def printKT(Q, i):
kt.exporttiles(array=Q,
height=1,
width=len(Q[0]),
outer_height=1,
outer_width=len(Q),
filename="results/obs_W_1_" + i + ".pgm")
def __init__(self):
self.values, self.height, self.width = KT.importimage ("bird_chirp.pnm")
print "values' shape=", numpy.shape(self.values), " height=", self.height, " width=", self.width
# values have shape 129*182; now bring them into shape (129,182)
self.values = numpy.reshape(self.values, (self.height,self.width))
print "values' shape=", numpy.shape(self.values)
KT.exporttiles (self.values, self.height, self.width, "/tmp/coco/obs_I_0.pgm") # display
# transpose this data matrix, so one data point is values[.]
self.values = numpy.transpose(self.values)
KT.exporttiles (self.values[0], self.height, 1, "/tmp/coco/obs_J_0.pgm")
self.t = 0
def visualisation(self):
KT.exporttiles(self.weights11[:, 0:-1], self.nhidden, self.nin,
basedir + "obs_V_1_0.pgm")
KT.exporttiles(self.weights12[:, 0:-1], self.nhidden, self.nin,
basedir + "obs_W_1_0.pgm")
KT.exporttiles(self.weights2[:, 0:-1], self.nhidden, self.nout,
basedir + "obs_V_2_1.pgm")
KT.exporttiles(self.weights4[:, 0:-1], self.nhidden, self.ncritic,
basedir + "obs_W_2_1.pgm")
print 'visualisation updated!!'
def __init__(self):
digitsalph = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ"
self.values = numpy.zeros((len(digitsalph), 8*8))
for tt in range(len(digitsalph)):
filename = "digits_alph/digit" + digitsalph[tt] + ".pgm"
self.values[tt], h, w = KT.importimage (filename)
if h != 8 or w != 8:
print "digits_alph files don't match expected size!"
self.datumsize = h*w
self.seqlen = len(digitsalph)
def visualize(self):
# print(self.weights)
KTimage.exporttiles(
self.weights[0],
self.width, self.height,
"/tmp/coco/obs_W_{}_{}.pgm".format(0 + 1, 0),
self.width, self.height)
KTimage.exporttiles(
self.weights[1].T,
self.width, self.height,
"/tmp/coco/obs_W_{}_{}.pgm".format(1 + 1, 1),
self.width, self.height)
KTimage.exporttiles(self.inputs[0], self.width, self.height,
"/tmp/coco/obs_S_0.pgm")
KTimage.exporttiles(self.inputs[1], self.width, self.height,
"/tmp/coco/obs_S_1.pgm")
KTimage.exporttiles(self.outputs[-1], self.width, self.height,
"/tmp/coco/obs_S_2.pgm")
def train(self, iteration):
gamma = 0.9
""" Train the thing """
# Add the inputs that match the bias node
self.maxIteration = iteration
self.error_action_sav = numpy.zeros((2, self.maxIteration / 10))
self.error_val_sav = numpy.zeros((self.maxIteration / 10))
self.error_total_action_sav = numpy.zeros((2, self.maxIteration / 10))
self.error_total_val_sav = numpy.zeros((self.maxIteration / 10))
self.average_duration_sav = numpy.zeros((self.maxIteration))
for iteration in range(0, self.maxIteration):
error = 0.0
error_val = 0.0
total_duration = 0
updateTimes = 0
self.visualisation()
for StartPos in range(0, self.__perception.StartPosCount**2):
self.__perception.newinit()
reward = 0.0
duration = 0
self.hidden = numpy.zeros((self.nhidden))
landmark_info = numpy.array(
self.__perception.sense()) # 2 continuous inputs
inputs_bias = numpy.concatenate(
(landmark_info, -1 * numpy.ones((1))), axis=0)
# ------- sigmoidal funcion for hidden layer 1 -------
self.hidden1_y = dot(self.weights11, inputs_bias)
self.hidden1 = 1.0 / (1.0 +
numpy.exp(-self.beta1 * self.hidden1_y))
self.hiddenplusone1 = numpy.concatenate(
(self.hidden1, -1 * numpy.ones(
(1))), axis=0) # add a bias unit
# ------- end of sigmoidal funcion for hidden layer 1 -------
h_out = self.mlpfwd()
h = self.normalised(h_out)
h_angle = math.atan2(h[1], h[0])
action_angle = self.__perception.rand_winner(
h_angle, self.sigma)
action = numpy.zeros((2))
action[0] = math.cos(action_angle)
action[1] = math.sin(action_angle)
action = self.normalised(action)
# ------- sigmoidal funcion for hidden layer 2 -------
self.hidden2_y = dot(self.weights12, inputs_bias)
self.hidden2 = 1.0 / (1.0 +
numpy.exp(-self.beta * self.hidden2_y))
self.hiddenplusone2 = numpy.concatenate(
(self.hidden2, -1 * numpy.ones(
(1))), axis=0) # add a bias unit
# ------- end of sigmoidal funcion for hidden layer 2 -------
val = self.valfwd()
r = self.__perception.reward() # read reward
while (r != 1.0) and duration < 1000:
duration += 1
total_duration += 1
updatew11 = numpy.zeros((numpy.shape(self.weights11)))
updatew12 = numpy.zeros((numpy.shape(self.weights12)))
updatew2 = numpy.zeros((numpy.shape(self.weights2)))
updatew4 = numpy.zeros((numpy.shape(self.weights4)))
self.__perception.act(action)
landmark_info_tic = numpy.array(
self.__perception.sense()) # 2 continuous inputs
r = self.__perception.reward() # read reward
inputs_bias_tic = numpy.concatenate(
(landmark_info_tic, -1 * numpy.ones((1))), axis=0)
KT.exporttiles(inputs_bias_tic, self.nin + 1, 1,
basedir + "obs_S_0.pgm")
# ------- sigmoidal funcion for hidden layer 1 -------
self.hidden1_y = dot(self.weights11, inputs_bias_tic)
self.hidden1 = 1.0 / (
1.0 + numpy.exp(-self.beta1 * self.hidden1_y))
self.hiddenplusone1 = numpy.concatenate(
(self.hidden1, -1 * numpy.ones(
(1))), axis=0) # add a bias unit
# ------- end of sigmoidal funcion for hidden layer 1 -------
h_out = self.mlpfwd()
h = self.normalised(h_out)
h_angle = math.atan2(h[1], h[0])
action_tic_angle = self.__perception.rand_winner(
h_angle, self.sigma)
action_tic = numpy.zeros((2))
action_tic[0] = math.cos(action_tic_angle)
action_tic[1] = math.sin(action_tic_angle)
action_tic = self.normalised(action_tic)
# ------- sigmoidal funcion for hidden layer 2 -------
self.hidden2_y = dot(self.weights12, inputs_bias_tic)
self.hidden2 = 1.0 / (
1.0 + numpy.exp(-self.beta * self.hidden2_y))
self.hiddenplusone2 = numpy.concatenate(
(self.hidden2, -1 * numpy.ones(
(1))), axis=0) # add a bias unit
# ------- end of sigmoidal funcion for hidden layer 2 -------
if self.__perception.sel_x > 0.1 and self.__perception.sel_y > 0.1 and self.__perception.sel_x < 0.3 and self.__perception.sel_y < 0.3:
KT.exporttiles(self.hidden1, 1, self.nhidden,
basedir + "obs_S_1.pgm")
KT.exporttiles(self.hidden2, 1, self.nhidden,
basedir + "obs_S_2.pgm")
if self.__perception.sel_x > 0.6 and self.__perception.sel_y > 0.6 and self.__perception.sel_x < 0.7 and self.__perception.sel_y < 0.7:
KT.exporttiles(self.hidden1, 1, self.nhidden,
basedir + "obs_A_1.pgm")
KT.exporttiles(self.hidden2, 1, self.nhidden,
basedir + "obs_A_2.pgm")
val_tic = self.valfwd()
# ----- here are the training process--------#
if r == 1.0: # reward achieved
target = r
else: # because critic weights now converge.
target = gamma * val_tic # gamma = 0.9
# prediction error;
deltao = (target - val)
error_val += abs(deltao)
updatew4 = self.eps * (outer(deltao, self.hiddenplusone2))
deltah0 = self.hiddenplusone2 * (
1 - self.hiddenplusone2) * (dot(
transpose(self.weights4), deltao))
updatew12 = self.eta * (outer(deltah0[:-1],
inputs_bias_tic))
self.weights12 += updatew12
self.weights4 += updatew4
if gamma * val_tic > val or r == 1.0:
updateTimes += 1
error += abs(action - h)
deltao2 = (action - h) / numpy.linalg.norm(action - h)
deltah0 = self.hiddenplusone1 * (
1 - self.hiddenplusone1) * dot(
transpose(self.weights2), deltao2)
updatew11 = self.eta * (outer(deltah0[:-1],
inputs_bias_tic))
self.weights11 += updatew11
updatew2 = self.eta * (outer(deltao2,
self.hiddenplusone1))
self.weights2 += updatew2
##-------------end update when the critic are higher-----------##
landmark_info = landmark_info_tic
action = action_tic
val = val_tic
if (iteration % 1 == 0):
print "iteration:", iteration
print "Error in val:", error_val, "average per move:", error_val / float(
total_duration + 1)
print "Error in action:", error, "average per move:", error / float(
updateTimes + 1)
print "Total duration:", total_duration
print "Average duration", total_duration / (
self.__perception.StartPosCount**2)
print "Update Times:", updateTimes
self.average_duration_sav[iteration] = total_duration / (
self.__perception.StartPosCount**2)
def visualisation(self):
KT.exporttiles(self.weights11[:,0:-1], self.nhidden, self.nin, basedir+"obs_V_1_0.pgm")
KT.exporttiles(self.weights12[:,0:-1], self.nhidden, self.nin, basedir+"obs_W_1_0.pgm")
KT.exporttiles(self.weights2[:,0:-1], self.nhidden, self.nout, basedir+"obs_V_2_1.pgm")
KT.exporttiles(self.weights4[:,0:-1], self.nhidden, self.ncritic, basedir+"obs_W_2_1.pgm")
print 'visualisation updated!!'
def train(self,iteration):
gamma = 0.9
""" Train the thing """
# Add the inputs that match the bias node
self.maxIteration = iteration
self.error_action_sav = numpy.zeros((2,self.maxIteration/10))
self.error_val_sav = numpy.zeros((self.maxIteration/10))
self.error_total_action_sav = numpy.zeros((2,self.maxIteration/10))
self.error_total_val_sav = numpy.zeros((self.maxIteration/10))
self.average_duration_sav = numpy.zeros((self.maxIteration))
for iteration in range(0,self.maxIteration):
error = 0.0
error_val = 0.0
total_duration = 0
updateTimes = 0
self.visualisation()
for StartPos in range(0, self.__perception.StartPosCount**2):
self.__perception.newinit()
reward = 0.0
duration = 0
self.hidden = numpy.zeros((self.nhidden))
landmark_info = numpy.array(self.__perception.sense()) # 2 continuous inputs
inputs_bias = numpy.concatenate((landmark_info,-1*numpy.ones((1))),axis=0)
# ------- sigmoidal funcion for hidden layer 1 -------
self.hidden1_y = dot(self.weights11,inputs_bias)
self.hidden1 = 1.0 / (1.0 + numpy.exp(-self.beta1*self.hidden1_y))
self.hiddenplusone1 = numpy.concatenate((self.hidden1,-1*numpy.ones((1))),axis=0) # add a bias unit
# ------- end of sigmoidal funcion for hidden layer 1 -------
h_out = self.mlpfwd()
h = self.normalised(h_out)
h_angle = math.atan2(h[1], h[0])
action_angle = self.__perception.rand_winner(h_angle,self.sigma)
action = numpy.zeros((2))
action[0] = math.cos(action_angle)
action[1] = math.sin(action_angle)
action = self.normalised(action)
# ------- sigmoidal funcion for hidden layer 2 -------
self.hidden2_y = dot(self.weights12,inputs_bias)
self.hidden2 = 1.0 / (1.0 + numpy.exp(-self.beta*self.hidden2_y))
self.hiddenplusone2 = numpy.concatenate((self.hidden2,-1*numpy.ones((1))),axis=0) # add a bias unit
# ------- end of sigmoidal funcion for hidden layer 2 -------
val = self.valfwd()
r = self.__perception.reward() # read reward
while (r != 1.0) and duration < 1000:
duration += 1
total_duration += 1
updatew11 = numpy.zeros((numpy.shape(self.weights11)))
updatew12 = numpy.zeros((numpy.shape(self.weights12)))
updatew2 = numpy.zeros((numpy.shape(self.weights2)))
updatew4 = numpy.zeros((numpy.shape(self.weights4)))
self.__perception.act(action)
landmark_info_tic = numpy.array(self.__perception.sense()) # 2 continuous inputs
r = self.__perception.reward() # read reward
inputs_bias_tic = numpy.concatenate((landmark_info_tic,-1*numpy.ones((1))),axis=0)
KT.exporttiles(inputs_bias_tic, self.nin+1, 1, basedir+"obs_S_0.pgm")
# ------- sigmoidal funcion for hidden layer 1 -------
self.hidden1_y = dot(self.weights11,inputs_bias_tic)
self.hidden1 = 1.0 / (1.0 + numpy.exp(-self.beta1*self.hidden1_y))
self.hiddenplusone1 = numpy.concatenate((self.hidden1,-1*numpy.ones((1))),axis=0) # add a bias unit
# ------- end of sigmoidal funcion for hidden layer 1 -------
h_out = self.mlpfwd()
h = self.normalised(h_out)
h_angle = math.atan2(h[1], h[0])
action_tic_angle = self.__perception.rand_winner(h_angle,self.sigma)
action_tic = numpy.zeros((2))
action_tic[0] = math.cos(action_tic_angle)
action_tic[1] = math.sin(action_tic_angle)
action_tic = self.normalised(action_tic)
# ------- sigmoidal funcion for hidden layer 2 -------
self.hidden2_y = dot(self.weights12,inputs_bias_tic)
self.hidden2 = 1.0 / (1.0 + numpy.exp(-self.beta*self.hidden2_y))
self.hiddenplusone2 = numpy.concatenate((self.hidden2,-1*numpy.ones((1))),axis=0) # add a bias unit
# ------- end of sigmoidal funcion for hidden layer 2 -------
if self.__perception.sel_x > 0.1 and self.__perception.sel_y > 0.1 and self.__perception.sel_x < 0.3 and self.__perception.sel_y < 0.3:
KT.exporttiles(self.hidden1, 1, self.nhidden, basedir+"obs_S_1.pgm")
KT.exporttiles(self.hidden2, 1, self.nhidden, basedir+"obs_S_2.pgm")
if self.__perception.sel_x > 0.6 and self.__perception.sel_y > 0.6 and self.__perception.sel_x < 0.7 and self.__perception.sel_y < 0.7:
KT.exporttiles(self.hidden1, 1, self.nhidden, basedir+"obs_A_1.pgm")
KT.exporttiles(self.hidden2, 1, self.nhidden, basedir+"obs_A_2.pgm")
val_tic = self.valfwd()
# ----- here are the training process--------#
if r == 1.0: # reward achieved
target = r
else: # because critic weights now converge.
target = gamma * val_tic # gamma = 0.9
# prediction error;
deltao = (target-val)
error_val += abs(deltao)
updatew4 = self.eps * (outer(deltao,self.hiddenplusone2))
deltah0 = self.hiddenplusone2*(1-self.hiddenplusone2)*(dot(transpose(self.weights4),deltao))
updatew12 = self.eta * (outer(deltah0[:-1],inputs_bias_tic))
self.weights12 += updatew12
self.weights4 += updatew4
if gamma * val_tic > val or r == 1.0:
updateTimes += 1
error += abs(action - h)
deltao2 = (action-h) / numpy.linalg.norm(action-h)
deltah0 = self.hiddenplusone1 * (1-self.hiddenplusone1) * dot(transpose(self.weights2),deltao2)
updatew11 = self.eta*(outer(deltah0[:-1],inputs_bias_tic))
self.weights11 += updatew11
updatew2 = self.eta * (outer(deltao2, self.hiddenplusone1))
self.weights2 += updatew2
##-------------end update when the critic are higher-----------##
landmark_info = landmark_info_tic
action = action_tic
val = val_tic
if (iteration%1 == 0):
print "iteration:", iteration
print "Error in val:", error_val, "average per move:", error_val/float(total_duration+1)
print "Error in action:", error, "average per move:", error/float(updateTimes+1)
print "Total duration:", total_duration
print "Average duration", total_duration / (self.__perception.StartPosCount**2)
print "Update Times:", updateTimes
self.average_duration_sav[iteration] = total_duration / (self.__perception.StartPosCount**2)
deltaOutputLayer = BP_GESCHWINDIGKEIT * np.outer(
fehler, sigAusgabeHiddenLayer)
print(deltaOutputLayer)
deltaHiddenLayer = BP_GESCHWINDIGKEIT * np.outer(
backHiddenLayer, eingabe)
print(outputLayer)
deltaBiasOutputLayer = BP_GESCHWINDIGKEIT * fehler
deltaBiasHiddenLayer = BP_GESCHWINDIGKEIT * backHiddenLayer
# Aenderung aller Werte
outputLayer += deltaOutputLayer
hiddenLayer += deltaHiddenLayer
biasOutputLayer += deltaBiasOutputLayer
biasHiddenLayer += deltaBiasHiddenLayer
#if (durchlauf % 100 == 0):
# print("Durchlauf: " + str(durchlauf))
# print("Koordinate: " + str(i))
# print(fehler)
kt.exporttiles(array=hiddenLayer,
height=1,
width=2,
outer_height=GROESSE_HD,
outer_width=1,
filename="results/obs_H_1_0.pgm")
def visualize(self, path):
"""create visualization for weights at <path>"""
if not os.path.isdir(os.path.dirname(path)):
os.makedirs(os.path.dirname(path))
KTimage.exporttiles(self.weights, self.in_size,
1, path, 1, self.size)
print(bird.dim())
network = MultiLayerNetwork(
layout=(in_out_size, 200, in_out_size),
transfer_function = MultiLayerNetwork.sigmoid_function,
last_transfer_function = MultiLayerNetwork.direct_function)
training_data = []
for i in range(data_sets - 1):
inputs = bird.sensor()
bird.act()
data_set = [inputs, bird.sensor()]
training_data.append(data_set)
errors = network.train_until_fit(
training_data=training_data,
train_steps=500,
learn_rate=0.2,
max_trains=1000)
# plt.plot(errors)
# plt.show()
KTimage.exporttiles(
X=network.weigths,
h=network.layout[1],
w=network.layout[0]+1,
filename="test",
x=network.layout[2],
y=network.layout[1]+1)
counter = 0
while (True):
# move the robot somehow
vrep.simxSetJointTargetVelocity(
clientID, leftMotorHandle, 3.1415 * 0.25, vrep.simx_opmode_oneshot)
vrep.simxSetJointTargetVelocity(
clientID, rightMotorHandle, 3.1415 * 0.1, vrep.simx_opmode_oneshot)
# get vision sensor image
err, res, img = vrep.simxGetVisionSensorImage(
clientID, visionSensorHandle, 0, vrep.simx_opmode_oneshot_wait)
colval = img_to_numpy(res, img)
if counter % 10 == 0:
KT.exporttiles(
numpy.transpose(colval), res[1], res[0], "/tmp/coco/obs_I_0.pgm", 3, 1)
# this is only for the err return value - some other functions don't
# return a reliable err code!
err, objs = vrep.simxGetObjects(
clientID, vrep.sim_handle_all, vrep.simx_opmode_oneshot_wait)
if err != vrep.simx_error_noerror:
print("Error in loop: simxGetObjects can't get handles anymore!")
exit(2)
# initialize to start
if counter % 100 == 0:
# stop simulation
vrep.simxStopSimulation(clientID, vrep.simx_opmode_oneshot_wait)
# wait a sec .. else the restart doesn't work
time.sleep(1)
def visu(self):
for i in range(len(self.W)):
KT.exporttiles (self.W[i]
, self.W[i].shape[0], self.W[i].shape[1], '/tmp/coco/obs_W_' + str(i) + '_'+str(i+1)+'.pgm' )# , len(self.W[i+1].T) , 1)
def mlptrain(self, eta_bias, eta, MaxIterations):
""" Train the thing """
updatew_in_hidden1_delay = zeros(
(shape(self.weights_in_hidden1_delay)))
updatew_in_hidden2_delay = zeros(
(shape(self.weights_in_hidden2_delay)))
updatew_in_hidden1 = zeros((shape(self.weights_in_hidden1)))
updatew_in_hidden2 = zeros((shape(self.weights_in_hidden2)))
updatew_hidden1_out = zeros((shape(self.weights_hidden1_out)))
updatew_hidden2_out = zeros((shape(self.weights_hidden2_out)))
updatew_hidden1_hidden1 = zeros((shape(self.weights_hidden1_hidden1)))
updatew_hidden2_hidden2 = zeros((shape(self.weights_hidden2_hidden2)))
error = 0.0
for n in range(MaxIterations):
old_error = error
error = 0.0
self.hidden1 = zeros((self.ndata * self.nLayer, self.nhidden1))
self.hidden2 = zeros((self.ndata * self.nLayer, self.nhidden2))
self.hidden1_y = zeros((self.ndata * self.nLayer, self.nhidden1))
self.hidden2_y = zeros((self.ndata * self.nLayer, self.nhidden2))
self.hidden1_z = zeros((self.ndata * self.nLayer, self.nhidden1))
self.hidden2_z = zeros((self.ndata * self.nLayer, self.nhidden2))
#hidden layer states of all samples by one iteration
self.hidden1[0] = 0.5 * ones(self.nhidden1)
self.hidden2[0] = 0.5 * ones(self.nhidden2)
outputs_sav = numpy.zeros(
(self.ndata * self.nLayer,
self.nLayer * self.size_in_h * self.size_in_w))
horProduct1_sav = numpy.zeros(
(self.ndata * self.nLayer,
self.nLayer * self.size_in_h * self.size_in_w))
horProduct2_sav = numpy.zeros(
(self.ndata * self.nLayer,
self.nLayer * self.size_in_h * self.size_in_w))
for self.iter in range(self.ndata * self.nLayer):
currenttar = self.targets[self.iter] #current target
if self.iter % (self.nSampleInLoop) == 0:
self.outputs = self.mlpfwd(self.new_inputs[self.iter],
numpy.zeros((self.nin)))
else:
self.outputs = self.mlpfwd(self.new_inputs[self.iter],
self.new_inputs[self.iter - 1])
outputs_sav[self.iter, :] = self.outputs
horProduct1_sav[self.iter, :] = self.horProduct1
horProduct2_sav[self.iter, :] = self.horProduct2
if self.iter % (self.nSampleInLoop) > 0:
error += 0.5 * sum((currenttar - self.outputs)**2)
# Different types of output neurons
if self.outtype == 'linear':
deltao = (currenttar - self.outputs)
elif self.outtype == 'logistic':
deltao = (currenttar - self.outputs) * self.outputs * (
1.0 - self.outputs)
elif self.outtype == 'softmax':
deltao = (currenttar - self.outputs)
else:
print "error"
# error in hidden node of current time-step
deltah0_1 = self.hidden1[self.iter] * (
1.0 - (self.hidden1[self.iter])) * (dot(
transpose(self.weights_hidden1_out),
deltao * self.horProduct2)) * (self.__a1)
deltah0_2 = self.hidden2[self.iter] * (
1.0 - (self.hidden2[self.iter])) * (dot(
transpose(self.weights_hidden2_out),
deltao * self.horProduct1)) * (self.__a2)
if (self.iter % self.nLayer > 1):
# error in hidden node of previous time-step, caused by recurrent
deltah2_2 = self.hidden2[self.iter - 1] * (
1.0 - self.hidden2[self.iter - 1]) * (dot(
transpose(self.weights_hidden2_hidden2),
deltah0_2))
deltah1_1 = self.hidden1[self.iter - 1] * (
1.0 - self.hidden1[self.iter - 1]) * (dot(
transpose(self.weights_hidden1_hidden1),
deltah0_1))
# update of weight between hidden layer and input (current and time-delay), learning only after movement starts
if self.iter % (self.nSampleInLoop) > 0:
updatew_in_hidden1 = eta * (outer(
deltah0_1, self.new_inputs[self.iter]))
updatew_in_hidden2 = eta * (outer(
deltah0_2, self.new_inputs[self.iter]))
updatew_in_hidden1_delay = eta * (outer(
deltah0_1, self.new_inputs[self.iter - 1]))
updatew_in_hidden2_delay = eta * (outer(
deltah0_2, self.new_inputs[self.iter - 1]))
if (self.iter % (self.nSampleInLoop) > 1):
updatew_in_hidden2 += eta * (outer(
deltah2_2, self.new_inputs[self.iter - 1]))
# update of weight between hidden layer and output, learning only after movement starts
if self.iter % (self.nSampleInLoop) > 0:
updatew_hidden1_out = eta * (outer(
deltao * self.horProduct2,
self.hidden1[self.iter] * self.__a1))
updatew_hidden2_out = eta * (outer(
deltao * self.horProduct1,
self.hidden2[self.iter] * self.__a2))
updatew_bias1_out = eta_bias * (outer(
dot(updatew_hidden1_out,
transpose(self.hidden1[self.iter])),
self.bias_hidden1_out))
updatew_bias2_out = eta_bias * (outer(
dot(updatew_hidden2_out,
transpose(self.hidden2[self.iter])),
self.bias_hidden2_out))
# update within recurrent weights
if (self.iter % (self.nSampleInLoop) > 1):
updatew_hidden2_hidden2 = eta * (outer(
deltah0_2, self.hidden2[self.iter - 1]))
updatew_hidden1_hidden1 = eta * (outer(
deltah0_1, self.hidden1[self.iter - 1]))
if (self.iter % (self.nSampleInLoop) > 2):
updatew_hidden2_hidden2 += eta * (outer(
deltah2_2, self.hidden2[self.iter - 2]))
updatew_hidden1_hidden1 += eta * (outer(
deltah1_1, self.hidden1[self.iter - 2]))
if (self.iter % (self.nSampleInLoop) > 0):
self.weights_in_hidden1_delay += updatew_in_hidden1_delay
self.weights_in_hidden2_delay += updatew_in_hidden2_delay
if self.iter % (self.nSampleInLoop) > 0:
self.weights_in_hidden1 += updatew_in_hidden1
self.weights_in_hidden2 += updatew_in_hidden2
self.weights_hidden1_out += updatew_hidden1_out
self.weights_hidden2_out += updatew_hidden2_out
self.weights_bias1_out += updatew_bias1_out
self.weights_bias2_out += updatew_bias2_out
self.weights_hidden2_hidden2 += updatew_hidden2_hidden2
self.weights_hidden1_hidden1 += updatew_hidden1_hidden1
self.weights_in_hidden1_delay = numpy.clip(
self.weights_in_hidden1_delay, 0.0, numpy.inf)
self.weights_in_hidden2_delay = numpy.clip(
self.weights_in_hidden2_delay, 0.0, numpy.inf)
self.weights_in_hidden1 = numpy.clip(
self.weights_in_hidden1, 0.0, numpy.inf)
self.weights_in_hidden2 = numpy.clip(
self.weights_in_hidden2, 0.0, numpy.inf)
self.weights_hidden1_out = numpy.clip(
self.weights_hidden1_out, 0.0, numpy.inf)
self.weights_hidden2_out = numpy.clip(
self.weights_hidden2_out, 0.0, numpy.inf)
# ----- Update of intrinsic plasticity ----- (Eq. 9 and 10)
inv_mu1 = 1.0 / self.__mu1
yz_1 = self.hidden1_y[self.iter] * self.hidden1_z[
self.iter]
dA_1 = 1.0 / self.__a1 + self.hidden1_y[
self.
iter] - 2 * yz_1 - inv_mu1 * yz_1 + inv_mu1 * yz_1 * self.hidden1_z[
self.iter]
dB_1 = 1.0 - 2 * self.hidden1_z[
self.iter] - inv_mu1 * self.hidden1_z[
self.iter] + inv_mu1 * self.hidden1_z[
self.iter] * self.hidden1_z[self.iter]
self.__a1 += self.__eta_a * dA_1
self.__b1 += self.__eta_b * dB_1
inv_mu2 = 1.0 / self.__mu2
yz_2 = self.hidden2_y[self.iter] * self.hidden2_z[
self.iter]
dA_2 = 1.0 / self.__a2 + self.hidden2_y[
self.
iter] - 2 * yz_2 - inv_mu2 * yz_2 + inv_mu2 * yz_2 * self.hidden2_z[
self.iter]
dB_2 = 1.0 - 2 * self.hidden2_z[
self.iter] - inv_mu2 * self.hidden2_z[
self.iter] + inv_mu2 * self.hidden2_z[
self.iter] * self.hidden2_z[self.iter]
self.__a2 += self.__eta_a * dA_2
self.__b2 += self.__eta_b * dB_2
# ----- End of update of intrinsic plasticity -----
# normalization
for i in range(self.nhidden1):
self.weights_in_hidden1_delay[i] /= numpy.linalg.norm(
self.weights_in_hidden1_delay[i])
self.weights_in_hidden1[i] /= numpy.linalg.norm(
self.weights_in_hidden1[i])
self.weights_hidden1_hidden1[i] /= numpy.linalg.norm(
self.weights_hidden1_hidden1[i])
for i in range(self.nhidden2):
self.weights_in_hidden2_delay[i] /= numpy.linalg.norm(
self.weights_in_hidden2_delay[i])
self.weights_in_hidden2[i] /= numpy.linalg.norm(
self.weights_in_hidden2[i])
self.weights_hidden2_hidden2[i] /= numpy.linalg.norm(
self.weights_hidden2_hidden2[i])
self.true_w_hidden1_out = self.weights_hidden1_out[:,
0:self.nhidden1]
self.true_w_hidden2_out = self.weights_hidden2_out[:,
0:self.nhidden2]
if n % self.visual_step == 0:
KT.exporttiles(self.hidden1, self.ndata * self.nLayer,
self.nLayer, basedir + "obs_H_1.pgm")
KT.exporttiles(self.hidden2, self.ndata * self.nLayer,
self.nhidden2, basedir + "obs_G_1.pgm")
KT.exporttiles(self.hidden1_z, self.ndata * self.nLayer,
self.nLayer, basedir + "obs_H_3.pgm")
KT.exporttiles(self.hidden2_z, self.ndata * self.nLayer,
self.nhidden2, basedir + "obs_G_3.pgm")
KT.exporttiles(self.__a1, 1, self.nhidden1,
basedir + "obs_A_1.pgm")
KT.exporttiles(self.__b1, 1, self.nhidden1,
basedir + "obs_B_1.pgm")
KT.exporttiles(self.__a2, 1, self.nhidden2,
basedir + "obs_C_1.pgm")
KT.exporttiles(self.__b2, 1, self.nhidden2,
basedir + "obs_D_1.pgm")
KT.exporttiles(self.weights_bias1_out,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_E_3.pgm")
KT.exporttiles(self.weights_bias2_out,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_F_3.pgm")
KT.exporttiles(self.weights_in_hidden1,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_W_1_0.pgm", self.nhidden1, 1)
KT.exporttiles(self.weights_in_hidden2,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_V_1_0.pgm", self.nhidden2, 1)
KT.exporttiles(self.weights_in_hidden1_delay,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_W_0_0.pgm", self.nhidden1, 1)
KT.exporttiles(self.weights_in_hidden2_delay,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_V_0_0.pgm", self.nhidden2, 1)
KT.exporttiles(
transpose(self.true_w_hidden1_out) + 0.5,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_W_2_1.pgm", self.nhidden1, 1)
KT.exporttiles(
transpose(self.true_w_hidden2_out) + 0.5,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_V_2_1.pgm", self.nhidden2, 1)
KT.exporttiles(self.weights_hidden2_hidden2, self.nhidden2,
self.nhidden2, basedir + "obs_V_1_1.pgm")
KT.exporttiles(self.weights_hidden1_hidden1, self.nhidden1,
self.nhidden1, basedir + "obs_W_1_1.pgm")
KT.exporttiles(outputs_sav + 0.5, self.size_in_h * self.nLayer,
self.size_in_w, basedir + "obs_O_2.pgm",
self.nLayer, self.ndata)
KT.exporttiles(horProduct1_sav + 1.0,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_M_2.pgm", self.nLayer,
self.ndata)
KT.exporttiles(horProduct2_sav + 1.0,
self.size_in_h * self.nLayer, self.size_in_w,
basedir + "obs_N_2.pgm", self.nLayer,
self.ndata)
print 'iteration', n, 'error', error
if abs(error - old_error) < 1e-8:
print 'no more improvements during training, existing..'
break
if (error - old_error) > 1e2 and (old_error != 0):
print error, old_error
print 'error increasing, existing..'
break
def mlptrain(self,eta_bias,eta,MaxIterations):
""" Train the thing """
updatew_in_hidden1_delay = zeros((shape(self.weights_in_hidden1_delay)))
updatew_in_hidden2_delay = zeros((shape(self.weights_in_hidden2_delay)))
updatew_in_hidden1 = zeros((shape(self.weights_in_hidden1)))
updatew_in_hidden2 = zeros((shape(self.weights_in_hidden2)))
updatew_hidden1_out = zeros((shape(self.weights_hidden1_out)))
updatew_hidden2_out = zeros((shape(self.weights_hidden2_out)))
updatew_hidden1_hidden1 = zeros((shape(self.weights_hidden1_hidden1)))
updatew_hidden2_hidden2 = zeros((shape(self.weights_hidden2_hidden2)))
error = 0.0
for n in range(MaxIterations):
old_error = error
error = 0.0
self.hidden1 = zeros((self.ndata*self.nLayer,self.nhidden1))
self.hidden2 = zeros((self.ndata*self.nLayer,self.nhidden2))
self.hidden1_y = zeros((self.ndata*self.nLayer, self.nhidden1))
self.hidden2_y = zeros((self.ndata*self.nLayer, self.nhidden2))
self.hidden1_z = zeros((self.ndata*self.nLayer, self.nhidden1))
self.hidden2_z = zeros((self.ndata*self.nLayer, self.nhidden2))
#hidden layer states of all samples by one iteration
self.hidden1[0] = 0.5 * ones(self.nhidden1)
self.hidden2[0] = 0.5 * ones(self.nhidden2)
outputs_sav = numpy.zeros((self.ndata*self.nLayer, self.nLayer*self.size_in_h*self.size_in_w))
horProduct1_sav = numpy.zeros((self.ndata*self.nLayer, self.nLayer*self.size_in_h*self.size_in_w))
horProduct2_sav = numpy.zeros((self.ndata*self.nLayer, self.nLayer*self.size_in_h*self.size_in_w))
for self.iter in range(self.ndata * self.nLayer):
currenttar = self.targets[self.iter] #current target
if self.iter%(self.nSampleInLoop) == 0:
self.outputs = self.mlpfwd(self.new_inputs[self.iter], numpy.zeros((self.nin)))
else:
self.outputs = self.mlpfwd(self.new_inputs[self.iter], self.new_inputs[self.iter - 1])
outputs_sav[self.iter,:] = self.outputs
horProduct1_sav[self.iter,:] = self.horProduct1
horProduct2_sav[self.iter,:] = self.horProduct2
if self.iter%(self.nSampleInLoop) > 0:
error += 0.5*sum((currenttar-self.outputs)**2)
# Different types of output neurons
if self.outtype == 'linear':
deltao = (currenttar-self.outputs)
elif self.outtype == 'logistic':
deltao = (currenttar-self.outputs)*self.outputs*(1.0-self.outputs)
elif self.outtype == 'softmax':
deltao = (currenttar-self.outputs)
else:
print "error"
# error in hidden node of current time-step
deltah0_1 = self.hidden1[self.iter] * (1.0-(self.hidden1[self.iter]))*(dot(transpose(self.weights_hidden1_out),deltao*self.horProduct2))*(self.__a1)
deltah0_2 = self.hidden2[self.iter] * (1.0-(self.hidden2[self.iter]))*(dot(transpose(self.weights_hidden2_out),deltao*self.horProduct1))*(self.__a2)
if (self.iter%self.nLayer > 1):
# error in hidden node of previous time-step, caused by recurrent
deltah2_2 = self.hidden2[self.iter-1]*(1.0-self.hidden2[self.iter-1])*(dot(transpose(self.weights_hidden2_hidden2),deltah0_2))
deltah1_1 = self.hidden1[self.iter-1]*(1.0-self.hidden1[self.iter-1])*(dot(transpose(self.weights_hidden1_hidden1),deltah0_1))
# update of weight between hidden layer and input (current and time-delay), learning only after movement starts
if self.iter%(self.nSampleInLoop) > 0:
updatew_in_hidden1 = eta*(outer(deltah0_1,self.new_inputs[self.iter]))
updatew_in_hidden2 = eta*(outer(deltah0_2,self.new_inputs[self.iter]))
updatew_in_hidden1_delay = eta*(outer(deltah0_1,self.new_inputs[self.iter - 1]))
updatew_in_hidden2_delay = eta*(outer(deltah0_2,self.new_inputs[self.iter - 1]))
if (self.iter%(self.nSampleInLoop) > 1):
updatew_in_hidden2 += eta*(outer(deltah2_2,self.new_inputs[self.iter - 1]))
# update of weight between hidden layer and output, learning only after movement starts
if self.iter%(self.nSampleInLoop) > 0:
updatew_hidden1_out = eta*(outer(deltao*self.horProduct2,self.hidden1[self.iter]*self.__a1))
updatew_hidden2_out = eta*(outer(deltao*self.horProduct1,self.hidden2[self.iter]*self.__a2))
updatew_bias1_out = eta_bias*(outer(dot(updatew_hidden1_out, transpose(self.hidden1[self.iter])) , self.bias_hidden1_out))
updatew_bias2_out = eta_bias*(outer(dot(updatew_hidden2_out, transpose(self.hidden2[self.iter])) , self.bias_hidden2_out))
# update within recurrent weights
if (self.iter%(self.nSampleInLoop) > 1):
updatew_hidden2_hidden2 = eta*(outer(deltah0_2,self.hidden2[self.iter-1]))
updatew_hidden1_hidden1 = eta*(outer(deltah0_1,self.hidden1[self.iter-1]))
if (self.iter%(self.nSampleInLoop) > 2):
updatew_hidden2_hidden2 += eta*(outer(deltah2_2,self.hidden2[self.iter-2]))
updatew_hidden1_hidden1 += eta*(outer(deltah1_1,self.hidden1[self.iter-2]))
if (self.iter%(self.nSampleInLoop) > 0):
self.weights_in_hidden1_delay += updatew_in_hidden1_delay
self.weights_in_hidden2_delay += updatew_in_hidden2_delay
if self.iter%(self.nSampleInLoop) > 0:
self.weights_in_hidden1 += updatew_in_hidden1
self.weights_in_hidden2 += updatew_in_hidden2
self.weights_hidden1_out += updatew_hidden1_out
self.weights_hidden2_out += updatew_hidden2_out
self.weights_bias1_out += updatew_bias1_out
self.weights_bias2_out += updatew_bias2_out
self.weights_hidden2_hidden2 += updatew_hidden2_hidden2
self.weights_hidden1_hidden1 += updatew_hidden1_hidden1
self.weights_in_hidden1_delay = numpy.clip(self.weights_in_hidden1_delay, 0.0, numpy.inf)
self.weights_in_hidden2_delay = numpy.clip(self.weights_in_hidden2_delay, 0.0, numpy.inf)
self.weights_in_hidden1 = numpy.clip(self.weights_in_hidden1, 0.0, numpy.inf)
self.weights_in_hidden2 = numpy.clip(self.weights_in_hidden2, 0.0, numpy.inf)
self.weights_hidden1_out = numpy.clip(self.weights_hidden1_out, 0.0, numpy.inf)
self.weights_hidden2_out = numpy.clip(self.weights_hidden2_out, 0.0, numpy.inf)
# ----- Update of intrinsic plasticity ----- (Eq. 9 and 10)
inv_mu1 = 1.0/self.__mu1
yz_1 = self.hidden1_y[self.iter]*self.hidden1_z[self.iter]
dA_1 = 1.0/self.__a1 + self.hidden1_y[self.iter] - 2*yz_1 - inv_mu1*yz_1 + inv_mu1*yz_1*self.hidden1_z[self.iter]
dB_1 = 1.0 - 2*self.hidden1_z[self.iter] - inv_mu1*self.hidden1_z[self.iter] + inv_mu1*self.hidden1_z[self.iter]*self.hidden1_z[self.iter]
self.__a1 += self.__eta_a * dA_1
self.__b1 += self.__eta_b * dB_1
inv_mu2 = 1.0/self.__mu2
yz_2 = self.hidden2_y[self.iter]*self.hidden2_z[self.iter]
dA_2 = 1.0/self.__a2 + self.hidden2_y[self.iter] - 2*yz_2 - inv_mu2*yz_2 + inv_mu2*yz_2*self.hidden2_z[self.iter]
dB_2 = 1.0 - 2*self.hidden2_z[self.iter] - inv_mu2*self.hidden2_z[self.iter] + inv_mu2*self.hidden2_z[self.iter]*self.hidden2_z[self.iter]
self.__a2 += self.__eta_a * dA_2
self.__b2 += self.__eta_b * dB_2
# ----- End of update of intrinsic plasticity -----
# normalization
for i in range(self.nhidden1):
self.weights_in_hidden1_delay[i] /= numpy.linalg.norm(self.weights_in_hidden1_delay[i])
self.weights_in_hidden1[i] /= numpy.linalg.norm(self.weights_in_hidden1[i])
self.weights_hidden1_hidden1[i] /= numpy.linalg.norm(self.weights_hidden1_hidden1[i])
for i in range(self.nhidden2):
self.weights_in_hidden2_delay[i] /= numpy.linalg.norm(self.weights_in_hidden2_delay[i])
self.weights_in_hidden2[i] /= numpy.linalg.norm(self.weights_in_hidden2[i])
self.weights_hidden2_hidden2[i] /= numpy.linalg.norm(self.weights_hidden2_hidden2[i])
self.true_w_hidden1_out = self.weights_hidden1_out[:, 0:self.nhidden1]
self.true_w_hidden2_out = self.weights_hidden2_out[:, 0:self.nhidden2]
if n%self.visual_step == 0:
KT.exporttiles(self.hidden1, self.ndata*self.nLayer, self.nLayer, basedir+"obs_H_1.pgm")
KT.exporttiles(self.hidden2, self.ndata*self.nLayer, self.nhidden2, basedir+"obs_G_1.pgm")
KT.exporttiles(self.hidden1_z, self.ndata*self.nLayer, self.nLayer, basedir+"obs_H_3.pgm")
KT.exporttiles(self.hidden2_z, self.ndata*self.nLayer, self.nhidden2, basedir+"obs_G_3.pgm")
KT.exporttiles(self.__a1, 1, self.nhidden1, basedir+"obs_A_1.pgm")
KT.exporttiles(self.__b1, 1, self.nhidden1, basedir+"obs_B_1.pgm")
KT.exporttiles(self.__a2, 1, self.nhidden2, basedir+"obs_C_1.pgm")
KT.exporttiles(self.__b2, 1, self.nhidden2, basedir+"obs_D_1.pgm")
KT.exporttiles(self.weights_bias1_out, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_E_3.pgm")
KT.exporttiles(self.weights_bias2_out, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_F_3.pgm")
KT.exporttiles(self.weights_in_hidden1, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_W_1_0.pgm", self.nhidden1, 1)
KT.exporttiles(self.weights_in_hidden2, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_V_1_0.pgm", self.nhidden2, 1)
KT.exporttiles(self.weights_in_hidden1_delay, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_W_0_0.pgm", self.nhidden1, 1)
KT.exporttiles(self.weights_in_hidden2_delay, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_V_0_0.pgm", self.nhidden2, 1)
KT.exporttiles(transpose(self.true_w_hidden1_out)+0.5, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_W_2_1.pgm", self.nhidden1, 1)
KT.exporttiles(transpose(self.true_w_hidden2_out)+0.5, self.size_in_h *self.nLayer, self.size_in_w, basedir+"obs_V_2_1.pgm", self.nhidden2, 1)
KT.exporttiles(self.weights_hidden2_hidden2, self.nhidden2, self.nhidden2, basedir+"obs_V_1_1.pgm")
KT.exporttiles(self.weights_hidden1_hidden1, self.nhidden1, self.nhidden1, basedir+"obs_W_1_1.pgm")
KT.exporttiles(outputs_sav + 0.5, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_O_2.pgm", self.nLayer, self.ndata)
KT.exporttiles(horProduct1_sav + 1.0, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_M_2.pgm", self.nLayer, self.ndata)
KT.exporttiles(horProduct2_sav + 1.0, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_N_2.pgm", self.nLayer, self.ndata)
print 'iteration', n, 'error', error
if abs(error-old_error)<1e-8:
print 'no more improvements during training, existing..'
break
if (error - old_error) > 1e2 and (old_error != 0):
print error, old_error
print 'error increasing, existing..'
break
def __init__(self, size, nObject, beta=1, outtype='linear'):
""" Constructor """
# Set up network size
self.size = size
self.size_in_h = self.size
self.size_in_w = self.size
self.size_out_h = self.size
self.size_out_w = self.size
self.visual_step = 100
self.nLayer = nObject
self.nhidden1 = self.nLayer # layer 1 represents object identity. Number of units should be the same as layers
self.nhidden2 = 4 * self.size**2 # layer 2 represents object movement info. Number of units equals to size of the grid in each layer multiplying 4 directions.
self.beta = beta
self.__forward_alpha_v = 0.70 # when doing forward transformation function, y = alpha*y + (1-alpha)*input*w
self.__forward_alpha_d = 0.0
self.__a1 = 2.5 * numpy.ones((self.nhidden1))
self.__b1 = -5.0 * numpy.ones((self.nhidden1))
self.__mu1 = 0.20
self.__a2 = 2.5 * numpy.ones((self.nhidden2))
self.__b2 = -5.0 * numpy.ones((self.nhidden2))
self.__mu2 = 0.0025
self.__eta_a = 0.0001
self.__eta_b = 0.00001
self.outtype = outtype
self.nSampleInLoop = self.size + 1
self.dataIteration = self.size_in_h * self.size_in_w # starting point of one block, equals to the size of block, i.e. width * height
self.ndata = self.dataIteration * self.nSampleInLoop * 4 # number of the training data set in one layer (one object)
print "Generating data with number", self.ndata, 'in one layer'
self.inputs, self.targets = self.__genLargerData(
) # generate data for training
print 'The complete training data includes', self.ndata * self.nLayer, 'samples.'
print 'Size of each layer:', self.size**2
self.nin = shape(self.inputs)[1] * shape(self.inputs)[2]
self.nout = shape(self.targets)[1] * shape(self.targets)[2]
#self.new_inputs = self.inputs
self.new_inputs = numpy.reshape(
self.inputs, (self.ndata * self.nLayer,
self.nLayer * self.size_in_h * self.size_in_w))
self.targets = numpy.reshape(self.targets,
(self.ndata * self.nLayer, self.nLayer *
self.size_in_h * self.size_in_w))
KT.exporttiles(self.new_inputs + 1, self.size_in_h * self.nLayer,
self.size_in_w, basedir + "obs_T_0.pgm", self.nLayer,
self.ndata)
print 'Training data saved...'
# Initialise network
self.weights_in_hidden1 = (
numpy.random.rand(self.nhidden1, self.nin) - 0.5) * 2 / sqrt(
self.nin) #weights between input and hidden1
self.weights_in_hidden2 = (
numpy.random.rand(self.nhidden2, self.nin) - 0.5) * 2 / sqrt(
self.nin) #weights between input and hidden2
self.weights_hidden1_out = (
numpy.random.rand(self.nout, self.nhidden1) - 0.5) * 2 / sqrt(
self.nhidden1) #weights between hidden1 and output
self.weights_hidden2_out = (
numpy.random.rand(self.nout, self.nhidden2) - 0.5) * 2 / sqrt(
self.nhidden2) #weights between hidden2 and output
self.bias_hidden1_out = -0.1 * numpy.ones((1))
self.bias_hidden2_out = -0.1 * numpy.ones((1))
self.weights_bias1_out = (numpy.random.rand(self.nout, 1) - 0.5) * 2
self.weights_bias2_out = (numpy.random.rand(self.nout, 1) - 0.5) * 2
self.weights_hidden1_hidden1 = (
numpy.random.rand(self.nhidden1, self.nhidden1) - 0.5) * 2 / sqrt(
self.nhidden1) #weights within hidden1
self.weights_hidden2_hidden2 = (
numpy.random.rand(self.nhidden2, self.nhidden2) - 0.5) * 2 / sqrt(
self.nhidden2) #weights within hidden2
self.weights_in_hidden1_delay = (numpy.random.rand(
self.nhidden1, self.nin) - 0.5) * 2 / sqrt(self.nin)
self.weights_in_hidden2_delay = (numpy.random.rand(
self.nhidden2, self.nin) - 0.5) * 2 / sqrt(self.nin)
def __init__(self,size, nObject, beta=1, outtype='linear'):
""" Constructor """
# Set up network size
self.size = size
self.size_in_h = self.size
self.size_in_w = self.size
self.size_out_h = self.size
self.size_out_w = self.size
self.visual_step = 100
self.nLayer = nObject
self.nhidden1 = self.nLayer # layer 1 represents object identity. Number of units should be the same as layers
self.nhidden2 = 4 * self.size**2 # layer 2 represents object movement info. Number of units equals to size of the grid in each layer multiplying 4 directions.
self.beta = beta
self.__forward_alpha_v = 0.70 # when doing forward transformation function, y = alpha*y + (1-alpha)*input*w
self.__forward_alpha_d = 0.0
self.__a1 = 2.5*numpy.ones((self.nhidden1))
self.__b1 = -5.0*numpy.ones((self.nhidden1))
self.__mu1 = 0.20
self.__a2 = 2.5*numpy.ones((self.nhidden2))
self.__b2 = -5.0*numpy.ones((self.nhidden2))
self.__mu2 = 0.0025
self.__eta_a = 0.0001
self.__eta_b = 0.00001
self.outtype = outtype
self.nSampleInLoop = self.size + 1
self.dataIteration = self.size_in_h * self.size_in_w # starting point of one block, equals to the size of block, i.e. width * height
self.ndata = self.dataIteration * self.nSampleInLoop * 4 # number of the training data set in one layer (one object)
print "Generating data with number", self.ndata, 'in one layer'
self.inputs, self.targets = self.__genLargerData() # generate data for training
print 'The complete training data includes', self.ndata*self.nLayer, 'samples.'
print 'Size of each layer:', self.size**2
self.nin = shape(self.inputs)[1] * shape(self.inputs)[2]
self.nout = shape(self.targets)[1] * shape(self.targets)[2]
#self.new_inputs = self.inputs
self.new_inputs = numpy.reshape(self.inputs,(self.ndata*self.nLayer, self.nLayer*self.size_in_h*self.size_in_w))
self.targets = numpy.reshape(self.targets, (self.ndata*self.nLayer, self.nLayer*self.size_in_h*self.size_in_w))
KT.exporttiles(self.new_inputs + 1, self.size_in_h*self.nLayer, self.size_in_w, basedir+"obs_T_0.pgm", self.nLayer, self.ndata)
print 'Training data saved...'
# Initialise network
self.weights_in_hidden1 = (numpy.random.rand(self.nhidden1,self.nin)-0.5)*2/sqrt(self.nin) #weights between input and hidden1
self.weights_in_hidden2 = (numpy.random.rand(self.nhidden2,self.nin)-0.5)*2/sqrt(self.nin) #weights between input and hidden2
self.weights_hidden1_out = (numpy.random.rand(self.nout,self.nhidden1)-0.5)*2/sqrt(self.nhidden1) #weights between hidden1 and output
self.weights_hidden2_out = (numpy.random.rand(self.nout,self.nhidden2)-0.5)*2/sqrt(self.nhidden2) #weights between hidden2 and output
self.bias_hidden1_out = -0.1*numpy.ones((1))
self.bias_hidden2_out = -0.1*numpy.ones((1))
self.weights_bias1_out = (numpy.random.rand(self.nout,1)-0.5)*2
self.weights_bias2_out = (numpy.random.rand(self.nout,1)-0.5)*2
self.weights_hidden1_hidden1 = (numpy.random.rand(self.nhidden1,self.nhidden1)-0.5)*2/sqrt(self.nhidden1) #weights within hidden1
self.weights_hidden2_hidden2 = (numpy.random.rand(self.nhidden2,self.nhidden2)-0.5)*2/sqrt(self.nhidden2) #weights within hidden2
self.weights_in_hidden1_delay = (numpy.random.rand(self.nhidden1,self.nin)-0.5)*2/sqrt(self.nin)
self.weights_in_hidden2_delay = (numpy.random.rand(self.nhidden2,self.nin)-0.5)*2/sqrt(self.nin)
pos_img1 = img_find_cm(binval1)
print "pos_img1 =", pos_img1
# get image from vision sensor 2
err,res,img = vrep.simxGetVisionSensorImage(clientID,visionSensor2Handle,0,vrep.simx_opmode_oneshot_wait)
colval2 = img_to_numpy(res,img)
# get the binary image marking the cyan ball in Youbot's gripper
binval2 = img_binarise(colval2,0.0,0.15,0.7,0.99,0.9,1.0)
# get the x- and y-coordinates of the cyan ball
binval2 = numpy.reshape(binval2,(res[1],res[0]))
pos_img2 = img_find_cm(binval2)
print "pos_img2 =", pos_img2
# export images for look.tcl display
if counter % 5 == 0:
KT.exporttiles (numpy.transpose(colval1), res[1], res[0], "/tmp/coco/obs_I_0.pgm", 3, 1)
KT.exporttiles (binval1, res[1], res[0], "/tmp/coco/obs_J_0.pgm")
KT.exporttiles (numpy.transpose(colval2), res[1], res[0], "/tmp/coco/obs_I_1.pgm", 3, 1)
KT.exporttiles (binval2, res[1], res[0], "/tmp/coco/obs_J_1.pgm")
# write the joint data and the ball-position data into one file (if joints are near target and if cyan ball is seen in both images)
if (joint_pos_err < 0.00001 and pos_img1[0] >= 0.0 and pos_img1[1] >= 0.0 and pos_img2[0] >= 0.0 and pos_img2[1] >= 0.0):
fobj = open("outfile.dat", "a")
fobj.write("%.6f %.6f %.6f %.6f %.6f %.6f %.6f %.6f\n" % (rand_base_rotate, rand_base_lower, rand_elbow_bend, rand_hand_bend, pos_img1[0], pos_img1[1], pos_img2[0], pos_img2[1]))
fobj.close()
print('Program ended')
exit(0)
def vis(self):
KT.exporttiles(self.weights_hidden, self.layersize[0], 1,
"result/obs_W_1_0.pgm", 1, self.layersize[1])
KT.exporttiles(self.weights_output, self.layersize[1], 1,
"result/obs_W_2_1.pgm", 1, self.layersize[2])