def test_one_node_updown(self):
"""Test flow updown phase with just one node."""
flow = dbn_binodes.get_DBN_flow(1, hidden_dims=[2])
N = 10000
x = mdp.numx.zeros((N, 4))
for i in range(N):
r = mdp.numx.rand()
if r>0.666:
x[i,:] = [0.,1.,0.,1.]
elif r>0.333:
x[i,:] = [1.,0.,1.,0.]
def data_gen():
for _ in range(100):
# (data, learning rate, decay, momentum)
yield (x, 0.5, 0., 0.5)
flow.train([data_gen()])
print flow[0].w_gen
flow.updown_phase([x], epsilon=0.5,
decay=0., momentum=0.5, n_updates=3,
max_iter=300, min_error=1e-2)
print flow[0].w_gen
error = flow.updown_phase([x], epsilon=0.1,
decay=0., momentum=0.9, n_updates=3,
max_iter=1000, min_error=0.05)
assert error < 0.05
def test_one_node_updown(self):
"""Test flow updown phase with just one node."""
flow = dbn_binodes.get_DBN_flow(1, hidden_dims=[2])
N = 10000
x = mdp.numx.zeros((N, 4))
for i in range(N):
r = mdp.numx.rand()
if r > 0.666:
x[i, :] = [0., 1., 0., 1.]
elif r > 0.333:
x[i, :] = [1., 0., 1., 0.]
def data_gen():
for _ in range(100):
# (data, learning rate, decay, momentum)
yield (x, 0.5, 0., 0.5)
flow.train([data_gen()])
print flow[0].w_gen
flow.updown_phase([x],
epsilon=0.5,
decay=0.,
momentum=0.5,
n_updates=3,
max_iter=300,
min_error=1e-2)
print flow[0].w_gen
error = flow.updown_phase([x],
epsilon=0.1,
decay=0.,
momentum=0.9,
n_updates=3,
max_iter=1000,
min_error=0.05)
assert error < 0.05
# -*- coding: utf-8 -*-
# Generated by codesnippet sphinx extension on 2012-10-04
import mdp
import numpy as np
np.random.seed(0)
import bimdp
import dbn_binodes
n_layers = 2
flow = dbn_binodes.get_DBN_flow(2, hidden_dims=[2, 2])
n_samples = 10000 # number of data points
n_greedy_reps = 100 # repetitions in greedy phase
x = mdp.numx.zeros((n_samples, 4))
for i in range(n_samples):
r = mdp.numx.rand()
if r > 0.666:
x[i, :] = [0., 1., 0., 1.]
elif r > 0.333:
x[i, :] = [1., 0., 1., 0.]
data_iterables = [None] + [[x] * n_greedy_reps] * n_layers + [[x]]
msg_iterables = ([None] + [[{
"epsilon": 0.1,
"decay": 0.0,
"momentum": 0.0
}] * n_greedy_reps] * n_layers + [[{
"top_updates": 3,
"epsilon": 0.1,
"decay": 0.0,
# -*- coding: utf-8 -*-
# Generated by codesnippet sphinx extension on 2012-10-04
import mdp
import numpy as np
np.random.seed(0)
import bimdp
import dbn_binodes
n_layers = 2
flow = dbn_binodes.get_DBN_flow(2, hidden_dims=[2,2])
n_samples = 10000 # number of data points
n_greedy_reps = 100 # repetitions in greedy phase
x = mdp.numx.zeros((n_samples, 4))
for i in range(n_samples):
r = mdp.numx.rand()
if r>0.666:
x[i,:] = [0.,1.,0.,1.]
elif r>0.333:
x[i,:] = [1.,0.,1.,0.]
data_iterables = [None] + [[x] * n_greedy_reps] * n_layers + [[x]]
msg_iterables = ([None] +
[[{"epsilon": 0.1, "decay": 0.0,
"momentum": 0.0}] * n_greedy_reps] * n_layers +
[[{"top_updates": 3, "epsilon": 0.1, "decay": 0.0,
"momentum": 0.0,
"max_iter": 2, "min_error": -1.0}]])
bimdp.show_training(flow, data_iterables, msg_iterables, debug=True)
