def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
m, _ = inpts.shape
hddn = logistic(gpu.dot(inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.m_end+self.shape[1]])
Z = gdot(hddn, params[:self.m_end].reshape(self.shape).T) + params[-self.shape[0]:]
if self.rho_hat_grad == None:
self.rho_hat_grad = hddn.mean(axis=0)
else:
self.rho_hat_grad *= 0.9
self.rho_hat_grad += 0.1*hddn.mean(axis=0)
# rho_hat = hddn.mean(axis=0)
rho_hat = self.rho_hat_grad
rho = self.rho
sparsity = self.beta * gpu.sum(bKL(rho, rho_hat))
_, delta = self.score(Z, inpts, error=True, addon=sparsity)
g[:self.m_end] = gdot(delta.T, hddn).ravel()
g[-self.shape[0]:] = delta.sum(axis=0)
diff = Dsigmoid(hddn)
dsparse_dha = -rho/rho_hat + (1-rho)/(1-rho_hat)
dsc_dha = diff * (gdot(delta, params[:self.m_end].reshape(self.shape)) + self.beta*dsparse_dha/m)
g[:self.m_end] += gdot(inpts.T, dsc_dha).ravel()
g[self.m_end:-self.shape[0]] = dsc_dha.sum(axis=0)
# clean up
del delta, hddn, Z
return g
def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
m, _ = inpts.shape
hddn = logistic(
gpu.dot(inpts, params[:self.m_end].reshape(self.shape)) +
params[self.m_end:self.m_end + self.shape[1]])
Z = gdot(hddn, params[:self.m_end].reshape(
self.shape).T) + params[-self.shape[0]:]
w = params[:self.m_end].reshape(self.shape)
cae = gpu.sum(
gpu.mean(Dsigmoid(hddn)**2, axis=0) * gpu.sum(w**2, axis=0))
cae *= self.cae
_, delta = self.score(Z, inpts, error=True, addon=cae)
g[:self.m_end] = gdot(delta.T, hddn).ravel()
g[-self.shape[0]:] = delta.sum(axis=0)
cae_grad = gpu.mean(Dsigmoid(hddn)**2, axis=0) * w
cae_grad += (gdot(inpts.T, (Dsigmoid(hddn)**2 * (1 - 2 * hddn))) / m *
gpu.sum(w**2, axis=0))
g[:self.m_end] += self.cae * 2 * cae_grad.ravel()
dsc_dha = Dsigmoid(hddn) * gdot(
delta, params[:self.m_end].reshape(self.shape))
g[:self.m_end] += gdot(inpts.T, dsc_dha).ravel()
g[self.m_end:-self.shape[0]] = dsc_dha.sum(axis=0)
# clean up
del delta, hddn, Z
return g
def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
m, _ = inpts.shape
hddn = logistic(gdot(inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.size])
Z = gdot(hddn, params[self.size:-self.shape[0]].reshape(self.Tshape)) + params[-self.shape[0]:]
if self.rho_hat_grad == None:
self.rho_hat_grad = hddn.mean(axis=0)
else:
self.rho_hat_grad *= 0.9
self.rho_hat_grad += 0.1*hddn.mean(axis=0)
# rho_hat = hddn.mean(axis=0)
rho_hat = self.rho_hat_grad
rho = self.rho
sparsity = self.beta * gpu.sum(bKL(rho, rho_hat))
_, delta = self.score(Z, inpts, error=True, addon=sparsity)
g[self.size:-self.shape[0]] = gdot(hddn.T, delta).ravel()
g[-self.shape[0]:] = delta.sum(axis=0)
diff = Dsigmoid(hddn)
dsparse_dha = -rho/rho_hat + (1-rho)/(1-rho_hat)
dsc_dha = diff * (gdot(delta, params[:self.m_end].reshape(self.shape)) + self.beta*dsparse_dha/m)
g[:self.m_end] = gdot(inpts.T, dsc_dha).ravel()
g[self.m_end:self.size] = dsc_dha.sum(axis=0)
# clean up
del delta, hddn, Z
return g
def pt_score(self, params, inpts, **kwargs):
hddn = self.activ(gdot(inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.size])
Z = gdot(hddn, params[self.size:-self.shape[0]].reshape(self.Tshape)) + params[-self.shape[0]:]
sc = self.score(Z, inpts)
return sc
def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
hddn = self.activ(
gpu.dot(inpts, params[: self.m_end].reshape(self.shape)) + params[self.m_end : self.m_end + self.shape[1]]
)
_hddn = hddn.as_numpy_array()
idxs = np.argsort(_hddn, axis=1)
_hddn[range(_hddn.shape[0]), idxs[:, self.ak :].T] = 0
hddn = gpu.garray(_hddn)
Z = gdot(hddn, params[: self.m_end].reshape(self.shape).T) + params[-self.shape[0] :]
_, delta = self.score(Z, inpts, error=True)
g[: self.m_end] = gdot(delta.T, hddn).ravel()
g[-self.shape[0] :] = delta.sum(axis=0)
dsc_dha = gdot(delta, params[: self.m_end].reshape(self.shape)) * diff_table[self.activ](hddn)
g[: self.m_end] += gdot(inpts.T, dsc_dha).ravel()
g[self.m_end : -self.shape[0]] = dsc_dha.sum(axis=0)
# clean up
del delta
return g
def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
m, _ = inpts.shape
hddn = logistic(
gpu.dot(inpts, params[: self.m_end].reshape(self.shape)) + params[self.m_end : self.m_end + self.shape[1]]
)
Z = gdot(hddn, params[: self.m_end].reshape(self.shape).T) + params[-self.shape[0] :]
w = params[: self.m_end].reshape(self.shape)
cae = gpu.sum(gpu.mean(Dsigmoid(hddn) ** 2, axis=0) * gpu.sum(w ** 2, axis=0))
cae *= self.cae
_, delta = self.score(Z, inpts, error=True, addon=cae)
g[: self.m_end] = gdot(delta.T, hddn).ravel()
g[-self.shape[0] :] = delta.sum(axis=0)
cae_grad = gpu.mean(Dsigmoid(hddn) ** 2, axis=0) * w
cae_grad += gdot(inpts.T, (Dsigmoid(hddn) ** 2 * (1 - 2 * hddn))) / m * gpu.sum(w ** 2, axis=0)
g[: self.m_end] += self.cae * 2 * cae_grad.ravel()
dsc_dha = Dsigmoid(hddn) * gdot(delta, params[: self.m_end].reshape(self.shape))
g[: self.m_end] += gdot(inpts.T, dsc_dha).ravel()
g[self.m_end : -self.shape[0]] = dsc_dha.sum(axis=0)
# clean up
del delta, hddn, Z
return g
def bprop(self, params, grad, delta):
dE_da = delta * diff_table[self.activ](self.Z)
# gradient of the bias
grad[self.m_end:] = dE_da.sum(axis=0)
# gradient of the weights
grad[:self.m_end] = gdot(self.data.T, dE_da).ravel()
# backpropagate the delta
delta = gdot(dE_da, params[:self.m_end].reshape(self.shape).T)
del self.Z
return delta
def grad_cd1(self, params, inputs, **kwargs):
"""
"""
g = gzeros(params.shape)
n, _ = inputs.shape
m_end = self.m_end
V = self.shape[0]
H = self.shape[1]
wm = params[:m_end].reshape(self.shape)
prec = params[-V:][:, gpu.newaxis]
h1, h_sampled = self.H(inputs, wm=prec*wm, bias=params[m_end:m_end+H], sampling=True)
v2, v_sampled = gauss(h_sampled, wm=(wm/prec).T, bias=params[-(2*V):-V], prec=prec.T, sampling=True)
h2, _ = self.H(v2, wm=prec*wm, bias=params[m_end:m_end+H])
#print h1[0,0], h_sampled[0,0], v2[0,0], v_sampled[0,0]
# Note the negative sign: the gradient is
# supposed to point into 'wrong' direction.
g[:m_end] = -gdot(inputs.T*prec, h1).ravel()
g[:m_end] += gdot(v_sampled.T*prec, h2).ravel()
g[:m_end] *= 1./n
g[:m_end] += self.l2*params[:m_end]
g[m_end:m_end+H] = -h1.sum(axis=0)
g[m_end:m_end+H] += h2.sum(axis=0)
g[m_end:m_end+H] *= 1./n
g[-2*V:-V] = -inputs.sum(axis=0)
g[-2*V:-V] += v_sampled.sum(axis=0)
g[-2*V:-V] *= 1./n
g[-2*V:-V] *= (prec**2).T
#print gsum(g[:m_end]**2), gsum(g[m_end:m_end+H]**2), gsum(g[-2*V:-V]**2)
# Gradient for square root of precision
g[-V:] = -gsum(2*prec.T*inputs*(params[-2*V:-V] - inputs/2), axis=0) + gsum(gdot(inputs.T, h1)*wm, axis=1)
g[-V:] += (gsum(2*prec.T*v_sampled*(params[-2*V:-V] - v_sampled/2), axis=0) + gsum(gdot(v_sampled.T, h2)*wm, axis=1))
g[-V:] *= 1./n
#print gsum(g[-V:]**2)
if self.lmbd > 0.:
if self.rho_hat is None:
self.rho_hat = h1.mean(axis=0)
else:
self.rho_hat *= 0.9
self.rho_hat += 0.1 * h1.mean(axis=0)
dKL_drho_hat = (self.rho - self.rho_hat)/(self.rho_hat*(1-self.rho_hat))
h1_1mh1 = h1*(1 - h1)
g[m_end:m_end+H] -= self.lmbd/n * gsum(h1_1mh1, axis=0) * dKL_drho_hat
g[:m_end] -= self.lmbd/n * (gdot(inputs.T * prec, h1_1mh1) * dKL_drho_hat).ravel()
#g[:] = -g[:]
return g
def pt_score(self, params, inpts, **kwargs):
hddn = logistic(gdot(inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.size])
Z = gdot(hddn, params[self.size:-self.shape[0]].reshape(self.Tshape)) + params[-self.shape[0]:]
if self.rho_hat == None:
self.rho_hat = hddn.mean(axis=0)
else:
self.rho_hat *= 0.9
self.rho_hat += 0.1*hddn.mean(axis=0)
sparsity = self.beta * gpu.sum(bKL(self.rho, self.rho_hat))
sc = self.score(Z, inpts, addon=sparsity)
return sc
def bprop(self, params, grad, delta):
# TODO: check next line, is it according
# to formula in the paper? delta must be
# defined correctly!!
# self.C necessary? in loss Function, there is no C
dE_da = self.C * delta * diff_table[self.activ](self.Z)
# gradient of the bias
grad[self.m_end:] = dE_da.sum(axis=0)
# gradient of the weights, takes care of weight 'decay' factor (second addend)
grad[:self.m_end] = gdot(self.data.T, dE_da).ravel() + params[:self.m_end]
# backpropagate the delta
delta = gdot(dE_da, params[:self.m_end].reshape(self.shape).T)
del self.Z
return delta
def bprop(self, params, grad, delta):
# TODO: check next line, is it according
# to formula in the paper? delta must be
# defined correctly!!
# self.C necessary? in loss Function, there is no C
dE_da = self.C * delta * diff_table[self.activ](self.Z)
# gradient of the bias
grad[self.m_end:] = dE_da.sum(axis=0)
# gradient of the weights, takes care of weight 'decay' factor (second addend)
grad[:self.m_end] = gdot(self.data.T,
dE_da).ravel() + params[:self.m_end]
# backpropagate the delta
delta = gdot(dE_da, params[:self.m_end].reshape(self.shape).T)
del self.Z
return delta
def pt_score(self, params, inpts, **kwargs):
# fprop in tied AE
hddn = self.activ(gpu.dot(inpts, params[:self.m_end].reshape(self.shape)), self.theta)
# get indices
Z = gdot(hddn, params[:self.m_end].reshape(self.shape).T) + params[-self.shape[0]:]
sc = self.score(Z, inpts)
return sc
def pt_score(self, params, inpts, **kwargs):
hddn = logistic(
gdot(inpts, params[:self.m_end].reshape(self.shape)) +
params[self.m_end:self.size])
Z = gdot(hddn, params[self.size:-self.shape[0]].reshape(
self.Tshape)) + params[-self.shape[0]:]
if self.rho_hat == None:
self.rho_hat = hddn.mean(axis=0)
else:
self.rho_hat *= 0.9
self.rho_hat += 0.1 * hddn.mean(axis=0)
sparsity = self.beta * gpu.sum(bKL(self.rho, self.rho_hat))
sc = self.score(Z, inpts, addon=sparsity)
return sc
def grad_cd1(self, params, inputs, **kwargs):
"""
"""
g = gzeros(params.shape)
n, _ = inputs.shape
m_end = self.m_end
V = self.shape[0]
H = self.shape[1]
wm = params[:m_end].reshape(self.shape)
h1, h_sampled = self.H(inputs,
wm=wm,
bias=params[m_end:-V],
sampling=True)
v2, _ = self.V(h_sampled, wm=wm.T, bias=params[-V:])
h2, _ = self.H(v2, wm=wm, bias=params[m_end:-V])
# Note the negative sign: the gradient is
# supposed to point into 'wrong' direction,
# because the used optimizer likes to minimize.
g[:m_end] = -gdot(inputs.T, h1).ravel()
g[:m_end] += gdot(v2.T, h2).ravel()
g[:m_end] *= 1. / n
g[:m_end] += self.l2 * params[:m_end]
g[m_end:-V] = -h1.mean(axis=0)
g[m_end:-V] += h2.mean(axis=0)
g[-V:] = -inputs.mean(axis=0)
g[-V:] += v2.mean(axis=0)
if self.rho_hat is None:
self.rho_hat = h1.mean(axis=0)
else:
self.rho_hat *= 0.9
self.rho_hat += 0.1 * h1.mean(axis=0)
dKL_drho_hat = (self.rho - self.rho_hat) / (self.rho_hat *
(1 - self.rho_hat))
h1_1mh1 = h1 * (1 - h1)
g[m_end:-V] -= self.lmbd / n * gsum(h1_1mh1, axis=0) * dKL_drho_hat
g[:m_end] -= self.lmbd / n * (gdot(inputs.T, h1_1mh1) *
dKL_drho_hat).ravel()
return g
def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
hddn = self.activ(gpu.dot(inpts, params[:self.m_end].reshape(self.shape)), self.theta)
Z = gdot(hddn, params[:self.m_end].reshape(self.shape).T) + params[-self.shape[0]:]
_, delta = self.score(Z, inpts, error=True)
g[:self.m_end] = gdot(delta.T, hddn).ravel()
g[-self.shape[0]:] = delta.sum(axis=0)
dsc_dha = gdot(delta, params[:self.m_end].reshape(self.shape)) * diff_table[self.activ](hddn)
g[:self.m_end] += gdot(inpts.T, dsc_dha).ravel()
# clean up
del delta
return g
def reconstruction(self, params, inputs, **kwargs):
"""
"""
x, y = inputs
n, _ = x.shape
weights_xf = params[:self.xf_sz].reshape(self.xfshape)
weights_yf = params[self.xf_sz:self._cum_xy].reshape(self.yfshape)
weights_fh = params[self._cum_xy:self._cum_xyh].reshape(self.fhshape)
bias_h = params[self._cum_xyh:self.size]
bias_x = params[self.size:-self.shape[0][1]]
bias_y = params[-self.shape[0][1]:]
factors_x = gdot(x, weights_xf)
factors_y = gdot(y, weights_yf)
factors = factors_x * factors_y
h, h_sampled = bernoulli(factors, wm=weights_fh, bias=bias_h, sampling=True)
rho_hat = h.sum()
factors_h = gdot(h, weights_fh.T)
way = np.random.rand() > 0.5
if way:
# reconstruct y (output) first.
tmp = factors_x * factors_h
y1, _ = self.V(tmp, wm=weights_yf.T, bias=bias_y)
factors_y[:] = gdot(y1, weights_yf)
# then reconstruct x (input).
tmp = factors_y * factors_h
x1, _ = self.V(tmp, wm=weights_xf.T, bias=bias_x)
else:
# reconstruct x (input) first.
tmp = factors_y * factors_h
x1, _ = self.V(tmp, wm=weights_xf.T, bias=bias_x)
factors_x[:] = gdot(x1, weights_xf)
# then reconstruct y (output).
tmp = factors_x * factors_h
y1, _ = self.V(tmp, wm=weights_yf.T, bias=bias_y)
xrec = gsum((x - x1)**2)
yrec = gsum((y - y1)**2)
return np.array([xrec, yrec, self.lmbd*rho_hat, self.avg_nxyf, self.avg_nfh])
def pt_grad(self, params, noisy_inpts, targets, l2=0., **kwargs):
g = gzeros(params.shape)
hddn = self.activ(gpu.dot(noisy_inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.m_end+self.shape[1]])
Z = gdot(hddn, params[:self.m_end].reshape(self.shape).T) + params[-self.shape[0]:]
_, delta = self.score(Z, targets, error=True)
g[:self.m_end] = gdot(delta.T, hddn).ravel()
g[-self.shape[0]:] = delta.sum(axis=0)
dsc_dha = gdot(delta, params[:self.m_end].reshape(self.shape)) * diff_table[self.activ](hddn)
g[:self.m_end] += gdot(noisy_inpts.T, dsc_dha).ravel()
g[self.m_end:-self.shape[0]] = dsc_dha.sum(axis=0)
# clean up
del delta
return g
def pt_score(self, params, inpts, **kwargs):
# fprop in tied AE
hddn = self.activ(
gpu.dot(inpts, params[:self.m_end].reshape(self.shape)),
self.theta)
# get indices
Z = gdot(hddn, params[:self.m_end].reshape(
self.shape).T) + params[-self.shape[0]:]
sc = self.score(Z, inpts)
return sc
def pt_grad(self, params, inputs, targets, l2=0, **kwargs):
g = gzeros(params.shape)
Z = self.activ(gpu.dot(inputs, params[:self.m_end].reshape(self.shape)) + params[self.m_end:])
_, delta = self.score(Z, targets, error=True)
# necessary?
delta = self.C * delta
g[:self.m_end] = gdot(inputs.T, delta).ravel() + params[:self.m_end]
g[self.m_end:] = delta.sum(axis=0)
# clean up
del delta
return g
def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
m, _ = inpts.shape
hddn = self.activ(gdot(inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.size])
Z = gdot(hddn, params[self.size:-self.shape[0]].reshape(self.Tshape)) + params[-self.shape[0]:]
_, delta = self.score(Z, inpts, error=True)
g[self.end:-self.shape[0]] = gdot(hddn.T, delta).ravel()
g[-self.shape[0]:] = delta.sum(axis=0)
diff = diff_table[self.activ](hddn)
dsc_dha = diff * gdot(delta, params[:self.m_end].reshape(self.shape))
g[:self.m_end] = gdot(inpts.T, dsc_dha).ravel()
g[self.m_end:self.size] = dsc_dha.sum(axis=0)
# clean up
del delta, hddn, Z
return g
def grad_cd1(self, params, inputs, **kwargs):
"""
"""
g = gzeros(params.shape)
n, _ = inputs.shape
m_end = self.m_end
V = self.shape[0]
H = self.shape[1]
wm = params[:m_end].reshape(self.shape)
h1, h_sampled = self.H(inputs, wm=wm, bias=params[m_end:-V], sampling=True)
v2, _ = self.V(h_sampled, wm=wm.T, bias=params[-V:])
h2, _ = self.H(v2, wm=wm, bias=params[m_end:-V])
# Note the negative sign: the gradient is
# supposed to point into 'wrong' direction,
# because the used optimizer likes to minimize.
g[:m_end] = -gdot(inputs.T, h1).ravel()
g[:m_end] += gdot(v2.T, h2).ravel()
g[:m_end] *= 1./n
g[:m_end] += self.l2*params[:m_end]
g[m_end:-V] = -h1.mean(axis=0)
g[m_end:-V] += h2.mean(axis=0)
g[-V:] = -inputs.mean(axis=0)
g[-V:] += v2.mean(axis=0)
if self.rho_hat is None:
self.rho_hat = h1.mean(axis=0)
else:
self.rho_hat *= 0.9
self.rho_hat += 0.1 * h1.mean(axis=0)
dKL_drho_hat = (self.rho - self.rho_hat)/(self.rho_hat*(1-self.rho_hat))
h1_1mh1 = h1*(1 - h1)
g[m_end:-V] -= self.lmbd/n * gsum(h1_1mh1, axis=0) * dKL_drho_hat
g[:m_end] -= self.lmbd/n * (gdot(inputs.T, h1_1mh1) * dKL_drho_hat).ravel()
return g
def pt_score(self, params, inpts, **kwargs):
# fprop in tied AE
hddn = self.activ(gpu.dot(inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.m_end+self.shape[1]])
# get indices
_hddn= hddn.as_numpy_array()
idxs = np.argsort(_hddn, axis=1)
_hddn[range(_hddn.shape[0]), idxs[:, self.ak:].T] = 0
hddn = gpu.garray(_hddn)
Z = gdot(hddn, params[:self.m_end].reshape(self.shape).T) + params[-self.shape[0]:]
sc = self.score(Z, inpts)
return sc
def pt_grad(self, params, inputs, targets, l2=0, **kwargs):
g = gzeros(params.shape)
Z = self.activ(gpu.dot(inputs, params[:self.m_end].reshape(self.shape)) + params[self.m_end:])
_, delta = self.score(Z, targets, error=True)
g[:self.m_end] = gdot(inputs.T, delta).ravel()
g[self.m_end:] = delta.sum(axis=0)
# clean up
del delta
return g
def pt_score(self, params, inpts, **kwargs):
hddn = logistic(
gpu.dot(inpts, params[: self.m_end].reshape(self.shape)) + params[self.m_end : self.m_end + self.shape[1]]
)
Z = gdot(hddn, params[: self.m_end].reshape(self.shape).T) + params[-self.shape[0] :]
w = params[: self.m_end].reshape(self.shape)
cae = gpu.sum(gpu.mean(Dsigmoid(hddn) ** 2, axis=0) * gpu.sum(w ** 2, axis=0))
cae *= self.cae
sc = self.score(Z, inpts, addon=cae)
return np.array([sc, cae])
def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
hddn = self.activ(
gpu.dot(inpts, params[:self.m_end].reshape(self.shape)),
self.theta)
Z = gdot(hddn, params[:self.m_end].reshape(
self.shape).T) + params[-self.shape[0]:]
_, delta = self.score(Z, inpts, error=True)
g[:self.m_end] = gdot(delta.T, hddn).ravel()
g[-self.shape[0]:] = delta.sum(axis=0)
dsc_dha = gdot(delta, params[:self.m_end].reshape(
self.shape)) * diff_table[self.activ](hddn)
g[:self.m_end] += gdot(inpts.T, dsc_dha).ravel()
# clean up
del delta
return g
def pt_score(self, params, inpts, **kwargs):
# fprop in tied AE
hddn = self.activ(
gpu.dot(inpts, params[: self.m_end].reshape(self.shape)) + params[self.m_end : self.m_end + self.shape[1]]
)
# get indices
_hddn = hddn.as_numpy_array()
idxs = np.argsort(_hddn, axis=1)
_hddn[range(_hddn.shape[0]), idxs[:, self.ak :].T] = 0
hddn = gpu.garray(_hddn)
Z = gdot(hddn, params[: self.m_end].reshape(self.shape).T) + params[-self.shape[0] :]
sc = self.score(Z, inpts)
return sc
def pt_score(self, params, inpts, **kwargs):
hddn = logistic(gpu.dot(inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.m_end+self.shape[1]])
Z = gdot(hddn, params[:self.m_end].reshape(self.shape).T) + params[-self.shape[0]:]
if self.rho_hat == None:
self.rho_hat = hddn.mean(axis=0)
else:
self.rho_hat *= 0.9
self.rho_hat += 0.1*hddn.mean(axis=0)
sparsity = self.beta * gpu.sum(bKL(self.rho, self.rho_hat))
sc = self.score(Z, inpts, addon=sparsity)
return np.array([sc, sc-sparsity, sparsity, gpu.mean(self.rho_hat)])
def pt_grad(self, params, inpts, **kwargs):
g = gzeros(params.shape)
hddn = self.activ(gpu.dot(inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.m_end+self.shape[1]])
_hddn= hddn.as_numpy_array()
idxs = np.argsort(_hddn, axis=1)
_hddn[range(_hddn.shape[0]), idxs[:, self.ak:].T] = 0
hddn = gpu.garray(_hddn)
Z = gdot(hddn, params[:self.m_end].reshape(self.shape).T) + params[-self.shape[0]:]
_, delta = self.score(Z, inpts, error=True)
g[:self.m_end] = gdot(delta.T, hddn).ravel()
g[-self.shape[0]:] = delta.sum(axis=0)
dsc_dha = gdot(delta, params[:self.m_end].reshape(self.shape)) * diff_table[self.activ](hddn)
g[:self.m_end] += gdot(inpts.T, dsc_dha).ravel()
g[self.m_end:-self.shape[0]] = dsc_dha.sum(axis=0)
# clean up
del delta
return g
def pt_score(self, params, inpts, **kwargs):
hddn = logistic(
gpu.dot(inpts, params[:self.m_end].reshape(self.shape)) +
params[self.m_end:self.m_end + self.shape[1]])
Z = gdot(hddn, params[:self.m_end].reshape(
self.shape).T) + params[-self.shape[0]:]
w = params[:self.m_end].reshape(self.shape)
cae = gpu.sum(
gpu.mean(Dsigmoid(hddn)**2, axis=0) * gpu.sum(w**2, axis=0))
cae *= self.cae
sc = self.score(Z, inpts, addon=cae)
return np.array([sc, cae])
def cd1_3way_grad(self, params, inputs, **kwargs):
"""
"""
g = gzeros(params.shape)
x, y = inputs
n, _ = x.shape
#print self.avg_nxyf, self.avg_nfh
weights_xf = params[:self.xf_sz].reshape(self.xfshape)
weights_yf = params[self.xf_sz:self._cum_xy].reshape(self.yfshape)
weights_fh = params[self._cum_xy:self._cum_xyh].reshape(self.fhshape)
bias_h = params[self._cum_xyh:self.size]
bias_x = params[self.size:-self.shape[0][1]]
bias_y = params[-self.shape[0][1]:]
# normalize weights
sq_xf = weights_xf * weights_xf
norm_xf = gpu.sqrt(sq_xf.sum(axis=0)) + SMALL
sq_yf = weights_yf * weights_yf
norm_yf = gpu.sqrt(sq_yf.sum(axis=0)) + SMALL
norm_xyf = (norm_xf.mean() + norm_yf.mean())/2.
self.avg_nxyf *= 0.95
self.avg_nxyf += (0.05 * norm_xyf)
weights_xf *= (self.avg_nxyf / norm_xf)
weights_yf *= (self.avg_nxyf / norm_yf)
sq_fh = weights_fh*weights_fh
norm_fh = gpu.sqrt(sq_fh.sum(axis=1)) + SMALL
self.avg_nfh *= 0.95
self.avg_nfh += (0.05 * norm_fh.mean())
weights_fh *= (self.avg_nfh / norm_fh[:, gpu.newaxis])
# normalization done
factors_x = gdot(x, weights_xf)
factors_y = gdot(y, weights_yf)
factors = factors_x * factors_y
h, h_sampled = bernoulli(factors, wm=weights_fh, bias=bias_h, sampling=True)
factors_h = gdot(h_sampled, weights_fh.T)
g[:self.xf_sz] = -gdot(x.T, factors_y*factors_h).ravel()
g[self.xf_sz:self._cum_xy] = -gdot(y.T, factors_x*factors_h).ravel()
g[self._cum_xy:self._cum_xyh] = -gdot(factors.T, h_sampled).ravel()
g[self._cum_xyh:self.size] = -h.sum(axis=0)
g[self.size:-self.shape[0][1]] = -x.sum(axis=0)
g[-self.shape[0][1]:] = -y.sum(axis=0)
# 3way cd
way = np.random.rand() > 0.5
if way:
# reconstruct y (output) first.
tmp = factors_x * factors_h
y1, _ = self.V(tmp, wm=weights_yf.T, bias=bias_y)
factors_y[:] = gdot(y1, weights_yf)
# then reconstruct x (input).
tmp = factors_y * factors_h
x1, _ = self.V(tmp, wm=weights_xf.T, bias=bias_x)
factors_x[:] = gdot(x1, weights_xf)
else:
# reconstruct x (input) first.
tmp = factors_y * factors_h
x1, _ = self.V(tmp, wm=weights_xf.T, bias=bias_x)
factors_x[:] = gdot(x1, weights_xf)
# then reconstruct y (output).
tmp = factors_x * factors_h
y1, _ = self.V(tmp, wm=weights_yf.T, bias=bias_y)
factors_y[:] = gdot(y1, weights_yf)
factors[:] = factors_x * factors_y
h1, _ = bernoulli(factors, wm=weights_fh, bias=bias_h)
factors_h[:] = gdot(h1, weights_fh.T)
g[:self.xf_sz] += gdot(x1.T, factors_y*factors_h).ravel()
g[:self.xf_sz] *= 1./n
g[self.xf_sz:self._cum_xy] += gdot(y1.T, factors_x*factors_h).ravel()
g[self.xf_sz:self._cum_xy] *= 1./n
g[self._cum_xy:self._cum_xyh] += gdot(factors.T, h1).ravel()
g[self._cum_xy:self._cum_xyh] *= 1./n
g[self._cum_xyh:self.size] += h1.sum(axis=0)
g[self._cum_xyh:self.size] *= 1./n
g[self.size:-self.shape[0][1]] += x1.sum(axis=0)
g[self.size:-self.shape[0][1]] *= 1./n
g[-self.shape[0][1]:] += y1.sum(axis=0)
g[-self.shape[0][1]:] *= 1./n
return g
def fward(self, params, data):
return gdot(data, params[:self.m_end].reshape(
self.shape)) + params[self.m_end:]
def fprop(self, params, data):
self.data = data
self.Z = gdot(data, params[:self.m_end].reshape(self.shape)) + params[self.m_end:]
return self.Z
def fward(self, params, data):
return gdot(data, params[:self.m_end].reshape(self.shape)) + params[self.m_end:]
def fprop_spike(self, params, data):
self.data = data
self.Z = self.activ(gdot(data, params[:self.m_end].reshape(self.shape)) + params[self.m_end:])
spike = self.Z > gpu.rand(self.Z.shape)
return spike
def fprop(self, params, data):
self.data = data
self.Z = gdot(data, params[:self.m_end].reshape(
self.shape)) + params[self.m_end:]
return self.Z
def grad_cd1(self, params, inputs, **kwargs):
"""
"""
g = gzeros(params.shape)
n, _ = inputs.shape
m_end = self.m_end
V = self.shape[0]
H = self.shape[1]
wm = params[:m_end].reshape(self.shape)
prec = params[-V:][:, gpu.newaxis]
h1, h_sampled = self.H(inputs,
wm=prec * wm,
bias=params[m_end:m_end + H],
sampling=True)
v2, v_sampled = gauss(h_sampled,
wm=(wm / prec).T,
bias=params[-(2 * V):-V],
prec=prec.T,
sampling=True)
h2, _ = self.H(v2, wm=prec * wm, bias=params[m_end:m_end + H])
#print h1[0,0], h_sampled[0,0], v2[0,0], v_sampled[0,0]
# Note the negative sign: the gradient is
# supposed to point into 'wrong' direction.
g[:m_end] = -gdot(inputs.T * prec, h1).ravel()
g[:m_end] += gdot(v_sampled.T * prec, h2).ravel()
g[:m_end] *= 1. / n
g[:m_end] += self.l2 * params[:m_end]
g[m_end:m_end + H] = -h1.sum(axis=0)
g[m_end:m_end + H] += h2.sum(axis=0)
g[m_end:m_end + H] *= 1. / n
g[-2 * V:-V] = -inputs.sum(axis=0)
g[-2 * V:-V] += v_sampled.sum(axis=0)
g[-2 * V:-V] *= 1. / n
g[-2 * V:-V] *= (prec**2).T
#print gsum(g[:m_end]**2), gsum(g[m_end:m_end+H]**2), gsum(g[-2*V:-V]**2)
# Gradient for square root of precision
g[-V:] = -gsum(2 * prec.T * inputs * (params[-2 * V:-V] - inputs / 2),
axis=0) + gsum(gdot(inputs.T, h1) * wm, axis=1)
g[-V:] += (gsum(2 * prec.T * v_sampled *
(params[-2 * V:-V] - v_sampled / 2),
axis=0) + gsum(gdot(v_sampled.T, h2) * wm, axis=1))
g[-V:] *= 1. / n
#print gsum(g[-V:]**2)
if self.lmbd > 0.:
if self.rho_hat is None:
self.rho_hat = h1.mean(axis=0)
else:
self.rho_hat *= 0.9
self.rho_hat += 0.1 * h1.mean(axis=0)
dKL_drho_hat = (self.rho - self.rho_hat) / (self.rho_hat *
(1 - self.rho_hat))
h1_1mh1 = h1 * (1 - h1)
g[m_end:m_end +
H] -= self.lmbd / n * gsum(h1_1mh1, axis=0) * dKL_drho_hat
g[:m_end] -= self.lmbd / n * (gdot(inputs.T * prec, h1_1mh1) *
dKL_drho_hat).ravel()
#g[:] = -g[:]
return g
def fprop_dropout(self, params, data):
self.data = data
self.Z = self.activ(gdot(data, params[:self.m_end].reshape(self.shape)) + params[self.m_end:])
self.drop = gpu.rand(self.Z.shape) > self.dropout
self.Z *= self.drop
return self.Z
def fprop(self, params, data):
self.data = data
self.Z = self.activ(gdot(data, params[:self.m_end].reshape(self.shape))\
+ params[self.m_end:])
return self.Z
def fward_spike(self, params, data):
Z = self.activ(gdot(data, params[:self.m_end].reshape(self.shape))\
+ params[self.m_end:])
spike = Z > gpu.rand(Z.shape)
return spike
def fward_dropout(self, params, data):
return (1 - self.dropout) * self.activ(gdot(data,\
params[:self.m_end].reshape(self.shape)) + params[self.m_end:])
def fward(self, params, data):
return self.activ(gdot(data, params[:self.m_end].reshape(self.shape))\
+ params[self.m_end:])
def pt_score(self, params, noisy_inpts, targets, l2=0., **kwargs):
# fprop in tied AE
hddn = self.activ(gpu.dot(noisy_inpts, params[:self.m_end].reshape(self.shape)) + params[self.m_end:self.m_end+self.shape[1]])
Z = gdot(hddn, params[:self.m_end].reshape(self.shape).T) + params[-self.shape[0]:]
sc = self.score(Z, targets)
return sc