def test_plant():
"""Example of a network using a dynamic plant as the output layer."""
eps = 1e-6 # value to use for finite differences computations
dt = 1e-2 # size of time step
sig_len = 40 # how many time steps to train over
batch_size = 32 # how many updates to perform with static input
num_batches = 20000 # how many batches to run total
import sys
# NOTE: Change to wherever you keep your arm models
sys.path.append("../../../studywolf_control/studywolf_control/")
from arms.two_link.arm_python import Arm as Arm
print 'Plant is: ', Arm
arm = Arm(dt=dt, init_q=[0.736134824578, 1.85227640003])
num_states = arm.DOF * 2 # are states are [positions, velocities]
targets = gen_targets(arm=arm, sig_len=sig_len) # target joint angles
init_state = np.zeros((len(targets), num_states)) # initial velocity = 0
init_state[:, :arm.DOF] = arm.init_q # set up the initial joint angles
plant = PlantArm(arm=arm, targets=targets, init_state=init_state, eps=eps)
# open up weights folder and checked for saved weights
import glob
files = sorted(glob.glob('weights/rnn*'))
if len(files) > 0:
# if weights found, load them up and keep going from last trial
W = np.load(files[-1])['arr_0']
print 'loading from ', files[-1]
last_trial = int(
files[-1].split('weights/rnn_weights-trial')[1].split('-err')[0])
print 'last_trial: ', last_trial
else:
# if no weights found, start fresh with new random seed
W = None
last_trial = -1
seed = np.random.randint(100000000)
print 'seed : ', seed
np.random.seed(seed)
# specify the network structure and loss functions
from hessianfree.loss_funcs import SquaredError, SparseL2
rnn = RNNet(
# specify the number of nodes in each layer
shape=[num_states * 2, 32, 32, num_states, num_states],
# specify the function of the nodes in each layer
layers=[Linear(), Tanh(), Tanh(),
Linear(), plant],
# specify the layers that have recurrent connections
rec_layers=[1, 2],
# specify the connections between layers
conns={
0: [1, 2],
1: [2],
2: [3],
3: [4]
},
# specify the loss function
loss_type=[
# squared error between plant output and targets
SquaredError()
],
load_weights=W,
use_GPU=False)
# set up masking so that weights between network output
# and the plant aren't modified in learning, always = 1
offset, W_end, b_end = rnn.offsets[(3, 4)]
rnn.mask = np.zeros(rnn.W.shape, dtype=bool)
rnn.mask[offset:b_end] = True
rnn.W[offset:W_end] = np.eye(4).flatten()
for ii in range(last_trial + 1, num_batches):
print '============================================='
print 'training batch ', ii
err = rnn.run_epochs(plant,
None,
max_epochs=batch_size,
optimizer=HessianFree(CG_iter=96,
init_damping=100))
# save the weights to file, track trial and error
err = rnn.best_error
name = 'weights/rnn_weights-trial%04i-err%.5f' % (ii, err)
np.savez_compressed(name, rnn.W)
print '============================================='
print 'network: ', name
print 'final error: ', err
print '============================================='
return rnn.best_error
def gen_data_plot(folder="weights",
index=-1,
show_plot=True,
save_plot=None,
save_paths=False,
verbose=True):
files = sorted(glob.glob('%s/rnn*' % folder))
# plot the values over time
vals = []
for ii, name in enumerate(files[:index]):
if verbose: print name
name = name.split('err')[1]
name = name.split('.npz')[0]
vals.append(float(name))
vals = np.array(vals)
plt.figure(figsize=(10, 3))
ax = plt.subplot2grid((1, 3), (0, 0), colspan=2)
ax.loglog(vals)
ax.loglog(range(len(vals)), np.ones(len(vals)) * min(vals), 'r--')
ax.loglog(range(len(vals)), np.ones(len(vals)) * min(vals), 'r--')
plt.xlim([0, len(files)])
plt.ylim([10**-5, 10])
plt.title('AHF training error')
plt.xlabel('Training iterations')
plt.ylabel('Error')
plt.yscale('log')
# load in the weights and see how well they control the arm
dt = 1e-2
sig_len = 40
# HACK: append system path to have access to the arm code
# NOTE: Change this path to wherever your plant model is kept!
sys.path.append("../../../studywolf_control/studywolf_control/")
from arms.two_link.arm_python import Arm as Arm
if verbose: print 'Plant is: ', Arm
arm = Arm(dt=dt, init_q=[0.736134824578, 1.85227640003])
from hessianfree import RNNet
from hessianfree.nonlinearities import (Tanh, Linear)
from train_hf import PlantArm, gen_targets
init_type = "sparse"
rec_coeff = [1, 1]
rec_type = "sparse"
eps = 1e-6
num_states = 4
targets = gen_targets(arm, sig_len=sig_len)
init_state = np.zeros((len(targets), num_states))
init_state[:, 0] = 0.736134824578
init_state[:, 1] = 1.85227640003
plant = PlantArm(arm, targets=targets, init_state=init_state, eps=eps)
W = np.load(files[index])['arr_0']
last_trial = -1
# make sure this network is the same as the one you trained!
from hessianfree.loss_funcs import SquaredError, SparseL2
suts_gain = 10e-2 * 1e-2 # for centre-out reaching task
net_size = 96
if '32' in folder:
net_size = 32
rnn = RNNet(
shape=[num_states * 2, net_size, net_size, num_states, num_states],
layers=[Linear(), Tanh(), Tanh(),
Linear(), plant],
debug=False,
rec_layers=[1, 2],
conns={
0: [1, 2],
1: [2],
2: [3],
3: [4]
},
W_rec_params={
"coeff": rec_coeff,
"init_type": rec_type
},
load_weights=W,
use_GPU=False)
full_output = rnn.forward(plant, rnn.W)
outputs = full_output[-1]
states = np.asarray(plant.get_vecs()[0][:, :, num_states:])
targets = np.asarray(plant.get_vecs()[1])
def kin(q):
L = [.31, .27]
q0 = q[:, 0]
q1 = q[:, 1]
return [
L[0] * np.cos(q0) + L[1] * np.cos(q0 + q1),
L[0] * np.sin(q0) + L[1] * np.sin(q0 + q1)
]
ax = plt.subplot2grid((1, 3), (0, 2))
# plot start point
initx, inity = kin(init_state)
ax.plot(initx, inity, 'x', mew=10)
for jj in range(0, len(targets)):
# plot target
targetx, targety = kin(targets[jj])
ax.plot(targetx, targety, 'rx', mew=1)
# plat path
pathx, pathy = kin(states[jj, :, :])
path = np.hstack([pathx[:, None], pathy[:, None]])
if save_paths is True:
np.savez_compressed('end-effector position%.3i.npz' % int(jj / 8),
array1=path)
ax.plot(path[:, 0], path[:, 1])
plt.tight_layout()
plt.xlim([-.1, .1])
plt.ylim([.25, .45])
plt.title('Hand trajectory')
plt.xlabel('x')
plt.ylabel('y')
if save_plot is not None:
plt.savefig(save_plot)
if show_plot is True:
plt.show()
plt.close()
def gen_data_plot(folder="weights", index=-1, show_plot=True,
save_plot=None, save_paths=False, verbose=True):
files = sorted(glob.glob('%s/rnn*'%folder))
# plot the values over time
vals = []
for ii,name in enumerate(files[:index]):
if verbose: print name
name = name.split('err')[1]
name = name.split('.npz')[0]
vals.append(float(name))
vals = np.array(vals)
plt.figure(figsize=(10,3))
ax = plt.subplot2grid((1,3), (0,0), colspan=2)
ax.loglog(vals)
ax.loglog(range(len(vals)), np.ones(len(vals)) * min(vals), 'r--')
ax.loglog(range(len(vals)), np.ones(len(vals)) * min(vals), 'r--')
plt.xlim([0, len(files)])
plt.ylim([10**-5, 10])
plt.title('AHF training error')
plt.xlabel('Training iterations')
plt.ylabel('Error')
plt.yscale('log')
# load in the weights and see how well they control the arm
dt = 1e-2
sig_len = 40
# HACK: append system path to have access to the arm code
# NOTE: Change this path to wherever your plant model is kept!
sys.path.append("../../../studywolf_control/studywolf_control/")
from arms.two_link.arm_python import Arm as Arm
if verbose: print 'Plant is: ', Arm
arm = Arm(dt=dt, init_q=[0.736134824578, 1.85227640003])
from hessianfree import RNNet
from hessianfree.nonlinearities import (Tanh, Linear)
from train_hf import PlantArm, gen_targets
init_type = "sparse"
rec_coeff = [1, 1]
rec_type = "sparse"
eps = 1e-6
num_states = 4
targets = gen_targets(arm, sig_len=sig_len)
init_state = np.zeros((len(targets), num_states))
init_state[:, 0] = 0.736134824578
init_state[:, 1] = 1.85227640003
plant = PlantArm(arm, targets=targets,
init_state=init_state, eps=eps)
W = np.load(files[index])['arr_0']
last_trial = -1
# make sure this network is the same as the one you trained!
from hessianfree.loss_funcs import SquaredError, SparseL2
suts_gain = 10e-2 * 1e-2 # for centre-out reaching task
net_size = 96
if '32' in folder:
net_size = 32
rnn = RNNet(shape=[num_states * 2, net_size, net_size, num_states, num_states],
layers=[Linear(), Tanh(), Tanh(), Linear(), plant],
debug=False,
rec_layers=[1,2],
conns={0:[1, 2], 1:[2], 2:[3], 3:[4]},
W_rec_params={"coeff": rec_coeff, "init_type": rec_type},
load_weights=W,
use_GPU=False)
full_output = rnn.forward(plant, rnn.W)
outputs = full_output[-1]
states = np.asarray(plant.get_vecs()[0][:, :, num_states:])
targets = np.asarray(plant.get_vecs()[1])
def kin(q):
L = [.31, .27]
q0 = q[:, 0]
q1 = q[:, 1]
return [L[0]*np.cos(q0) + L[1]*np.cos(q0+q1),
L[0]*np.sin(q0) + L[1]*np.sin(q0+q1)]
ax = plt.subplot2grid((1,3),(0,2))
# plot start point
initx, inity = kin(init_state)
ax.plot(initx, inity, 'x', mew=10)
for jj in range(0,len(targets)):
# plot target
targetx, targety = kin(targets[jj])
ax.plot(targetx, targety, 'rx', mew=1)
# plat path
pathx, pathy = kin(states[jj,:,:])
path = np.hstack([pathx[:,None], pathy[:,None]])
if save_paths is True:
np.savez_compressed('end-effector position%.3i.npz'%int(jj/8),
array1=path)
ax.plot(path[:,0], path[:,1])
plt.tight_layout()
plt.xlim([-.1, .1])
plt.ylim([.25, .45])
plt.title('Hand trajectory')
plt.xlabel('x')
plt.ylabel('y')
if save_plot is not None:
plt.savefig(save_plot)
if show_plot is True:
plt.show()
plt.close()
def gen_data_plot(folder="weights", index=None, show_plot=True,
save_plot=None, save_paths=False, verbose=True):
files = sorted(glob.glob('%s/rnn*' % folder))
files = files[:index] if index is not None else files
# plot the values over time
vals = []
for ii, name in enumerate(files):
if verbose:
print(name)
name = name.split('err')[1]
name = name.split('.npz')[0]
vals.append(float(name))
vals = np.array(vals)
plt.figure(figsize=(10, 3))
ax = plt.subplot2grid((1, 3), (0, 0), colspan=2)
ax.loglog(vals)
ax.loglog(range(len(vals)), np.ones(len(vals)) * min(vals), 'r--')
ax.loglog(range(len(vals)), np.ones(len(vals)) * min(vals), 'r--')
plt.xlim([0, len(files)])
plt.ylim([10**-5, 10])
plt.title('AHF training error')
plt.xlabel('Training iterations')
plt.ylabel('Error')
plt.yscale('log')
# load in the weights and see how well they control the arm
dt = 1e-2
sig_len = 40
# HACK: append system path to have access to the arm code
# NOTE: Change this path to wherever your plant model is kept!
sys.path.append("../../../studywolf_control/studywolf_control/")
# from arms.two_link.arm_python import Arm as Arm
from arms.three_link.arm import Arm as Arm
if verbose:
print('Plant is: %s' % str(Arm))
arm = Arm(dt=dt)
from hessianfree import RNNet
from hessianfree.nonlinearities import (Tanh, Linear)
from train_hf_3link import PlantArm, gen_targets
rec_coeff = [1, 1]
rec_type = "sparse"
eps = 1e-6
num_states = arm.DOF * 2
targets = gen_targets(arm, sig_len=sig_len)
init_state = np.zeros((len(targets), num_states), dtype=np.float32)
init_state[:, :arm.DOF] = arm.init_q # set up the initial joint angles
plant = PlantArm(arm, targets=targets,
init_state=init_state, eps=eps)
index = -1 if index is None else index
W = np.load(files[index])['arr_0']
# make sure this network is the same as the one you trained!
net_size = 96
if '32' in folder:
net_size = 32
rnn = RNNet(shape=[num_states * 2,
net_size,
net_size,
num_states,
num_states],
layers=[Linear(), Tanh(), Tanh(), Linear(), plant],
debug=False,
rec_layers=[1, 2],
conns={0: [1, 2], 1: [2], 2: [3], 3: [4]},
W_rec_params={"coeff": rec_coeff, "init_type": rec_type},
load_weights=W,
use_GPU=False)
rnn.forward(plant, rnn.W)
states = np.asarray(plant.get_vecs()[0][:, :, num_states:])
targets = np.asarray(plant.get_vecs()[1])
def kin(q):
x = np.sum([arm.L[ii] * np.cos(np.sum(q[:, :ii+1], axis=1))
for ii in range(arm.DOF)], axis=0)
y = np.sum([arm.L[ii] * np.sin(np.sum(q[:, :ii+1], axis=1))
for ii in range(arm.DOF)], axis=0)
return x,y
ax = plt.subplot2grid((1, 3), (0, 2))
# plot start point
initx, inity = kin(init_state)
ax.plot(initx, inity, 'x', mew=10)
for jj in range(0, len(targets)):
# plot target
targetx, targety = kin(targets[jj])
ax.plot(targetx, targety, 'rx', mew=1)
# plat path
pathx, pathy = kin(states[jj, :, :])
path = np.hstack([pathx[:, None], pathy[:, None]])
if save_paths is True:
np.savez_compressed('end-effector position%.3i.npz' % int(jj/8),
array1=path)
ax.plot(path[:, 0], path[:, 1])
plt.tight_layout()
# plt.xlim([-.1, .1])
# plt.ylim([.25, .45])
plt.title('Hand trajectory')
plt.xlabel('x')
plt.ylabel('y')
if save_plot is not None:
plt.savefig(save_plot)
if show_plot is True:
plt.show()
plt.close()
def test_plant():
"""Example of a network using a dynamic plant as the output layer."""
eps = 1e-6 # value to use for finite differences computations
dt = 1e-2 # size of time step
sig_len = 40 # how many time steps to train over
batch_size = 32 # how many updates to perform with static input
num_batches = 20000 # how many batches to run total
import sys
# NOTE: Change to wherever you keep your arm models
sys.path.append("../../../studywolf_control/studywolf_control/")
from arms.two_link.arm_python import Arm as Arm
print 'Plant is: ', Arm
arm = Arm(dt=dt, init_q=[0.736134824578, 1.85227640003])
num_states = arm.DOF * 2 # are states are [positions, velocities]
targets = gen_targets(arm=arm, sig_len=sig_len) # target joint angles
init_state = np.zeros((len(targets), num_states)) # initial velocity = 0
init_state[:, :arm.DOF] = arm.init_q # set up the initial joint angles
plant = PlantArm(arm=arm, targets=targets,
init_state=init_state, eps=eps)
# open up weights folder and checked for saved weights
import glob
files = sorted(glob.glob('weights/rnn*'))
if len(files) > 0:
# if weights found, load them up and keep going from last trial
W = np.load(files[-1])['arr_0']
print 'loading from ', files[-1]
last_trial = int(files[-1].split('weights/rnn_weights-trial')[1].split('-err')[0])
print 'last_trial: ', last_trial
else:
# if no weights found, start fresh with new random seed
W = None
last_trial = -1
seed = np.random.randint(100000000)
print 'seed : ', seed
np.random.seed(seed)
# specify the network structure and loss functions
from hessianfree.loss_funcs import SquaredError, SparseL2
rnn = RNNet(
# specify the number of nodes in each layer
shape=[num_states * 2, 32, 32, num_states, num_states],
# specify the function of the nodes in each layer
layers=[Linear(), Tanh(), Tanh(), Linear(), plant],
# specify the layers that have recurrent connections
rec_layers=[1,2],
# specify the connections between layers
conns={0:[1, 2], 1:[2], 2:[3], 3:[4]},
# specify the loss function
loss_type=[
# squared error between plant output and targets
SquaredError()],
load_weights=W,
use_GPU=False)
# set up masking so that weights between network output
# and the plant aren't modified in learning, always = 1
offset, W_end, b_end = rnn.offsets[(3,4)]
rnn.mask = np.zeros(rnn.W.shape, dtype=bool)
rnn.mask[offset:b_end] = True
rnn.W[offset:W_end] = np.eye(4).flatten()
for ii in range(last_trial+1, num_batches):
print '============================================='
print 'training batch ', ii
err = rnn.run_epochs(plant, None, max_epochs=batch_size,
optimizer=HessianFree(CG_iter=96, init_damping=100))
# save the weights to file, track trial and error
err = rnn.best_error
name = 'weights/rnn_weights-trial%04i-err%.5f'%(ii, err)
np.savez_compressed(name, rnn.W)
print '============================================='
print 'network: ', name
print 'final error: ', err
print '============================================='
return rnn.best_error
