def calculate_contraction_ratios(self):
print "calculating contraction ratios for ", self.exptype()
self.cratios = []
if not self.conf["stack"][0]["twolayer"]:
W1 = self.weights["ae_weights-after_pretrain"]
b1 = self.weights["ae_bias_h-after_pretrain"]
W2 = self.weights["ae_weights1-after_pretrain"]
b2 = self.weights["ae_bias_h1-after_pretrain"]
else:
W1 = self.weights["ae_weights1-after_pretrain"]
b1 = self.weights["ae_bias_h1a-after_pretrain"]
W2 = self.weights["ae_weights2-after_pretrain"]
b2 = self.weights["ae_bias_h2-after_pretrain"]
D = mnist.get_test_data()[0]
v0 = jacobian_2l(W1, b1, W2, b2, D, only_norm=True)['v']
self.cratios_x = np.arange(1, 40, 1.0)
pbar = ProgressBar(maxval=len(self.cratios_x))
for r in self.cratios_x:
rnd = np.random.normal(size=D.shape)
# norm to one in each row
rnd *= r / np.sqrt(np.sum(rnd ** 2, 1))[:, None]
v = jacobian_2l(W1, b1, W2, b2, D + rnd, only_norm=True)['v']
self.cratios.append((v0 - v) / r)
pbar.update(r)
pbar.finish()
def calculate_jacobian(self):
if not self.want_weights:
return
print "calculating spectra for ", self.exptype()
D = mnist.get_test_data()[0]
if not self.conf["stack"][0]["twolayer"]:
W1 = self.weights["ae_weights-after_pretrain"]
b1 = self.weights["ae_bias_h-after_pretrain"]
W2 = self.weights["ae_weights1-after_pretrain"]
b2 = self.weights["ae_bias_h1-after_pretrain"]
J = jacobian_2l(W1, b1, W2, b2, D)
else:
W1 = self.weights["ae_weights1-after_pretrain"]
b1 = self.weights["ae_bias_h1a-after_pretrain"]
W2 = self.weights["ae_weights2-after_pretrain"]
b2 = self.weights["ae_bias_h2-after_pretrain"]
J = jacobian_2l(W1, b1, W2, b2, D)
self.spec = J["s"]
self.spec_std = J["s_std"]
