def __init__(self, X, Y,layer_sizes, L2_reg, model_optimize_restarts=1):
self.X = X.copy()
self.Y = Y
self.num_data, self.input_dim = self.X.shape
self.layer_sizes = layer_sizes
rbf = lambda x: norm.pdf(x, 0, 1)
self.nonlinearity= rbf
self.sq = lambda x: np.sin(x)
noise_variance = 0.01
self.L2_reg =L2_reg
self.num_weights, self.predictions, self.logprob = \
self.make_nn_funs(layer_sizes, L2_reg, noise_variance ,self.nonlinearity )
self.log_posterior = lambda weights, t: self.logprob(weights, self.X, self.Y)
# Build variational objective.
self.objective, self.gradient, self.unpack_params= \
self.black_box_variational_inference(self.log_posterior, self.num_weights,
20)
self.rs = npr.RandomState(0)
init_mean = self.rs.randn(self.num_weights)
init_log_std = -5 * np.ones(self.num_weights)
self.init_var_params = np.concatenate([init_mean, init_log_std])
self.update_param = np.concatenate([init_mean, init_log_std])
self.num_samples=20
# variables used for running optimization process
self.optimization_runs = []
self.model_optimize_restarts=model_optimize_restarts
self.verbosity= True
self.reset= True
def __init__(self, filename="Default.log"):
self.terminal = sys.stdout
self.log = open(filename, "a")
def write(self, message):
self.terminal.write(message)
self.log.write(message)
if __name__ == '__main__':
sys.stdout = Logger("experiment.txt")
# Specify inference problem by its unnor
# Specify inference problem by its unnormalized log-posterior.
rbf = lambda x: norm.pdf(x, 0, 1)
sq = lambda x: np.sin(x)
num_weights, predictions, logprob = \
make_nn_funs(layer_sizes=[1, 10, 10, 1], L2_reg=0.01,
noise_variance = 0.01, nonlinearity=rbf)
inputs, targets = build_toy_dataset()
log_posterior = lambda weights, t: logprob(weights, inputs, targets)
# Build variational objective.
objective, gradient, unpack_params = \
black_box_variational_inference(log_posterior, num_weights,
num_samples=20)
# Set up figure.
fig = plt.figure(figsize=(8,8), facecolor='white')
