_images(weight,
                    100 + 10 * k + k,
                    channels=channels,
                    title='Decoding weights')
def plot_layers(weights, tied_weights=False, channels=1):
    '''Create a plot of weights, visualized as "bottom-level" pixel arrays.'''
    if hasattr(weights[0], 'get_value'):
        weights = [w.get_value() for w in weights]
    k = min(len(weights), 9)
    imgs = np.eye(weights[0].shape[0])
    for i, weight in enumerate(weights[:-1]):
        imgs = np.dot(weight.T, imgs)
        plot_images(imgs,
                    100 + 10 * k + i + 1,
                    channels=channels,
                    title='Layer {}'.format(i+1))
    weight = weights[-1]
    n = weight.shape[1] / channels
    if int(np.sqrt(n)) ** 2 != n:
        return
    if tied_weights:
        imgs = np.dot(weight.T, imgs)
        plot_images(imgs,
                    100 + 10 * k + k,
                    channels=channels,
                    title='Layer {}'.format(k))
    else:
        plot_images(weight,
                    100 + 10 * k + k,
                    channels=channels,
                    title='Decoding weights')
def plot_layers ( weights , tied_weights = False , channels = 1 ) : if hasattr ( weights [ 0 ] , 'get_value' ) : weights = [ w . get_value ( ) for w in weights ] k = min ( len ( weights ) , 9 ) imgs = np . eye ( weights [ 0 ] . shape [ 0 ] ) for i , weight in enumerate ( weights [ : - 1 ] ) : imgs = np . dot ( weight . T , imgs ) plot_images ( imgs , 100 + 10 * k + i + 1 , channels = channels , title = 'Layer {}' . format ( i + 1 ) ) weight = weights [ - 1 ] n = weight . shape [ 1 ] / channels if int ( np . sqrt ( n ) ) ** 2 != n : return if tied_weights : imgs = np . dot ( weight . T , imgs ) plot_images ( imgs , 100 + 10 * k + k , channels = channels , title = 'Layer {}' . format ( k ) ) else : plot_images ( weight , 100 + 10 * k + k , channels = channels , title = 'Decoding weights' )
Create a plot of weights, visualized as "bottom-level" pixel arrays.
Create a plot of weights visualized as bottom - level pixel arrays .