def draw_conv_weights(self):
for i, (name, weight) in enumerate(zip(self.layer_names, self._weights)):
if len(weight.shape) != 4:
return
for j, _w in enumerate(weight):
for k, _ww in enumerate(_w):
VisUtil.show_img(_ww, "{} {} filter {} channel {}".format(name, i+1, j+1, k+1))
def draw_conv_weights(self):
for i, (name, weight) in enumerate(zip(self.layer_names, self._weights)):
if len(weight.shape) != 4:
return
for j, _w in enumerate(weight):
for k, _ww in enumerate(_w):
VisUtil.show_img(_ww, "{} {} filter {} channel {}".format(name, i+1, j+1, k+1))
def draw_conv_series(self, x, shape=None):
x = np.array(x)
for xx in x:
VisUtil.show_img(VisUtil.trans_img(xx, shape), "Original")
for i, (layer, ac) in enumerate(
zip(
self._layers,
self._get_acts(
np.array([xx.transpose(1, 2, 0)],
dtype=np.float32)))):
if len(ac.shape) == 4:
VisUtil.show_batch_img(
ac[0].transpose(2, 0, 1),
"Layer {} ({})".format(i + 1, layer.name))
else:
ac = ac[0]
length = sqrt(np.prod(ac.shape))
if length < 10:
continue
(height,
width) = xx.shape[1:] if shape is None else shape[1:]
sqrt_shape = sqrt(height * width)
oh, ow = int(length * height / sqrt_shape), int(
length * width / sqrt_shape)
VisUtil.show_img(ac[:oh * ow].reshape(oh, ow),
"Layer {} ({})".format(i + 1, layer.name))
def draw_conv_series(self, x, shape=None):
x = np.asarray(x)
for xx in x:
VisUtil.show_img(VisUtil.trans_img(xx, shape), "Original")
for i, (layer, ac) in enumerate(zip(
self._layers, self._get_acts(np.array([xx.transpose(1, 2, 0)], dtype=np.float32)))):
if len(ac.shape) == 4:
VisUtil.show_batch_img(ac[0].transpose(2, 0, 1), "Layer {} ({})".format(i + 1, layer.name))
else:
ac = ac[0]
length = sqrt(np.prod(ac.shape))
if length < 10:
continue
(height, width) = xx.shape[1:] if shape is None else shape[1:]
sqrt_shape = sqrt(height * width)
oh, ow = int(length * height / sqrt_shape), int(length * width / sqrt_shape)
VisUtil.show_img(ac[:oh * ow].reshape(oh, ow), "Layer {} ({})".format(i + 1, layer.name))
def draw_conv_series(self, x, shape=None):
for xx in x:
VisUtil.show_img(VisUtil.trans_img(xx, shape), "Original")
activations = self._get_activations(np.array([xx]), predict=True)
for i, (layer, ac) in enumerate(zip(self._layers, activations)):
if len(ac.shape) == 4:
for n in ac:
_n, height, width = n.shape
a = int(ceil(sqrt(_n)))
g = np.ones((a * height + a, a * width + a), n.dtype)
g *= np.min(n)
_i = 0
for y in range(a):
for x in range(a):
if _i < _n:
g[y * height + y:(y + 1) * height + y, x * width + x:(x + 1) * width + x] = n[
_i, :, :]
_i += 1
# normalize to [0,1]
max_g = g.max()
min_g = g.min()
g = (g - min_g) / (max_g - min_g)
VisUtil.show_img(g, "Layer {} ({})".format(i + 1, layer.name))
else:
ac = ac[0]
length = sqrt(np.prod(ac.shape))
if length < 10:
continue
(height, width) = xx.shape[1:] if shape is None else shape[1:]
sqrt_shape = sqrt(height * width)
oh, ow = int(length * height / sqrt_shape), int(length * width / sqrt_shape)
VisUtil.show_img(ac[:oh*ow].reshape(oh, ow), "Layer {} ({})".format(i + 1, layer.name))
def draw_conv_series(self, x, shape=None):
for xx in x:
VisUtil.show_img(VisUtil.trans_img(xx, shape), "Original")
activations = self._get_activations(np.array([xx]), predict=True)
for i, (layer, ac) in enumerate(zip(self._layers, activations)):
if len(ac.shape) == 4:
for n in ac:
_n, height, width = n.shape
a = int(ceil(sqrt(_n)))
g = np.ones((a * height + a, a * width + a), n.dtype)
g *= np.min(n)
_i = 0
for y in range(a):
for x in range(a):
if _i < _n:
g[y * height + y:(y + 1) * height + y, x * width + x:(x + 1) * width + x] = n[
_i, :, :]
_i += 1
# normalize to [0,1]
max_g = g.max()
min_g = g.min()
g = (g - min_g) / (max_g - min_g)
VisUtil.show_img(g, "Layer {} ({})".format(i + 1, layer.name))
else:
ac = ac[0]
length = sqrt(np.prod(ac.shape))
if length < 10:
continue
(height, width) = xx.shape[1:] if shape is None else shape[1:]
sqrt_shape = sqrt(height * width)
oh, ow = int(length * height / sqrt_shape), int(length * width / sqrt_shape)
VisUtil.show_img(ac[:oh*ow].reshape(oh, ow), "Layer {} ({})".format(i + 1, layer.name))
