def __init__(self, **kwargs):
super(Control, self).__init__(**kwargs)
self.old_target = [None, None]
# load up our network
import glob
# this code goes into the weights folder, finds the most
# recent trial, and loads up the weights
files = sorted(glob.glob('controllers/weights/rnn*'))
print 'loading weights from %s'%files[-1]
W = np.load(files[-1])['arr_0']
num_states = 4
self.rnn = RNNet(shape=[num_states * 2, 32, 32, num_states, num_states],
layers=[Linear(), Tanh(), Tanh(), Linear(), Linear()],
rec_layers=[1,2],
conns={0:[1, 2], 1:[2], 2:[3], 3:[4]},
load_weights=W,
use_GPU=False)
offset, W_end, b_end = self.rnn.offsets[(3,4)]
self.rnn.mask = np.zeros(self.rnn.W.shape, dtype=bool)
self.rnn.mask[offset:b_end] = True
self.rnn.W[offset:W_end] = np.eye(4).flatten()
self.joint_targets = None
self.act = None
# set up recorders
if self.write_to_file is True:
from recorder import Recorder
self.u_recorder = Recorder('control signal', self.task, 'hf')
self.xy_recorder = Recorder('end-effector position', self.task, 'hf')
self.dist_recorder = Recorder('distance from target', self.task, 'hf')
self.recorders = [self.u_recorder,
self.xy_recorder,
self.dist_recorder]
