num_pseudo_params = 10#50
n_trials = 2
dimension_set = [[1,1],[1,1,1],[1,1,1,1]]#[[1,1],[1,1,1],[1,1,1,1]]
n_data_set = [75,150,300]
npr.seed(0)
rs = npr.RandomState(0)
results = []
for i in xrange(n_trials):
print("Trial {}".format(i))
for n_data in n_data_set:
X, y = build_step_function_dataset(D=1, n_data=n_data)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
for dimensions in dimension_set:
n_layers = len(dimensions)-1
start_time = time.time()
total_num_params, log_likelihood, sample_mean_cov_from_deep_gp, predict_layer_funcs, squared_error, create_deep_map = \
build_deep_gp(dimensions, rbf_covariance, num_pseudo_params, random)
init_params = .1 * npr.randn(total_num_params)
deep_map = create_deep_map(init_params)
init_params = initialize(deep_map,X,num_pseudo_params)
print("Optimizing covariance parameters...")
objective = lambda params: -log_likelihood(params,X,y,n_samples)
import autograd.numpy as np
import autograd.numpy.random as npr
from autograd import value_and_grad,grad
from scipy.optimize import minimize
from deep_gaussian_process_6 import rbf_covariance, pack_gp_params, pack_layer_params, pack_deep_params, build_deep_gp, initialize,build_step_function_dataset
if __name__ == '__main__':
random = 1
n_samples = 10
n_samples_to_plot = 10
n_data = 20
input_dimension = 1
num_pseudo_params = 10
X, y = build_step_function_dataset(D=input_dimension, n_data=n_data)
dimension_set = [[1,1],[1,1,1]] # Architecture of the GP. Last layer should always be 1
fig = plt.figure(figsize=(20,8), facecolor='white')
ax_one_layer = fig.add_subplot(121, frameon=False)
ax_two_layer = fig.add_subplot(122, frameon=False)
plt.show(block=False)
for dimensions in dimension_set:
n_layers = len(dimensions)-1
total_num_params, log_likelihood, sample_mean_cov_from_deep_gp, predict_layer_funcs, squared_error, create_deep_map = \
build_deep_gp(dimensions, rbf_covariance, num_pseudo_params, random)
def callback(params):
if __name__ == '__main__':
random = 1
n_samples = 10
n_samples_to_test = 100
num_pseudo_params = 10
dimensions = [1, 1, 1, 1]
n_data = 60
npr.seed(1) #Randomness comes from KMeans
rs = npr.RandomState(1)
X, y = build_step_function_dataset(D=1, n_data=n_data)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
n_layers = len(dimensions) - 1
total_num_params, log_likelihood, sample_mean_cov_from_deep_gp, predict_layer_funcs, squared_error, create_deep_map = \
build_deep_gp(dimensions, rbf_covariance, num_pseudo_params, random)
init_params = .1 * rs.randn(total_num_params)
deep_map = create_deep_map(init_params)
init_params = initialize(deep_map, X, num_pseudo_params)
print("Optimizing covariance parameters...")
