def get_output(self, train=False):
#X=self.get_input(train)
#outball=apply_model(self.nownet,X)
#outball=self.get_input(train)
#self.middle = outball;
X=self.get_input(train)
#self.middle=self.prenet.get_output()
self.middle=apply_model(self.prenet,X)
print 'gaussianmiddle',self.middle
##########Main Function
#Read Data
outmap=[]
a=np.zeros((self.ballnum),dtype="int32")
b=np.zeros((self.ballnum),dtype="int32")
for batch in range(0,self.batchsize):
for i in range(0,self.ballnum):
a[i]=i
b[i]=batch
ballshare=theano.shared(np.array(a).T)
batchshare=theano.shared(np.array(b).T)
results, updates = theano.scan(fn=self._loopoverallball ,
sequences=[ballshare,batchshare])
print 'dim1',results.ndim
results=results.max(axis=[0])
print 'dim2',results.ndim
print results
print 'prepare'
print 'gen'
print 'compute'
#print outball
outmap.append(results)
output=T.reshape(outmap,(self.batchsize,1,self.height,self.width))
return output
def get_output(self, train=False):
X = self.get_input()
batch_size, time_len = X.shape[:2]
X = X.flatten(ndim=2) # (sample*time, dim)
X = K.reshape(X, self.reshape_dim) # (sample*time, dim1, dim2, ...)
Y = apply_model(self.model, X)
Y = K.reshape(Y, (batch_size, time_len, -1)) # (sample, time, dim_out)
return Y
def get_output(self, train=False):
X = self.get_input()
batch_size, time_len = X.shape[:2]
X = X.flatten(ndim=2) # (sample*time, dim)
X = K.reshape(X, self.reshape_dim) # (sample*time, dim1, dim2, ...)
Y = apply_model(self.model, X)
Y = K.reshape(Y, (batch_size, time_len, -1)) # (sample, time, dim_out)
return Y
def test_tensor():
h2o = Dense(4, 2)
model = Tensor(input_dim=3, output_dim=4, causes_dim=2, hid2output=h2o,
return_mode='both')
X = tensor.tensor3()
Y = apply_model(model, X)
F = function([X], Y, allow_input_downcast=True)
x = np.ones((3, 10, 3))
y = F(x)
assert y.shape == (3, 10, 4+2)
def get_output(self,train=False):
outputans=[]
I = self.get_input(train)
XX=apply_model(self.prenet,I)
for batch in range(0,self.batchsize):
X=XX[batch]
for k in range(0,self.ballnum):
outputans.append((X[3*k] /-X[3*k+2]+0.5)*self.width)
outputans.append((-X[3*k+1]/-X[3*k+2]+0.5)*self.height)
outputans=T.reshape(outputans,(self.batchsize,96))
return outputans
def _step(self, x_t, mask_tm1, h_tm1, o_tm1, u_z, u_r, u_h, *args):
xo = T.concatenate([x_t, o_tm1], axis=-1)
xz_t = T.dot(xo, self.W_z) + self.b_z
xr_t = T.dot(xo, self.W_r) + self.b_r
xh_t = T.dot(xo, self.W_h) + self.b_h
z = self.inner_activation(xz_t + T.dot(h_tm1, u_z))
r = self.inner_activation(xr_t + T.dot(h_tm1, u_r))
hh_t = self.activation(xh_t + T.dot(r * h_tm1, u_h))
h_t = z * h_tm1 + (1 - z) * hh_t
o_t = apply_model(self.readout, h_t)
return _masking(h_t, h_tm1, mask_tm1), _masking(o_t, o_tm1, mask_tm1)
def _step(self, x_t, mask_tm1, h_tm1, o_tm1, u_z, u_r, u_h, *args):
xo = T.concatenate([x_t, o_tm1], axis=-1)
xz_t = T.dot(xo, self.W_z) + self.b_z
xr_t = T.dot(xo, self.W_r) + self.b_r
xh_t = T.dot(xo, self.W_h) + self.b_h
z = self.inner_activation(xz_t + T.dot(h_tm1, u_z))
r = self.inner_activation(xr_t + T.dot(h_tm1, u_r))
hh_t = self.activation(xh_t + T.dot(r * h_tm1, u_h))
h_t = z * h_tm1 + (1 - z) * hh_t
o_t = apply_model(self.readout, h_t)
return _masking(h_t, h_tm1, mask_tm1), _masking(o_t, o_tm1, mask_tm1)
def test_tensor():
h2o = Dense(4, 2)
model = Tensor(input_dim=3,
output_dim=4,
causes_dim=2,
hid2output=h2o,
return_mode='both')
X = tensor.tensor3()
Y = apply_model(model, X)
F = function([X], Y, allow_input_downcast=True)
x = np.ones((3, 10, 3))
y = F(x)
assert y.shape == (3, 10, 4 + 2)
def test_apply_model(self):
"""Test keras.models.Sequential.__call__"""
nb_samples, input_dim, output_dim = 3, 10, 5
model = Sequential()
model.add(Dense(output_dim=output_dim, input_dim=input_dim))
model.compile('sgd', 'mse')
X = K.placeholder(ndim=2)
Y = apply_model(model, X)
F = theano.function([X], Y)
x = np.random.randn(nb_samples, input_dim).astype(floatX)
y1 = F(x)
y2 = model.predict(x)
# results of __call__ should match model.predict
assert_allclose(y1, y2)
def test_apply_model(self):
"""Test keras.models.Sequential.__call__"""
nb_samples, input_dim, output_dim = 3, 10, 5
model = Sequential()
model.add(Dense(output_dim=output_dim, input_dim=input_dim))
model.compile('sgd', 'mse')
X = K.placeholder(ndim=2)
Y = apply_model(model, X)
F = theano.function([X], Y)
x = np.random.randn(nb_samples, input_dim).astype(floatX)
y1 = F(x)
y2 = model.predict(x)
# results of __call__ should match model.predict
assert_allclose(y1, y2)
def _get_initial_states(self, batch_size):
init_h = T.repeat(self.init_h.dimshuffle('x', 0), batch_size, axis=0)
init_o = apply_model(self.readout, init_h)
return init_h, init_o
def get_output(self, train=False):
initial=[[27,0,15],
[9,0,10],
[-9,0,10],
[-27,0,10],
[27,0,30],
[9,0,30],
[-9,0,30],
[-27,0,30],
[27,0,50],
[9,0,50],
[-9,0,50],
[-27,0,50],
[28,0,70],
[8,0,70],
[-11,0,70],
[-28,0,70],
[41,0,15],
[45,0,15],
[49,0,15],
[53,0,15],
[67,0,15],
[81,0,15],
[94,0,15],
[105,0,15],
[28,0,88],
[28,0,104],
[28,0,119],
[28,0,133],
[28,0,145],
[28,0,156],
[8,0,90],
[8,0,110],
[8,0,127],
[8,0,141],
[8,0,155],
[8,0,169],
[-11,0,88],
[-11,0,104],
[-11,0,119],
[-11,0,133],
[-11,0,145],
[-11,0,156],
[-28,0,85],
[-28,0,95],
[-28,0,105],
[-28,0,116],
[-28,0,127],
[-28,0,135]]
outputans=[]
XX = self.get_input(train)
#self.middle=self.prenet.get_output()
X=apply_model(self.prenet,XX)
for batch in range(0,self.batchsize):
d=X[batch].reshape((26,)) ##ndim=0
ro=T.dot(T.dot(self._getrx(d[3]),self._getry(d[4])),self._getrz(d[5]))
to1to16=theano.shared(np.zeros((3,1),dtype='float32'))
###o'=(d1,d2,d3)
to1to16=T.set_subtensor(to1to16[0],d[0])
to1to16=T.set_subtensor(to1to16[1],d[1])
to1ti16=T.set_subtensor(to1to16[2],d[2])
o1to16=T.tile(to1ti16,(1,16))
#
xi1to16=theano.shared(np.zeros((16,3),dtype='float32'))
xi1to16=T.dot(ro,np.array(initial[0:16]).T)+o1to16
####################################Thumb First
###17-20
r16=T.dot(self._getry(d[6]),self._getrz(d[7]))
###x16origin rotation joint is sphere 1
txori16=theano.shared(np.zeros((3,1),dtype='float32'))
txori16=T.set_subtensor(txori16[0],initial[0][0])
txori16=T.set_subtensor(txori16[1],initial[0][1])
txori16=T.set_subtensor(txori16[2],initial[0][2])
xori16=T.tile(txori16,(1,4))
to17to20=theano.shared(np.zeros((3,1),dtype='float32'))
to17to20=T.set_subtensor(to17to20[0],xi1to16[0][0])
to17to20=T.set_subtensor(to17to20[1],xi1to16[1][0])
to17to20=T.set_subtensor(to17to20[2],xi1to16[2][0])
o17to20=T.tile(to17to20,(1,4))
xi17to20=T.dot(T.dot(ro,r16),np.array(initial[16:20]).T -xori16)+o17to20
######################################Thumb Second
###21-22
r20=self._getrz(d[8])
txori20=theano.shared(np.zeros((3,1),dtype='float32'))
txori20=T.set_subtensor(txori20[0],initial[19][0])
txori20=T.set_subtensor(txori20[1],initial[19][1])
txori20=T.set_subtensor(txori20[2],initial[19][2])
xori20=T.tile(txori20,(1,2))
to21to22=theano.shared(np.zeros((3,1),dtype='float32'))
to21to22=T.set_subtensor(to21to22[0],xi17to20[0][3])
to21to22=T.set_subtensor(to21to22[1],xi17to20[1][3])
to21to22=T.set_subtensor(to21to22[2],xi17to20[2][3])
o21to22=T.tile(to21to22,(1,2))
xi21to22=T.dot(T.dot(T.dot(ro,r16),r20) ,np.array(initial[20:22]).T -xori20)+o21to22
######################################Thumb Third
###23-24
r22=self._getrz(d[9])
txori22=theano.shared(np.zeros((3,1),dtype='float32'))
txori22=T.set_subtensor(txori22[0],initial[21][0])
txori22=T.set_subtensor(txori22[1],initial[21][1])
txori22=T.set_subtensor(txori22[2],initial[21][2])
xori22=T.tile(txori22,(1,2))
to23to24=theano.shared(np.zeros((3,1),dtype='float32'))
to23to24=T.set_subtensor(to23to24[0],xi21to22[0][1])
to23to24=T.set_subtensor(to23to24[1],xi21to22[1][1])
to23to24=T.set_subtensor(to23to24[2],xi21to22[2][1])
o23to24=T.tile(to23to24,(1,2))
xi23to24=T.dot(T.dot(T.dot(T.dot(ro,r16),r20),r22) ,np.array(initial[22:24]).T -xori22)+o23to24
######################################First Joint Index
###25-26
r13=T.dot(self._getrx(d[10]),self._getry(d[11]))
txori13=theano.shared(np.zeros((3,1),dtype='float32'))
txori13=T.set_subtensor(txori13[0],initial[12][0])
txori13=T.set_subtensor(txori13[1],initial[12][1])
txori13=T.set_subtensor(txori13[2],initial[12][2])
xori13=T.tile(txori13,(1,2))
to25to26=theano.shared(np.zeros((3,1),dtype='float32'))
to25to26=T.set_subtensor(to25to26[0],xi1to16[0][12])
to25to26=T.set_subtensor(to25to26[1],xi1to16[1][12])
to25to26=T.set_subtensor(to25to26[2],xi1to16[2][12])
o25to26=T.tile(to25to26,(1,2))
xi25to26=T.dot(T.dot(ro,r13),np.array(initial[24:26]).T -xori13)+o25to26
######################################First Joint Middle
###31-32
r14=T.dot(self._getrx(d[14]),self._getry(d[15]))
txori14=theano.shared(np.zeros((3,1),dtype='float32'))
txori14=T.set_subtensor(txori14[0],initial[13][0])
txori14=T.set_subtensor(txori14[1],initial[13][1])
txori14=T.set_subtensor(txori14[2],initial[13][2])
xori14=T.tile(txori14,(1,2))
to31to32=theano.shared(np.zeros((3,1),dtype='float32'))
to31to32=T.set_subtensor(to31to32[0],xi1to16[0][13])
to31to32=T.set_subtensor(to31to32[1],xi1to16[1][13])
to31to32=T.set_subtensor(to31to32[2],xi1to16[2][13])
o31to32=T.tile(to31to32,(1,2))
xi31to32=T.dot(T.dot(ro,r14),np.array(initial[30:32]).T -xori14)+o31to32
######################################First Joint Ring
###37-38
r15=T.dot(self._getrx(d[18]),self._getry(d[19]))
txori15=theano.shared(np.zeros((3,1),dtype='float32'))
txori15=T.set_subtensor(txori15[0],initial[14][0])
txori15=T.set_subtensor(txori15[1],initial[14][1])
txori15=T.set_subtensor(txori15[2],initial[14][2])
xori15=T.tile(txori15,(1,2))
to37to38=theano.shared(np.zeros((3,1),dtype='float32'))
to37to38=T.set_subtensor(to37to38[0],xi1to16[0][14])
to37to38=T.set_subtensor(to37to38[1],xi1to16[1][14])
to37to38=T.set_subtensor(to37to38[2],xi1to16[2][14])
o37to38=T.tile(to37to38,(1,2))
xi37to38=T.dot(T.dot(ro,r15),np.array(initial[36:38]).T -xori15)+o37to38
######################################First Joint Little
###43-44
r16=T.dot(self._getrx(d[22]),self._getry(d[23]))
txori16=theano.shared(np.zeros((3,1),dtype='float32'))
txori16=T.set_subtensor(txori16[0],initial[15][0])
txori16=T.set_subtensor(txori16[1],initial[15][1])
txori16=T.set_subtensor(txori16[2],initial[15][2])
xori16=T.tile(txori16,(1,2))
to43to44=theano.shared(np.zeros((3,1),dtype='float32'))
to43to44=T.set_subtensor(to43to44[0],xi1to16[0][15])
to43to44=T.set_subtensor(to43to44[1],xi1to16[1][15])
to43to44=T.set_subtensor(to43to44[2],xi1to16[2][15])
o43to44=T.tile(to43to44,(1,2))
xi43to44=T.dot(T.dot(ro,r16),np.array(initial[42:44]).T -xori16)+o43to44
######################################Second Joint Index
###27-28
r26=self._getrx(d[12])
txori26=theano.shared(np.zeros((3,1),dtype='float32'))
txori26=T.set_subtensor(txori26[0],initial[25][0])
txori26=T.set_subtensor(txori26[1],initial[25][1])
txori26=T.set_subtensor(txori26[2],initial[25][2])
xori26=T.tile(txori26,(1,2))
to27to28=theano.shared(np.zeros((3,1),dtype='float32'))
to27to28=T.set_subtensor(to27to28[0],xi25to26[0][1])
to27to28=T.set_subtensor(to27to28[1],xi25to26[1][1])
to27to28=T.set_subtensor(to27to28[2],xi25to26[2][1])
o27to28=T.tile(to27to28,(1,2))
xi27to28=T.dot(T.dot(T.dot(ro,r13),r26),np.array(initial[26:28]).T -xori26)+o27to28
######################################Second Joint Middle
###33-34
r32=self._getrx(d[16])
txori32=theano.shared(np.zeros((3,1),dtype='float32'))
txori32=T.set_subtensor(txori32[0],initial[31][0])
txori32=T.set_subtensor(txori32[1],initial[31][1])
txori32=T.set_subtensor(txori32[2],initial[31][2])
xori32=T.tile(txori32,(1,2))
to33to34=theano.shared(np.zeros((3,1),dtype='float32'))
to33to34=T.set_subtensor(to33to34[0],xi31to32[0][1])
to33to34=T.set_subtensor(to33to34[1],xi31to32[1][1])
to33to34=T.set_subtensor(to33to34[2],xi31to32[2][1])
o33to34=T.tile(to33to34,(1,2))
xi33to34=T.dot(T.dot(T.dot(ro,r14),r32),np.array(initial[32:34]).T -xori32)+o33to34
######################################Second Joint Ring
###39-40
r38=self._getrx(d[20])
txori38=theano.shared(np.zeros((3,1),dtype='float32'))
txori38=T.set_subtensor(txori38[0],initial[37][0])
txori38=T.set_subtensor(txori38[1],initial[37][1])
txori38=T.set_subtensor(txori38[2],initial[37][2])
xori38=T.tile(txori38,(1,2))
to39to40=theano.shared(np.zeros((3,1),dtype='float32'))
to39to40=T.set_subtensor(to39to40[0],xi37to38[0][1])
to39to40=T.set_subtensor(to39to40[1],xi37to38[1][1])
to39to40=T.set_subtensor(to39to40[2],xi37to38[2][1])
o39to40=T.tile(to39to40,(1,2))
xi39to40=T.dot(T.dot(T.dot(ro,r15),r38),np.array(initial[38:40]).T -xori38)+o39to40
######################################Second Joint Little
###45-46
r44=self._getrx(d[24])
txori44=theano.shared(np.zeros((3,1),dtype='float32'))
txori44=T.set_subtensor(txori44[0],initial[43][0])
txori44=T.set_subtensor(txori44[1],initial[43][1])
txori44=T.set_subtensor(txori44[2],initial[43][2])
xori44=T.tile(txori44,(1,2))
to45to46=theano.shared(np.zeros((3,1),dtype='float32'))
to45to46=T.set_subtensor(to45to46[0],xi43to44[0][1])
to45to46=T.set_subtensor(to45to46[1],xi43to44[1][1])
to45to46=T.set_subtensor(to45to46[2],xi43to44[2][1])
o45to46=T.tile(to45to46,(1,2))
xi45to46=T.dot(T.dot(T.dot(ro,r16),r44),np.array(initial[44:46]).T -xori44)+o45to46
#####################################Third Joint Index
###29-30
r28=self._getrx(d[13])
txori28=theano.shared(np.zeros((3,1),dtype='float32'))
txori28=T.set_subtensor(txori28[0],initial[27][0])
txori28=T.set_subtensor(txori28[1],initial[27][1])
txori28=T.set_subtensor(txori28[2],initial[27][2])
xori28=T.tile(txori28,(1,2))
to29to30=theano.shared(np.zeros((3,1),dtype='float32'))
to29to30=T.set_subtensor(to29to30[0],xi27to28[0][1])
to29to30=T.set_subtensor(to29to30[1],xi27to28[1][1])
to29to30=T.set_subtensor(to29to30[2],xi27to28[2][1])
o29to30=T.tile(to29to30,(1,2))
xi29to30=T.dot(T.dot(T.dot(T.dot(ro,r13),r26),r28),np.array(initial[28:30]).T -xori28)+o29to30
#####################################Third Joint Middle
###35-36
r34=self._getrx(d[17])
txori34=theano.shared(np.zeros((3,1),dtype='float32'))
txori34=T.set_subtensor(txori34[0],initial[33][0])
txori34=T.set_subtensor(txori34[1],initial[33][1])
txori34=T.set_subtensor(txori34[2],initial[33][2])
xori34=T.tile(txori34,(1,2))
to35to36=theano.shared(np.zeros((3,1),dtype='float32'))
to35to36=T.set_subtensor(to35to36[0],xi33to34[0][1])
to35to36=T.set_subtensor(to35to36[1],xi33to34[1][1])
to35to36=T.set_subtensor(to35to36[2],xi33to34[2][1])
o35to36=T.tile(to35to36,(1,2))
xi35to36=T.dot(T.dot(T.dot(T.dot(ro,r14),r32),r34),np.array(initial[34:36]).T -xori34)+o35to36
#####################################Third Joint Ring
###41-42
r40=self._getrx(d[21])
txori40=theano.shared(np.zeros((3,1),dtype='float32'))
txori40=T.set_subtensor(txori40[0],initial[39][0])
txori40=T.set_subtensor(txori40[1],initial[39][1])
txori40=T.set_subtensor(txori40[2],initial[39][2])
xori40=T.tile(txori40,(1,2))
to41to42=theano.shared(np.zeros((3,1),dtype='float32'))
to41to42=T.set_subtensor(to41to42[0],xi39to40[0][1])
to41to42=T.set_subtensor(to41to42[1],xi39to40[1][1])
to41to42=T.set_subtensor(to41to42[2],xi39to40[2][1])
o41to42=T.tile(to41to42,(1,2))
xi41to42=T.dot(T.dot(T.dot(T.dot(ro,r15),r38),r40),np.array(initial[40:42]).T -xori40)+o41to42
#####################################Third Joint Little
###47-48
r46=self._getrx(d[25])
txori46=theano.shared(np.zeros((3,1),dtype='float32'))
txori46=T.set_subtensor(txori46[0],initial[45][0])
txori46=T.set_subtensor(txori46[1],initial[45][1])
txori46=T.set_subtensor(txori46[2],initial[45][2])
xori46=T.tile(txori46,(1,2))
to47to48=theano.shared(np.zeros((3,1),dtype='float32'))
to47to48=T.set_subtensor(to47to48[0],xi45to46[0][1])
to47to48=T.set_subtensor(to47to48[1],xi45to46[1][1])
to47to48=T.set_subtensor(to47to48[2],xi45to46[2][1])
o47to48=T.tile(to47to48,(1,2))
xi47to48=T.dot(T.dot(T.dot(T.dot(ro,r16),r44),r46),np.array(initial[46:48]).T -xori46)+o47to48
ret=T.concatenate([xi1to16 ,xi17to20,xi21to22,xi23to24,xi25to26,xi27to28,xi29to30,xi31to32,xi33to34,xi35to36,xi37to38,xi39to40,
xi41to42,xi43to44,xi45to46,xi47to48],axis=1)
ret=ret.reshape((144,))
outputans.append(ret)
outputans=T.reshape(outputans,(self.batchsize,144))
return outputans
def _get_initial_states(self, batch_size):
init_h = T.repeat(self.init_h.dimshuffle('x', 0), batch_size, axis=0)
init_o = apply_model(self.readout, init_h)
return init_h, init_o
def _step(self, x_t, *args):
x_t = K.reshape(x_t, self.reshape_dim)
return apply_model(self.model, x_t)
def _step(self, x_t, *args):
x_t = K.reshape(x_t, self.reshape_dim)
return apply_model(self.model, x_t)
