def train(self, X):
n_samples, n_features = X.shape
for i in range(self.numepochs):
kk = np.random.permutation(m)
err = 0
for l in gen_even_slices(n_samples, self.batchsize):
batch = x[l, :]
v1 = batch # n_samples X n_visible
h1 = sigmrnd(np.dot(v1, self.W.T) + self.c) # n_samples X n_hidden
v2 = sigmrnd(np.dot(h1, self.W) + self.b) # n_samples X n_visible
h2 = sigm(np.dot(v2, self.W.T) + self.c) # n_samples X n_hidden
c1 = np.dot(h1.T, v1) # n_hidden X n_visible
c2 = np.dot(h2.T, v2) # n_hidden X n_visible
self.vW = self.momentum * self.vW + self.alpha * (c1 - c2) / self.batchsize # n_hidden X n_visible
self.vb = self.momentum * self.vb + self.alpha * np.sum(v1 - v2, axis=0) / self.batchsize # n_visible X 1
self.vc = self.momentum * self.vc + self.alpha * np.sum(h1 - h2, axis=0) / self.batchsize # n_hidden X 1
self.W = self.W + self.vW # n_hidden X n_visible
self.b = self.b + self.vb # n_visible X 1
self.c = self.c + self.vc # n_visible X 1
err = err + np.sum(np.power(v1 - v2), 2) / self.batchsize
print 'epoch '+ str(i) + '/' + str(self.numepochs) + '. Average reconstruction error is: ' + str(err / numbatches)
def get_cost_monitor(self):
if not hasattr(self, 'cost_monitor'):
bit_i_idx = theano.shared(value=0, name='bit_i_idx')
xi = T.round(self.input)
fe_xi = self.free_energy(xi)
xi_flip = T.set_subtensor(xi[:, bit_i_idx], 1 - xi[:, bit_i_idx])
fe_xi_flip = self.free_energy(xi_flip)
cost = T.mean(self.n_vis * T.log(sigm(fe_xi_flip - fe_xi)))
self.cost_monitor = theano.function(
inputs=[self.input],
outputs=cost,
updates={bit_i_idx: (bit_i_idx + 1) % self.n_vis},
name='monitoring_function')
return self.cost_monitor
def predict(self, input):
L = np.shape(input)[0]
az = np.zeros((L, self.Nhidden))
ar = np.zeros((L, self.Nhidden))
ahhat = np.zeros((L, self.Nhidden))
ah = np.zeros((L, self.Nhidden))
a1 = tanh(np.dot(input, self.w1) + self.b1)
x = np.concatenate((np.zeros((self.Nhidden)), a1[1, :]))
az[1, :] = sigm(np.dot(x, self.wz) + self.bz)
ar[1, :] = sigm(np.dot(x, self.wr) + self.br)
ahhat[1, :] = tanh(np.dot(x, self.wh) + self.bh)
ah[1, :] = az[1, :] * ahhat[1, :]
for i in range(1, L):
x = np.concatenate((ah[i - 1, :], a1[i, :]))
az[i, :] = sigm(np.dot(x, self.wz) + self.bz)
ar[i, :] = sigm(np.dot(x, self.wr) + self.br)
x = np.concatenate((ar[i, :] * ah[i - 1, :], a1[i, :]))
ahhat[i, :] = tanh(np.dot(x, self.wh) + self.bh)
ah[i, :] = (1 - az[i, :]) * ah[i - 1, :] + az[i, :] * ahhat[i, :]
a2 = tanh(np.dot(ah, self.w2) + self.b2)
return [a1, az, ar, ahhat, ah, a2]
def gibbs_step(v, binomial=False):
mean_h = sigm(T.dot(v, W) + bh)
h = rng.binomial(size=mean_h.shape, n=1, p=mean_h, dtype=dtype)
mean_v = sigm(T.dot(h, W.T) + bv)
v = rng.binomial(size=mean_v.shape, n=1, p=mean_v, dtype=theano.config.floatX) if binomial else mean_v
return mean_v, v
def hidden_to_visible(self, h_sample):
rw = T.tensordot(self.R, self.W, axes=[2, 0])
return sigm(
T.tensordot(h_sample, rw, axes=[[1, 2], [1, 2]]) + self.vbias)
def hidden_to_visible(self, h_sample):
wx_b = T.dot(h_sample, self.W.T) + self.vbias
return sigm(wx_b)
def visible_to_hidden(self, v_sample):
wx_b = T.dot(v_sample, self.W) + self.hbias
return binomial(sigm(wx_b))
def rbmup(self, X):
X = sigm(np.dot(X, self.W.T) + self.c)
return X
def rbmdown(self, X):
X = sigm(np.dot(X, self.W) + self.b)
return X
