def dbn_forward_pass(ws_vh, ws_v, ws_h, x, y=None):
"""
Deep belief net forward pass.
x: input data (N x D matrix)
y: Class label (1-of-K coded, N x K matrix). If not None, it is concatenated
to the input for top layer RBM when calculating the output of the DBN.
ws_vh: list of layer weights (L x D x H)
ws_v: list of layer input biases (L x D x 1)
ws_h: list of layer output biases (L x H x 1)
Returns activations (continuous) and outputs (0-1, sigmoid(activations)) of
top layer
"""
L = len(ws_vh)
h = x.T
# forward (bottom-up) pass
for l in range(L - 1):
ah = gnp.dot(ws_vh[l].T, h) + ws_h[l]
h = gnp.logistic(ah)
# if supervised, concatenate class labels to input to top layer RBM
if y is not None:
h = gnp.concatenate((y.T, h))
ah = gnp.dot(ws_vh[-1].T, h) + ws_h[-1]
h = gnp.logistic(ah)
return ah.T, h.T
def check_fisher_information_indep():
"""Fisher information should agree with analytic solution for base rate RBM."""
with misc.gnumpy_conversion_check('allow'):
rbm = random_base_rate_rbm()
E_v = gnp.logistic(rbm.vbias)
E_h = gnp.logistic(rbm.hbias)
G = tractable.exact_fisher_information(rbm, batch_units=BATCH_UNITS)
assert_close(G, G.T, 'G not symmetric')
G_vis_vishid = G[:NVIS, NVIS + NHID:].reshape((NVIS, NVIS, NHID))
G_hid_vishid = G[NVIS:NVIS + NHID, NVIS + NHID:].reshape(
(NHID, NVIS, NHID))
G_vishid_vishid = G[NVIS + NHID:, NVIS + NHID:].reshape(
(NVIS, NHID, NVIS, NHID))
assert_close(G_vis_vishid[0, 0, 1], E_v[0] * (1. - E_v[0]) * E_h[1])
assert_close(G_vis_vishid[0, 1, 2], 0.)
assert_close(G_hid_vishid[0, 1, 0], E_h[0] * (1. - E_h[0]) * E_v[1])
assert_close(G_hid_vishid[0, 1, 2], 0.)
assert_close(G_vishid_vishid[0, 1, 0, 1],
E_v[0] * E_h[1] * (1. - E_v[0] * E_h[1]))
assert_close(G_vishid_vishid[0, 1, 0, 2],
E_v[0] * (1. - E_v[0]) * E_h[1] * E_h[2])
assert_close(G_vishid_vishid[0, 2, 1, 2],
E_h[2] * (1. - E_h[2]) * E_v[0] * E_v[1])
assert_close(G_vishid_vishid[0, 1, 2, 3], 0.)
def rbm_sample(w_vh, w_v, w_h, x, k=1, clamped=None):
"""
Sample from RBM with k steps of Gibbs sampling
w_vh: Weights between visible and hidden units (matrix of size DxH)
w_v: Visible unit biases (column vector of size Dx1)
w_h: Hidden unit biases (column vector of size Hx1)
x: Input (column vector of size DxN)
k: Number of Gibbs steps. Default is 1.
clamped: If not None, keeps the given elements of x clamped (constant)
while sampling
clamped is a two-tuple that gives the start and end indices of clamped elements
Returns hidden unit and visible unit activations (matrices of size HxN, DxN)
"""
if clamped is not None:
cx = x[clamped[0] : clamped[1], :]
v = x
for i in range(k):
# sample hiddens
ah = gnp.dot(w_vh.T, v) + w_h
h = gnp.logistic(ah)
hs = h > gnp.rand(h.shape[0], h.shape[1])
# sample visibles
av = gnp.dot(w_vh, hs) + w_v
v = gnp.logistic(av)
if clamped is not None:
v[clamped[0] : clamped[1], :] = cx
return h, v
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 exact_fisher_information_biases(rbm, batch_units=10, show_progress=False):
batch_size = 2 ** batch_units
nvis, nhid = rbm.nvis, rbm.nhid
num_params = nvis + nhid
s = gnp.zeros(num_params)
G = gnp.zeros((num_params, num_params))
for hid, p in iter_configurations(rbm, batch_units=batch_units, show_progress=show_progress):
g = gnp.zeros((batch_size, num_params))
cond_vis = gnp.logistic(rbm.vis_inputs(hid))
g[:, :nvis] = cond_vis
g[:, nvis:] = hid
s += gnp.dot(p, g)
G += gnp.dot(g.T * p, g)
diag_term = gnp.dot(p, g * (1. - g))
G += np.diag(diag_term.as_numpy_array())
G -= s[:, nax] * s[nax, :]
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 exact_samples(rbm, num, batch_units=10, show_progress=False):
scores = get_scores(rbm, batch_units=batch_units).as_numpy_array()
scores -= np.logaddexp.reduce(scores.ravel())
p = np.exp(scores)
prefix_len = rbm.nhid - batch_units
prefixes = combinations_array(prefix_len).as_numpy_array()
postfixes = combinations_array(batch_units).as_numpy_array()
p_row = p.sum(1)
p_row /= p_row.sum()
cond_p_col = p / p_row[:, nax]
cond_p_col *= (1. - 1e-8) # keep np.random.multinomial from choking because the sum is greater than 1
vis = np.zeros((num, rbm.nvis))
hid = np.zeros((num, rbm.nhid))
with misc.gnumpy_conversion_check('allow'):
rows = np.random.multinomial(1, p_row, size=num).argmax(1)
#cols = np.random.multinomial(1, cond_p_col[rows, :]).argmax(1)
cols = np.array([np.random.multinomial(1, cond_p_col[row, :]).argmax()
for row in rows])
hid = np.hstack([prefixes[rows, :], postfixes[cols, :]])
vis = np.random.binomial(1, gnp.logistic(rbm.vis_inputs(hid)))
return binary_rbms.RBMState(gnp.garray(vis), gnp.garray(hid))
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 dbn_supervised_predict_sample(ws_vh, ws_v, ws_h, x, k=20):
"""
Predict the class label of input x from supervised DBN
WARNING: THIS IS PRETTY SLOW AND LESS RELIABLE THAN THE EXACT METHOD
Uses the sampling method mentioned in section 6.2 of Hinton, Osindero, Teh 2006
x: Input data. (NxD matrix)
k: Number of Gibbs steps
"""
L = len(ws_vh)
N = x.shape[0]
# make a forward pass to get from input layer to visible layer of top level
# RBM
h_prev = x.T
# forward (bottom-up) pass, (use deterministic (we pass the activations, not
# the stochastically sampled steps) forward pass)
for l in range(L - 1):
ah = gnp.dot(ws_vh[l].T, h_prev) + ws_h[l]
h_prev = gnp.logistic(ah)
H = ws_vh[-1].shape[0] # number of visible units top level RBM
Hx = h_prev.shape[0] # number of hidden units in the penultimate layer
K = H - Hx
# (H - Hx) is the number of supervised inputs to top level RBM
# we give random values to the supervised portion of the input
v = gnp.concatenate((gnp.ones((K, N)) / K, h_prev))
# we keep the visible units clamped while sampling
h, v = rbm_sample(ws_vh[-1], ws_v[-1], ws_h[-1], v, k, clamped=(K, H))
# sample visible units of top level RBM given
return v[0:K, :].T
def sigmoid(x, computeGrad=False):
if (not computeGrad):
f = gp.logistic(x)
return f
g = x * (1. - x)
return g
def sigmoid(x, computeGrad = False):
if (not computeGrad):
f = gp.logistic(x)
return f
g = x * (1.-x)
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 pseudo_likelihood_for_bit(self, vis, i):
"""Returns the likelihood of bit i of vis given all other bits
of vis."""
fe = self.free_energy(vis)
vis_flip = vis
vis_flip[:,i] = 1 - vis[:,i]
fe_flip = self.free_energy(vis_flip)
pl = gp.log(gp.logistic(fe_flip - fe))
return pl
def __init__(self, m, n, q=100, name=""):
# name of layer
self.name = name
self.m = m # input layer size
self.n = n # output layer size
#self.p = p # piece group
self.q = q # batch size
self.dropout = 0.1 # dropout rate
self.learn = 10**-3
self.l2reg = (1.0 - 10**-100)
# activation function
#self.f = lambda z: 1.0/(gpu.exp(-z)+1.0)
self.f = lambda z: gpu.logistic(z)
#self.f = lambda z: z
# deriviative of activation function
#self.g = lambda z: self.f(z)*(1.0-self.f(z))
self.g = lambda z: z * (1.0 - z)
#self.g = lambda z: 1.0
d = 10**-8
# weight matrix
self.w = gpu.garray(
np.random.uniform(low=-d, high=d, size=(m, n)).astype(np.float32))
# bias vector
self.b = gpu.garray(
np.random.uniform(low=-d, high=d, size=(n)).astype(np.float32))
# input of forward propagation
self.x = gpu.garray(
np.random.uniform(low=-d, high=d, size=(q, m)).astype(np.float32))
# output of forward propagation
self.s = gpu.garray(
np.random.uniform(low=-d, high=d, size=(q, n)).astype(np.float32))
# input of back propagation
self.d = gpu.garray(
np.random.uniform(low=-d, high=d, size=(q, n)).astype(np.float32))
# output of back propagation
self.e = gpu.garray(
np.random.uniform(low=-d, high=d, size=(q, m)).astype(np.float32))
# temporary array for error
#self.u = gpu.garray(np.random.uniform(low=-d, high=d, size=(q, n, m)).astype(np.float32))
# novelty key ****-> set self.t.size to (n, 1, p, 1) ---> group max
# mask for dropout
self.r = gpu.garray(
np.random.uniform(low=0., high=1., size=(self.m)).astype(
np.float32) > self.dropout) / (1.0 - self.dropout)
#print self.r
# mask for piece group
#self.t = gpu.garray(np.random.randint(low=0, high=2, size=(1, n, q)).astype(np.float32))
# outward connections
self.next = []
# inward connections
self.prev = []
def dbn_sample(ws_vh, ws_v, ws_h, x, y=None, k=1):
"""
Sample from DBN
ws_vh, ws_v, ws_h: Lists of layer weights for DBN
x: Initial sample. This is the input to DBN. (1xD vector)
y: Class label for the sample. This corresponds to sampling from class
conditionals. (1-of-K coded, row vector)
k: Number of Gibbs steps
Returns a sample from DBN (1xD vector)
"""
L = len(ws_vh)
# make a forward pass to get from input layer to visible layer of top level
# RBM
h_prev = x.T
# forward (bottom-up) pass
for l in range(L - 1):
ah = gnp.dot(ws_vh[l].T, h_prev) + ws_h[l]
h_prev = gnp.logistic(ah)
h_prev = h_prev > gnp.rand(h_prev.shape[0], h_prev.shape[1])
# if not supervised, sample from top layer RBM without clamping any of its
# inputs
if y is None:
# sample from top layer RBM
h, v = rbm_sample(ws_vh[-1], ws_v[-1], ws_h[-1], h_prev, k)
else:
K = y.shape[1] # number of classes
H = ws_vh[-1].shape[0]
# generate a random input to top layer RBM with class label units clamped to y
v = gnp.concatenate((y.T, h_prev))
# sample from top layer RBM
h, v = rbm_sample(ws_vh[-1], ws_v[-1], ws_h[-1], v, k, clamped=(0, K))
v = v[K:H, :]
# backward (top-down) pass
# propagate sample from RBM back to input
for l in range(L - 2, -1, -1):
av = gnp.dot(ws_vh[l], v) + ws_v[l]
v = gnp.logistic(av)
return v.T
def check_fisher_information_biases_indep():
"""Fisher information should agree with analytic solution for base rate RBM."""
with misc.gnumpy_conversion_check('allow'):
rbm = random_base_rate_rbm()
E_v = gnp.logistic(rbm.vbias)
E_h = gnp.logistic(rbm.hbias)
G = tractable.exact_fisher_information_biases(rbm, batch_units=BATCH_UNITS)
assert_close(G, G.T, 'G not symmetric')
G_vis_vis = G[:NVIS, :NVIS]
G_vis_hid = G[:NVIS, NVIS:]
G_hid_hid = G[NVIS:, NVIS:]
assert_close(G_vis_vis[0, 0], E_v[0] * (1. - E_v[0]))
assert_close(G_vis_vis[0, 1], 0.)
assert_close(G_vis_hid[0, 0], 0.)
assert_close(G_hid_hid[0, 0], E_h[0] * (1. - E_h[0]))
assert_close(G_hid_hid[0, 1], 0.)
def nn_forward_pass(x, w, b, return_all=True):
"""
Forward pass for multilayer feed-forward sigmoid neural network
Hidden units have sigmoid non-linearity.
Output is soft-max.
x: DxN matrix of input data
w: Weights. List of weight matrices for each layer.
b: Biases. List of bias vectors for each layer
return_all: If True, returns hidden unit activations for each layer. If False
just returns the output layer activations
Returns a list h where each element is a matrix containing the activations
for that layer. h[0] is input data x.
"""
# ---- TEMP HACK --------------
# I should find a more seamless way of running in mixed (some operations
# with numpy, some with gnumpy) mode.
# I had to resort to this, because i needed the validation classification
# step in nn_train to run on CPU with numpy. GPU ran out of memory.
if isinstance(x, gnp.garray):
use_gpu = True
else:
use_gpu = False
layer_count = len(w)
if return_all:
hs = [x] # unit activations for each layer
h = x
# all layers except the output layer
for l in range(layer_count - 1):
if use_gpu:
a = gnp.dot(w[l].T, h) + b[l]
h = gnp.logistic(a)
else:
a = np.dot(gnp.as_numpy_array(w[l]).T, h) + gnp.as_numpy_array(b[l])
h = 1.0 / (1 + np.exp(-a))
if return_all:
hs.append(h)
# output layer
if use_gpu:
h = gnp.dot(w[-1].T, h) + b[-1]
h = gnp.exp(h) / gnp.sum(gnp.exp(h), axis=0) # soft-max
else:
h = np.dot(gnp.as_numpy_array(w[-1]).T, h) + gnp.as_numpy_array(b[-1])
h = np.exp(h) / np.sum(np.exp(h), axis=0) # soft-max
if return_all:
hs.append(h)
return hs
else:
return h
def check_fisher_information_biases_indep():
"""Fisher information should agree with analytic solution for base rate RBM."""
with misc.gnumpy_conversion_check('allow'):
rbm = random_base_rate_rbm()
E_v = gnp.logistic(rbm.vbias)
E_h = gnp.logistic(rbm.hbias)
G = tractable.exact_fisher_information_biases(rbm,
batch_units=BATCH_UNITS)
assert_close(G, G.T, 'G not symmetric')
G_vis_vis = G[:NVIS, :NVIS]
G_vis_hid = G[:NVIS, NVIS:]
G_hid_hid = G[NVIS:, NVIS:]
assert_close(G_vis_vis[0, 0], E_v[0] * (1. - E_v[0]))
assert_close(G_vis_vis[0, 1], 0.)
assert_close(G_vis_hid[0, 0], 0.)
assert_close(G_hid_hid[0, 0], E_h[0] * (1. - E_h[0]))
assert_close(G_hid_hid[0, 1], 0.)
def dbn_supervised_predict_exact(ws_vh, ws_v, ws_h, x):
"""
Predict the class label of input x from supervised DBN
Uses the exact method mentioned in section 6.2 of Hinton, Osindero, Teh 2006
The free energy formula is taken from http://deeplearning.net/tutorial/rbm.html
x: Input data. (NxD matrix)
"""
L = len(ws_vh)
N = x.shape[0]
# make a forward pass to get from input layer to visible layer of top level
# RBM
h_prev = x.T
# forward (bottom-up) pass, (use deterministic (we pass the activations, not
# the stochastically sampled steps) forward pass)
for l in range(L - 1):
ah = gnp.dot(ws_vh[l].T, h_prev) + ws_h[l]
h_prev = gnp.logistic(ah)
H = ws_vh[-1].shape[0] # number of visible units top level RBM
Hx = h_prev.shape[0] # number of hidden units in the penultimate layer
K = H - Hx
# (H - Hx) is the number of supervised inputs to top level RBM
# for every class, assume it is the correct label and calculate its free energy
y = gnp.zeros((K, N))
free_energy = gnp.zeros((N, K)) # we actually calculate -free_energy
for k in range(K):
# set the current assumed class label
y[k, :] = 1.0
# visible unit vector
v = gnp.concatenate((y, h_prev))
e_v = gnp.dot(ws_v[-1].T, v) # bias energy term
ah = gnp.dot(ws_vh[-1].T, v) + ws_h[-1]
e_h = gnp.sum(gnp.log(gnp.exp(ah) + 1.0), axis=0)
free_energy[:, k] = e_v + e_h
# zero the class labels for next iteration
y[:, :] = 0.0
# since these numbers may get pretty small, use the sum-exp trick for converting
# these to probabilities
pred_y = (
gnp.exp(free_energy - gnp.max(free_energy, axis=1)[:, gnp.newaxis])
/ gnp.sum(gnp.exp(free_energy - gnp.max(free_energy, axis=1)[:, gnp.newaxis]), axis=1)[:, gnp.newaxis]
)
return pred_y
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 exact_moments(rbm, batch_units=10, show_progress=False):
expect_vis = gnp.zeros(rbm.nvis)
expect_hid = gnp.zeros(rbm.nhid)
expect_prod = gnp.zeros((rbm.nvis, rbm.nhid))
for hid, p in iter_configurations(rbm, batch_units=batch_units, show_progress=show_progress):
cond_vis = gnp.logistic(rbm.vis_inputs(hid))
expect_vis += gnp.dot(p, cond_vis)
expect_hid += gnp.dot(p, hid)
expect_prod += gnp.dot(cond_vis.T * p, hid)
return binary_rbms.Moments(expect_vis, expect_hid, expect_prod)
def check_fisher_information_indep():
"""Fisher information should agree with analytic solution for base rate RBM."""
with misc.gnumpy_conversion_check('allow'):
rbm = random_base_rate_rbm()
E_v = gnp.logistic(rbm.vbias)
E_h = gnp.logistic(rbm.hbias)
G = tractable.exact_fisher_information(rbm, batch_units=BATCH_UNITS)
assert_close(G, G.T, 'G not symmetric')
G_vis_vishid = G[:NVIS, NVIS+NHID:].reshape((NVIS, NVIS, NHID))
G_hid_vishid = G[NVIS:NVIS+NHID, NVIS+NHID:].reshape((NHID, NVIS, NHID))
G_vishid_vishid = G[NVIS+NHID:, NVIS+NHID:].reshape((NVIS, NHID, NVIS, NHID))
assert_close(G_vis_vishid[0, 0, 1], E_v[0] * (1. - E_v[0]) * E_h[1])
assert_close(G_vis_vishid[0, 1, 2], 0.)
assert_close(G_hid_vishid[0, 1, 0], E_h[0] * (1. - E_h[0]) * E_v[1])
assert_close(G_hid_vishid[0, 1, 2], 0.)
assert_close(G_vishid_vishid[0, 1, 0, 1], E_v[0] * E_h[1] * (1. - E_v[0] * E_h[1]))
assert_close(G_vishid_vishid[0, 1, 0, 2], E_v[0] * (1. - E_v[0]) * E_h[1] * E_h[2])
assert_close(G_vishid_vishid[0, 2, 1, 2], E_h[2] * (1. - E_h[2]) * E_v[0] * E_v[1])
assert_close(G_vishid_vishid[0, 1, 2, 3], 0.)
def __init__(self, m, n, q=100, name=""):
# name of layer
self.name = name
self.m = m # input layer size
self.n = n # output layer size
# self.p = p # piece group
self.q = q # batch size
self.dropout = 0.1 # dropout rate
self.learn = 10 ** -3
self.l2reg = 1.0 - 10 ** -100
# activation function
# self.f = lambda z: 1.0/(gpu.exp(-z)+1.0)
self.f = lambda z: gpu.logistic(z)
# self.f = lambda z: z
# deriviative of activation function
# self.g = lambda z: self.f(z)*(1.0-self.f(z))
self.g = lambda z: z * (1.0 - z)
# self.g = lambda z: 1.0
d = 10 ** -8
# weight matrix
self.w = gpu.garray(np.random.uniform(low=-d, high=d, size=(m, n)).astype(np.float32))
# bias vector
self.b = gpu.garray(np.random.uniform(low=-d, high=d, size=(n)).astype(np.float32))
# input of forward propagation
self.x = gpu.garray(np.random.uniform(low=-d, high=d, size=(q, m)).astype(np.float32))
# output of forward propagation
self.s = gpu.garray(np.random.uniform(low=-d, high=d, size=(q, n)).astype(np.float32))
# input of back propagation
self.d = gpu.garray(np.random.uniform(low=-d, high=d, size=(q, n)).astype(np.float32))
# output of back propagation
self.e = gpu.garray(np.random.uniform(low=-d, high=d, size=(q, m)).astype(np.float32))
# temporary array for error
# self.u = gpu.garray(np.random.uniform(low=-d, high=d, size=(q, n, m)).astype(np.float32))
# novelty key ****-> set self.t.size to (n, 1, p, 1) ---> group max
# mask for dropout
self.r = gpu.garray(np.random.uniform(low=0.0, high=1.0, size=(self.m)).astype(np.float32) > self.dropout) / (
1.0 - self.dropout
)
# print self.r
# mask for piece group
# self.t = gpu.garray(np.random.randint(low=0, high=2, size=(1, n, q)).astype(np.float32))
# outward connections
self.next = []
# inward connections
self.prev = []
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 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_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 sig_ssd(z, targets, weight=0.5, predict=False, error=False, addon=0):
"""
Sigmoid SSD.
"""
bern = gpu.logistic(z)
if predict:
return bern
n, m = bern.shape
err = bern - targets
if error:
# rec. error + first deriv
return weight * gpu.sum(err**2) / n + addon, 2. * weight * err / n
else:
# only return reconstruction error
return weight * gpu.sum(err**2) / n + addon
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 mia(z, targets, predict=False, error=False, addon=0, tiny=1e-10):
"""
Multiple independent attributes (i.e. independent
binary cross entropy errors).
Feed model output _z_ through logistic to get
bernoulli distributed variables.
"""
bern = gpu.logistic(z)
if predict:
return bern
n, _ = bern.shape
# loss is binary cross entropy
# for every output variable
bce = -(targets * (bern + tiny).log() + (1 - targets) *
(1 - bern + tiny).log()).sum()
if error:
return bce + addon, (bern - targets) / n
else:
return bce + addon
def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
y = gnp.logistic(gnp.as_garray(pred))
return -((1 - self.target) * safe_log(1 - y) +
self.target * safe_log(y)).sum(), y - self.target
import numpy as np
import gnumpy as g
from bkputils import *
import scipy.special
g.max_memory_usage = 285 * (1024 * 1024) # no idea where this comes from, but it makes gnumpy not crash (empiric value)
w = 10000
h = 10000
write("making random matrices")
m1 = np.random.rand(w, h)
m2 = np.random.rand(h, w)
writeDone()
write("numpy multiply")
n = np.dot(m1, m2)
p = scipy.special.expit(n)
writeDone()
write("gnumpy setup")
a = g.garray(m1)
b = g.garray(m2)
writeDone()
write("gnumpy multiply")
c = g.dot(a, b)
c = g.logistic(c)
writeDone()
def rbm_train(dataset, H, batch_size, epoch_count, epsilon, momentum, return_hidden=True, verbose=True):
"""
Train a (binary) restricted boltzmann machine.
dataset: Input data. DataSet instance or matrix of size N (number of data points) x D (input dimension)
H: Number of hidden units
batch_size: Number of data points in each batch
epoch_count: Number of training epochs
epsilon: Learning rate, either a scalar or an array (one value for each epoch)
momentum: Momentum parameter, either a scalar or an array (one value for each epoch)
return_hidden: If True, returns hidden unit activations for training data.
verbose: If True, prints progress information
Returns w_vh (weights between visible-hidden units), w_v (visible unit
biases), w_h (hidden unit biases), h (hidden unit activations for input data),
error (reconstruction error at each epoch)
"""
if isinstance(dataset, ds.DataSet):
train_x = dataset.train.x
N = dataset.train.N
D = dataset.train.D
else:
train_x = dataset
N = train_x.shape[0]
D = train_x.shape[1]
batch_count = int(np.ceil(N / float(batch_size)))
# if momentum is a scalar, create a list with the same value for all epochs
if not isinstance(momentum, list):
momentum = [momentum] * epoch_count
if not isinstance(epsilon, list):
epsilon = [epsilon] * epoch_count
# initialize weights
w_vh = gnp.randn((D, H)) * 0.1
w_v = gnp.zeros((D, 1))
w_h = gnp.zeros((H, 1))
# weight updates
dw_vh = gnp.zeros((D, H))
dw_v = gnp.zeros((D, 1))
dw_h = gnp.zeros((H, 1))
# hidden unit activations
if return_hidden:
h = np.zeros((N, H)) # keep this a numpy array to save memory
else:
h = []
start_time = time.time()
# reconstruction errors over epochs
error = []
batch_order = range(batch_count)
for e in range(epoch_count):
if verbose:
print("Epoch " + repr(e + 1))
batch_error = []
processed_batch = 0
for b in range(batch_count):
processed_batch += 1
if verbose:
print("\r%d/%d" % (processed_batch, batch_count)),
start = b * batch_size
end = (b + 1) * batch_size if (b + 1) * batch_size < N else N
x = train_x[start:end, :].T
# apply momentum
dw_vh *= momentum[e]
dw_v *= momentum[e]
dw_h *= momentum[e]
# positive phase
ahp = gnp.dot(w_vh.T, x) + w_h
hp = gnp.logistic(ahp)
# if it is the last epoch, store hidden unit activations
if return_hidden and e == epoch_count - 1:
h[start:end, :] = gnp.as_numpy_array(hp.T)
# add positive gradient term
dw_vh += gnp.dot(x, hp.T)
dw_v += gnp.sum(x, axis=1)[:, gnp.newaxis]
dw_h += gnp.sum(hp, axis=1)[:, gnp.newaxis]
# sample hiddens
hs = hp > gnp.rand(hp.shape[0], hp.shape[1])
# negative phase
avn = gnp.dot(w_vh, hs) + w_v
vn = gnp.logistic(avn)
ahn = gnp.dot(w_vh.T, vn) + w_h
hn = gnp.logistic(ahn)
dw_vh -= gnp.dot(vn, hn.T)
dw_v -= gnp.sum(vn, axis=1)[:, gnp.newaxis]
dw_h -= gnp.sum(hn, axis=1)[:, gnp.newaxis]
# update weights
w_vh += epsilon[e] / (end - start) * dw_vh
w_v += epsilon[e] / (end - start) * dw_v
w_h += epsilon[e] / (end - start) * dw_h
batch_error.append(gnp.mean((vn - x) ** 2))
# shuffle batch order
np.random.shuffle(batch_order)
error.append(np.mean(batch_error))
if verbose:
print("\nReconstruction error: " + repr(error[-1]))
print("Elapsed time: " + str(time.time() - start_time))
return w_vh, w_v, w_h, h, error
def negative_phase(self):
self.h = gpu.logistic(gpu.dot(self.v,self.w)+self.bias_h)
self.w_updt -= gpu.dot(self.v.T,self.h)
self.bias_h_updt -= gpu.sum(self.h,axis=0)
self.bias_v_updt -= gpu.sum(self.v,axis=0)
def p_vis_given_hid(self, hid):
"""Returns a vector whose ith component is the probability that the ith
visible unit is active given the states of the hidden units"""
return gp.logistic(gp.dot(hid, self.weights.T) + self.bias_vis)
def gibbs_updates(self):
self.h = (self.h > gpu.rand(100,800))
self.v = gpu.logistic(gpu.dot(self.h,self.w.T)+self.bias_v)
def vis_expectations(self, h):
return gnp.logistic(self.vis_inputs(h))
def forward(self, A):
return gnp.logistic(A)
def hid_expectations(self, v):
return gnp.logistic(self.hid_inputs(v))
def forward_prop(self, x):
return gnp.logistic(x)
def p_hid_given_vis(self, vis):
"""Returns a vector whose ith component is the probability that the ith
hidden unit is active given the states of the visible units"""
return gp.logistic(gp.dot(vis, self.weights) + self.bias_hid)
def forward_prop(self, x):
return gnp.logistic(x)
m2 = (momentum*m2) - ((grad2 + n2*L2)*alpha/(batch_size*1.0))
mb1 = (momentum*mb1) - ((gradb1 + nb1*L2)*alpha/(batch_size*1.0))
mb2 = (momentum*mb2) - ((gradb2 + nb2*L2)*alpha/(batch_size*1.0))
w1 = w1 + m1
w2 = w2 + m2
b1 = b1 + mb1
b2 = b2 + mb2
momentum = momentum + 0.001
if momentum > 0.95: momentum = 0.95
batch_error_cv = 0
for i in range(100):
batch_error_cv += 1.0 - (gpu.sum(np.argmax(gpu.dot(gpu.logistic(gpu.dot(X_val[i],w1))*0.5,w2),axis=1) == y_val[i])/120.)
batch_error = 0
for i in xrange(batches):#train error 5.9 sec
z1 = gpu.dot(X[i],w1).logistic()*0.5
feedforward = gpu.dot(z1,w2)
batch_error += 1. - (np.sum(np.equal(np.argmax(feedforward,axis=1),y.as_numpy_array()[i].T)/(batch_size*1.0)))
'''
if gpu.max(w1)**2 > 9:
print 'halving the weights of w1'
w1 = w1/2.
m1 = m1/2.
if gpu.max(w2)**2 > 9:
print 'halving the weights of w2'
w2 = w2/2.
mb2 = (momentum * mb2) - ((gradb2 + nb2 * L2) * alpha /
(batch_size * 1.0))
w1 = w1 + m1
w2 = w2 + m2
b1 = b1 + mb1
b2 = b2 + mb2
momentum = momentum + 0.001
if momentum > 0.95: momentum = 0.95
batch_error_cv = 0
for i in range(100):
batch_error_cv += 1.0 - (gpu.sum(
np.argmax(gpu.dot(gpu.logistic(gpu.dot(X_val[i], w1)) * 0.5, w2),
axis=1) == y_val[i]) / 120.)
batch_error = 0
for i in xrange(batches): #train error 5.9 sec
z1 = gpu.dot(X[i], w1).logistic() * 0.5
feedforward = gpu.dot(z1, w2)
batch_error += 1. - (np.sum(
np.equal(np.argmax(feedforward, axis=1),
y.as_numpy_array()[i].T) / (batch_size * 1.0)))
'''
if gpu.max(w1)**2 > 9:
print 'halving the weights of w1'
w1 = w1/2.
m1 = m1/2.
def hid_expectations(self, v):
return gnp.logistic(self.hid_inputs(v))
def compute_not_weighted_loss_and_grad(self, pred, compute_grad=False):
y = gnp.logistic(pred)
return -((1 - self.target) * safe_log(1 - y) + self.target * safe_log(y)).sum(), y - self.target
def forward(self, A):
return gnp.logistic(A)
def base_sample_vis(self, n_samples):
"Samples the visible units from the base rate RBM"
p = gp.logistic(self.base_bias_vis)
r = gp.rand((n_samples, self.base_bias_vis.shape[0]))
return r < p
def vis_expectations(self, h):
return gnp.logistic(self.vis_inputs(h))
def exact_fisher_information(rbm, batch_units=10, show_progress=False, vis_shape=None, downsample=1, return_mean=False):
batch_size = 2 ** batch_units
if downsample == 1:
vis_idxs = np.arange(rbm.nvis)
else:
temp = np.arange(rbm.nvis).reshape((28, 28))
mask = np.zeros((28, 28), dtype=bool)
mask[::downsample, ::downsample] = 1
vis_idxs = temp[mask]
nvis = vis_idxs.size
nhid = rbm.nhid
num_params = nvis + nhid + nvis * nhid
E_vis = np.zeros(nvis)
E_hid = np.zeros(nhid)
E_vishid = np.zeros((nvis, nhid))
E_vis_vis = np.zeros((nvis, nvis))
E_vis_hid = np.zeros((nvis, nhid))
E_vis_vishid = np.zeros((nvis, nvis, nhid))
E_hid_hid = np.zeros((nhid, nhid))
E_hid_vishid = np.zeros((nhid, nvis, nhid))
E_vishid_vishid = np.zeros((nvis, nhid, nvis, nhid))
for hid, p in iter_configurations(rbm, batch_units=batch_units, show_progress=show_progress):
with misc.gnumpy_conversion_check('allow'):
cond_vis = gnp.logistic(rbm.vis_inputs(hid))
cond_vis = gnp.garray(cond_vis.as_numpy_array()[:, vis_idxs])
vishid = (cond_vis[:, :, nax] * hid[:, nax, :]).reshape((batch_size, nvis * nhid))
var_vis = cond_vis * (1. - cond_vis)
E_vis += gnp.dot(p, cond_vis)
E_hid += gnp.dot(p, hid)
E_vishid += gnp.dot(cond_vis.T * p, hid)
E_vis_vis += gnp.dot(cond_vis.T * p, cond_vis)
diag_term = gnp.dot(p, cond_vis * (1. - cond_vis))
E_vis_vis += gnp.garray(np.diag(diag_term.as_numpy_array()))
E_vis_hid += gnp.dot(cond_vis.T * p, hid)
E_hid_hid += gnp.dot(hid.T * p, hid)
E_vis_vishid += gnp.dot(cond_vis.T * p, vishid).reshape((nvis, nvis, nhid))
diag_term = gnp.dot(var_vis.T * p, hid)
E_vis_vishid[np.arange(nvis), np.arange(nvis), :] += diag_term
E_hid_vishid += gnp.dot(hid.T * p, vishid).reshape((nhid, nvis, nhid))
E_vishid_vishid += gnp.dot(vishid.T * p, vishid).reshape((nvis, nhid, nvis, nhid))
diag_term = ((cond_vis * (1. - cond_vis))[:, :, nax, nax] * hid[:, nax, :, nax] * hid[:, nax, nax, :] * p[:, nax, nax, nax]).sum(0)
E_vishid_vishid[np.arange(nvis), :, np.arange(nvis), :] += diag_term
G = np.zeros((num_params, num_params))
vis_slc = slice(0, nvis)
hid_slc = slice(nvis, nvis + nhid)
vishid_slc = slice(nvis + nhid, None)
G[vis_slc, vis_slc] = E_vis_vis
G[vis_slc, hid_slc] = E_vis_hid
G[vis_slc, vishid_slc] = E_vis_vishid.reshape((nvis, nvis * nhid))
G[hid_slc, vis_slc] = E_vis_hid.T
G[hid_slc, hid_slc] = E_hid_hid
G[hid_slc, vishid_slc] = E_hid_vishid.reshape((nhid, nvis * nhid))
G[vishid_slc, vis_slc] = E_vis_vishid.reshape((nvis, nvis * nhid)).T
G[vishid_slc, hid_slc] = E_hid_vishid.reshape((nhid, nvis * nhid)).T
G[vishid_slc, vishid_slc] = E_vishid_vishid.reshape((nvis * nhid, nvis * nhid))
s = np.concatenate([E_vis, E_hid, E_vishid.ravel()])
G -= np.outer(s, s)
if return_mean:
return G, s
else:
return G
