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model checkgrad enhancement

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Zhenwen Dai 2014-11-05 11:58:05 +00:00
parent 6f06646437
commit 9febc73be3
2 changed files with 9 additions and 4 deletions
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11
GPy/core/model.py
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@ -263,7 +263,7 @@ class Model(Parameterized):
sgd.run() sgd.run()
self.optimization_runs.append(sgd) self.optimization_runs.append(sgd)
def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3): def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3, df_tolerance=1e-12):
""" """
Check the gradient of the ,odel by comparing to a numerical Check the gradient of the ,odel by comparing to a numerical
estimate. If the verbose flag is passed, individual estimate. If the verbose flag is passed, individual
@ -322,7 +322,7 @@ class Model(Parameterized):
except NotImplementedError: except NotImplementedError:
names = ['Variable %i' % i for i in range(len(x))] names = ['Variable %i' % i for i in range(len(x))]
# Prepare for pretty-printing # Prepare for pretty-printing
header = ['Name', 'Ratio', 'Difference', 'Analytical', 'Numerical'] header = ['Name', 'Ratio', 'Difference', 'Analytical', 'Numerical', 'dF_ratio']
max_names = max([len(names[i]) for i in range(len(names))] + [len(header[0])]) max_names = max([len(names[i]) for i in range(len(names))] + [len(header[0])])
float_len = 10 float_len = 10
cols = [max_names] cols = [max_names]
@ -359,6 +359,8 @@ class Model(Parameterized):
f1 = self._objective(xx) f1 = self._objective(xx)
xx[xind] -= 2.*step xx[xind] -= 2.*step
f2 = self._objective(xx) f2 = self._objective(xx)
df_ratio = np.abs((f1-f2)/f1)
df_unstable = df_ratio<df_tolerance
numerical_gradient = (f1 - f2) / (2 * step) numerical_gradient = (f1 - f2) / (2 * step)
if np.all(gradient[xind] == 0): ratio = (f1 - f2) == gradient[xind] if np.all(gradient[xind] == 0): ratio = (f1 - f2) == gradient[xind]
else: ratio = (f1 - f2) / (2 * step * gradient[xind]) else: ratio = (f1 - f2) / (2 * step * gradient[xind])
@ -370,12 +372,15 @@ class Model(Parameterized):
else: else:
formatted_name = "\033[91m {0} \033[0m".format(names[nind]) formatted_name = "\033[91m {0} \033[0m".format(names[nind])
ret &= False ret &= False
if df_unstable:
formatted_name = "\033[94m {0} \033[0m".format(names[nind])
r = '%.6f' % float(ratio) r = '%.6f' % float(ratio)
d = '%.6f' % float(difference) d = '%.6f' % float(difference)
g = '%.6f' % gradient[xind] g = '%.6f' % gradient[xind]
ng = '%.6f' % float(numerical_gradient) ng = '%.6f' % float(numerical_gradient)
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4]) df = '%1.e' % float(df_ratio)
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}|{5:^{c5}}".format(formatted_name, r, d, g, ng, df, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4], c5=cols[5])
print grad_string print grad_string
self.optimizer_array = x self.optimizer_array = x

2
GPy/examples/dimensionality_reduction.py
View file

@ -348,7 +348,7 @@ def ssgplvm_simulation(optimize=True, verbose=1,
D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9 D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
_, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim) _, _, Ylist = _simulate_matern(D1, D2, D3, N, num_inducing, plot_sim)
Y = Ylist[0] Y = Ylist[0]
k = kern.Linear(Q, ARD=True, useGPU=useGPU)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q) k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
#k = kern.RBF(Q, ARD=True, lengthscale=10.) #k = kern.RBF(Q, ARD=True, lengthscale=10.)
m = SSGPLVM(Y, Q, init="pca", num_inducing=num_inducing, kernel=k) m = SSGPLVM(Y, Q, init="pca", num_inducing=num_inducing, kernel=k)
m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape) m.X.variance[:] = _np.random.uniform(0,.01,m.X.shape)