def fprop(params):
"""
Forward pass of the NTM.
"""
W = params # aliasing for brevity
xs, hs, ys, ps, ts, os = {}, {}, {}, {}, {}, {}
def l():
"""
Silly utility function that should be called in init.
"""
return [{} for _ in xrange(self.heads)]
rs = l()
k_rs, beta_rs, g_rs, s_rs, gamma_rs = l(),l(),l(),l(),l()
k_ws, beta_ws, g_ws, s_ws, gamma_ws = l(),l(),l(),l(),l()
adds, erases = l(),l()
w_ws, w_rs = l(),l() # read weights and write weights
for idx in range(self.heads):
rs[idx][-1] = self.W['rsInit' + str(idx)] # stores values read from memory
w_ws[idx][-1] = softmax(self.W['w_wsInit' + str(idx)])
w_rs[idx][-1] = softmax(self.W['w_rsInit' + str(idx)])
mems = {} # the state of the memory at every timestep
mems[-1] = self.W['memsInit']
loss = 0
for t in xrange(len(inputs)):
xs[t] = np.reshape(np.array(inputs[t]),inputs[t].shape[::-1])
rsum = 0
for idx in range(self.heads):
rsum = rsum + np.dot(W['rh' + str(idx)], np.reshape(rs[idx][t-1],(self.M,1)))
hs[t] = np.tanh(np.dot(W['xh'], xs[t]) + rsum + W['bh'])
os[t] = np.tanh(np.dot(W['ho'], hs[t]) + W['bo'])
for idx in range(self.heads):
# parameters to the read head
k_rs[idx][t] = np.tanh(np.dot(W['ok_r' + str(idx)],os[t]) + W['bk_r' + str(idx)])
beta_rs[idx][t] = softplus(np.dot(W['obeta_r' + str(idx)],os[t])
+ W['bbeta_r' + str(idx)])
g_rs[idx][t] = sigmoid(np.dot(W['og_r' + str(idx)],os[t]) + W['bg_r' + str(idx)])
s_rs[idx][t] = softmax(np.dot(W['os_r' + str(idx)],os[t]) + W['bs_r' + str(idx)])
gamma_rs[idx][t] = 1 + sigmoid(np.dot(W['ogamma_r' + str(idx)], os[t])
+ W['bgamma_r' + str(idx)])
# parameters to the write head
k_ws[idx][t] = np.tanh(np.dot(W['ok_w' + str(idx)],os[t]) + W['bk_w' + str(idx)])
beta_ws[idx][t] = softplus(np.dot(W['obeta_w' + str(idx)], os[t])
+ W['bbeta_w' + str(idx)])
g_ws[idx][t] = sigmoid(np.dot(W['og_w' + str(idx)],os[t]) + W['bg_w' + str(idx)])
s_ws[idx][t] = softmax(np.dot(W['os_w' + str(idx)],os[t]) + W['bs_w' + str(idx)])
gamma_ws[idx][t] = 1 + sigmoid(np.dot(W['ogamma_w' + str(idx)], os[t])
+ W['bgamma_w' + str(idx)])
# the erase and add vectors
# these are also parameters to the write head
# but they describe "what" is to be written rather than "where"
adds[idx][t] = np.tanh(np.dot(W['oadds' + str(idx)], os[t]) + W['badds' + str(idx)])
erases[idx][t] = sigmoid(np.dot(W['oerases' + str(idx)], os[t]) + W['erases' + str(idx)])
w_ws[idx][t] = addressing.create_weights( k_ws[idx][t]
, beta_ws[idx][t]
, g_ws[idx][t]
, s_ws[idx][t]
, gamma_ws[idx][t]
, w_ws[idx][t-1]
, mems[t-1])
w_rs[idx][t] = addressing.create_weights( k_rs[idx][t]
, beta_rs[idx][t]
, g_rs[idx][t]
, s_rs[idx][t]
, gamma_rs[idx][t]
, w_rs[idx][t-1]
, mems[t-1])
ys[t] = np.dot(W['oy'], os[t]) + W['by']
ps[t] = sigmoid(ys[t])
one = np.ones(ps[t].shape)
ts[t] = np.reshape(np.array(targets[t]),(self.out_size,1))
epsilon = 2**-23 # to prevent log(0)
a = np.multiply(ts[t] , np.log2(ps[t] + epsilon))
b = np.multiply(one - ts[t], np.log2(one-ps[t] + epsilon))
loss = loss - (a + b)
for idx in range(self.heads):
# read from the memory
rs[idx][t] = memory.read(mems[t-1],w_rs[idx][t])
# write into the memory
mems[t] = memory.write(mems[t-1],w_ws[idx][t],erases[idx][t],adds[idx][t])
self.stats = [loss, ps, w_rs, w_ws, adds, erases]
return np.sum(loss)
def manual_grads(params):
"""
Compute the gradient of the loss WRT the parameters
Ordering of the operations is reverse of that in fprop()
"""
deltas = {}
for key, val in params.iteritems():
deltas[key] = np.zeros_like(val)
[
loss, mems, ps, ys, os, zos, hs, zhs, xs, rs, w_rs, w_ws, adds,
erases, k_rs, k_ws, g_rs, g_ws, wc_rs, wc_ws, zbeta_rs,
zbeta_ws, zs_rs, zs_ws, wg_rs, wg_ws
] = self.stats
dd = {}
drs = {}
dzh = {}
dmem = {} # might not need this, since we have dmemtilde
dmemtilde = {}
du_r = {}
du_w = {}
dwg_r = {}
dwg_w = {}
for t in reversed(xrange(len(targets))):
dy = np.copy(ps[t])
dy -= targets[t].T # backprop into y
deltas['oy'] += np.dot(dy, os[t].T)
deltas['by'] += dy
if t < len(targets) - 1:
# r[t] affects cost through zh[t+1] via Wrh
drs[t] = np.dot(self.W['rh'].T, dzh[t + 1])
# right now, mems[t] influences cost through rs[t+1], via w_rs[t+1]
dmem[t] = np.dot(w_rs[t + 1], drs[t + 1].reshape(
(self.M, 1)).T)
# and also through mems at next step
W = np.reshape(w_ws[t + 1], (w_ws[t + 1].shape[0], 1))
E = np.reshape(erases[t + 1], (erases[t + 1].shape[0], 1))
WTE = np.dot(W, E.T)
KEEP = np.ones(mems[0].shape) - WTE
dmem[t] += np.multiply(dmemtilde[t + 1], KEEP)
# and also through its influence on the content weighting next step
dmem[t] += du_r[t + 1] + du_w[t + 1]
dmemtilde[t] = dmem[t]
# erases[t] affects cost through mems[t], via w_ws[t]
derase = np.dot(
np.multiply(dmemtilde[t], -mems[t - 1]).T, w_ws[t])
# zerase affects just erases through a sigmoid
dzerase = derase * (erases[t] * (1 - erases[t]))
# adds[t] affects costs through mems[t], via w_ws
dadd = np.dot(dmem[t].T, w_ws[t])
# zadds affects just adds through a tanh
dzadd = dadd * (1 - adds[t] * adds[t])
# dbadds is just dzadds
deltas['badds'] += dzadd
deltas['oadds'] += np.dot(dzadd, os[t].T)
deltas['berases'] += dzerase
deltas['oerases'] += np.dot(dzerase, os[t].T)
# # read weights affect what is read, via what's in mems[t-1]
# dwc_r = np.dot(mems[t-1], drs[t])
# # write weights affect mem[t] through adding
# dwc_w = np.dot(dmem[t], adds[t])
# # they also affect memtilde[t] through erasing
# dwc_w += np.dot(np.multiply(dmemtilde[t], -mems[t-1]), erases[t])
dw_r = np.dot(mems[t - 1], drs[t])
dw_r += dwg_r[t + 1] * (1 - g_rs[t + 1])
# write weights affect mem[t] through adding
dw_w = np.dot(dmem[t], adds[t])
# they also affect memtilde[t] through erasing
dw_w += np.dot(np.multiply(dmemtilde[t], -mems[t - 1]),
erases[t])
dw_w += dwg_w[t + 1] * (1 - g_ws[t + 1])
sgwr = np.zeros((self.N, self.N))
sgww = np.zeros((self.N, self.N))
for i in range(self.N):
sgwr[i, i] = softmax(zs_rs[t])[0]
sgwr[i, (i + 1) % self.N] = softmax(zs_rs[t])[2]
sgwr[i, (i - 1) % self.N] = softmax(zs_rs[t])[1]
sgww[i, i] = softmax(zs_ws[t])[0]
sgww[i, (i + 1) % self.N] = softmax(zs_ws[t])[2]
sgww[i, (i - 1) % self.N] = softmax(zs_ws[t])[1]
# right now, shifted weights are final weight
dws_r = dw_r
dws_w = dw_w
dwg_r[t] = np.dot(sgwr.T, dws_r)
dwg_w[t] = np.dot(sgww.T, dws_w)
dwc_r = dwg_r[t] * g_rs[t]
dwc_w = dwg_w[t] * g_ws[t]
"""
We need dw/dK
now w has N elts and K has N elts
and we want, for every elt of W, the grad of that elt w.r.t. each
of the N elts of K. that gives us N * N things
"""
# first, we must build up the K values (should be taken from fprop)
K_rs = []
K_ws = []
for i in range(self.N):
K_rs.append(cosine_sim(mems[t - 1][i, :], k_rs[t]))
K_ws.append(cosine_sim(mems[t - 1][i, :], k_ws[t]))
# then, we populate the grads
dwdK_r = np.zeros((self.N, self.N))
dwdK_w = np.zeros((self.N, self.N))
# for every row in the memory
for i in range(self.N):
# for every element in the weighting
for j in range(self.N):
dwdK_r[i,
j] += softmax_grads(K_rs,
softplus(zbeta_rs[t]),
i, j)
dwdK_w[i,
j] += softmax_grads(K_ws,
softplus(zbeta_ws[t]),
i, j)
# compute dK for all i in N
# K is the evaluated cosine similarity for the i-th row of mem matrix
dK_r = np.zeros_like(w_rs[0])
dK_w = np.zeros_like(w_ws[0])
# for all i in N (for every row that we've simmed)
for i in range(self.N):
# for every j in N (for every elt of the weighting)
for j in range(self.N):
# specifically, dwdK_r will change, and for write as well
dK_r[i] += dwc_r[j] * dwdK_r[i, j]
dK_w[i] += dwc_w[j] * dwdK_w[i, j]
"""
dK_r_dk_rs is a list of N things
each elt of the list corresponds to grads of K_idx
w.r.t. the key k_t
so it should be a length N list of M by 1 vectors
"""
dK_r_dk_rs = []
dK_r_dmem = []
for i in range(self.N):
# let k_rs be u, Mem[i] be v
u = np.reshape(k_rs[t], (self.M, ))
v = mems[t - 1][i, :]
dK_r_dk_rs.append(dKdu(u, v))
dK_r_dmem.append(dKdu(v, u))
dK_w_dk_ws = []
dK_w_dmem = []
for i in range(self.N):
# let k_ws be u, Mem[i] be v
u = np.reshape(k_ws[t], (self.M, ))
v = mems[t - 1][i, :]
dK_w_dk_ws.append(dKdu(u, v))
dK_w_dmem.append(dKdu(v, u))
# compute delta for keys
dk_r = np.zeros_like(k_rs[0])
dk_w = np.zeros_like(k_ws[0])
# for every one of M elt of dk_r
for i in range(self.M):
# for every one of the N Ks
for j in range(self.N):
# add delta K_r[j] * dK_r[j] / dk_r[i]
# add influence on through K_r[j]
dk_r[i] += dK_r[j] * dK_r_dk_rs[j][i]
dk_w[i] += dK_w[j] * dK_w_dk_ws[j][i]
# these represent influence of mem on next K
"""
Let's let du_r[t] represent the
influence of mems[t-1] on the cost through the K values
this is analogous to dk_w, but, k only every affects that
whereas mems[t-1] will also affect what is read at time t+1
and through memtilde at time t+1
"""
du_r[t] = np.zeros_like(mems[0])
du_w[t] = np.zeros_like(mems[0])
# for every row in mems[t-1]
for i in range(self.N):
# for every elt of this row (one of M)
for j in range(self.M):
du_r[t][i, j] = dK_r[i] * dK_r_dmem[i][j]
du_w[t][i, j] = dK_w[i] * dK_w_dmem[i][j]
# key values are activated as tanh
dzk_r = dk_r * (1 - k_rs[t] * k_rs[t])
dzk_w = dk_w * (1 - k_ws[t] * k_ws[t])
deltas['ok_r'] += np.dot(dzk_r, os[t].T)
deltas['ok_w'] += np.dot(dzk_w, os[t].T)
deltas['bk_r'] += dzk_r
deltas['bk_w'] += dzk_w
dg_r = np.dot(dwg_r[t].T, (wc_rs[t] - w_rs[t - 1]))
dg_w = np.dot(dwg_w[t].T, (wc_ws[t] - w_ws[t - 1]))
# compute dzg_r, dzg_w
dzg_r = dg_r * (g_rs[t] * (1 - g_rs[t]))
dzg_w = dg_w * (g_ws[t] * (1 - g_ws[t]))
deltas['og_r'] += np.dot(dzg_r, os[t].T)
deltas['og_w'] += np.dot(dzg_w, os[t].T)
deltas['bg_r'] += dzg_r
deltas['bg_w'] += dzg_w
# compute dbeta, which affects w_content through interaction with Ks
dwcdbeta_r = np.zeros_like(w_rs[0])
dwcdbeta_w = np.zeros_like(w_ws[0])
for i in range(self.N):
dwcdbeta_r[i] = beta_grads(K_rs, softplus(zbeta_rs[t]),
i)
dwcdbeta_w[i] = beta_grads(K_ws, softplus(zbeta_ws[t]),
i)
dbeta_r = np.zeros_like(zbeta_rs[0])
dbeta_w = np.zeros_like(zbeta_ws[0])
for i in range(self.N):
dbeta_r[0] += dwc_r[i] * dwcdbeta_r[i]
dbeta_w[0] += dwc_w[i] * dwcdbeta_w[i]
# beta is activated from zbeta by softplus, grad of which is sigmoid
dzbeta_r = dbeta_r * sigmoid(zbeta_rs[t])
dzbeta_w = dbeta_w * sigmoid(zbeta_ws[t])
deltas['obeta_r'] += np.dot(dzbeta_r, os[t].T)
deltas['obeta_w'] += np.dot(dzbeta_w, os[t].T)
deltas['bbeta_r'] += dzbeta_r
deltas['bbeta_w'] += dzbeta_w
sgsr = np.zeros((self.N, 3))
sgsw = np.zeros((self.N, 3))
for i in range(self.N):
sgsr[i, 1] = wg_rs[t][(i - 1) % self.N]
sgsr[i, 0] = wg_rs[t][i]
sgsr[i, 2] = wg_rs[t][(i + 1) % self.N]
sgsw[i, 1] = wg_ws[t][(i - 1) % self.N]
sgsw[i, 0] = wg_ws[t][i]
sgsw[i, 2] = wg_ws[t][(i + 1) % self.N]
ds_r = np.dot(sgsr.T, dws_r)
ds_w = np.dot(sgsw.T, dws_w)
shift_act_jac_r = np.zeros((3, 3))
shift_act_jac_w = np.zeros((3, 3))
bf = np.array([[1.0]])
for i in range(3):
for j in range(3):
shift_act_jac_r[i, j] = softmax_grads(
zs_rs[t], bf, i, j)
shift_act_jac_w[i, j] = softmax_grads(
zs_ws[t], bf, i, j)
dzs_r = np.dot(shift_act_jac_r.T, ds_r)
dzs_w = np.dot(shift_act_jac_w.T, ds_w)
deltas['os_r'] += np.dot(dzs_r, os[t].T)
deltas['os_w'] += np.dot(dzs_w, os[t].T)
deltas['bs_r'] += dzs_r
deltas['bs_w'] += dzs_w
else:
drs[t] = np.zeros_like(rs[0])
dmemtilde[t] = np.zeros_like(mems[0])
du_r[t] = np.zeros_like(mems[0])
du_w[t] = np.zeros_like(mems[0])
dwg_r[t] = np.zeros_like(w_rs[0])
dwg_w[t] = np.zeros_like(w_ws[0])
# o affects y through Woy
do = np.dot(params['oy'].T, dy)
if t < len(targets) - 1:
# and also zadd through Woadds
do += np.dot(params['oadds'].T, dzadd)
do += np.dot(params['oerases'].T, dzerase)
# and also through the keys
do += np.dot(params['ok_r'].T, dzk_r)
do += np.dot(params['ok_w'].T, dzk_w)
# and also through the interpolators
do += np.dot(params['og_r'].T, dzg_r)
do += np.dot(params['og_w'].T, dzg_w)
# and also through beta
do += np.dot(params['obeta_r'].T, dzbeta_r)
do += np.dot(params['obeta_w'].T, dzbeta_w)
# and also through the shift values
do += np.dot(params['os_r'].T, dzs_r)
do += np.dot(params['os_w'].T, dzs_w)
# compute deriv w.r.t. pre-activation of o
dzo = do * (1 - os[t] * os[t])
deltas['ho'] += np.dot(dzo, hs[t].T)
deltas['bo'] += dzo
# compute hidden dh
dh = np.dot(params['ho'].T, dzo)
# compute deriv w.r.t. pre-activation of h
dzh[t] = dh * (1 - hs[t] * hs[t])
deltas['xh'] += np.dot(dzh[t], xs[t].T)
deltas['bh'] += dzh[t]
# Wrh affects zh via rs[t-1]
deltas['rh'] += np.dot(dzh[t], rs[t - 1].reshape(
(self.M, 1)).T)
return deltas
def fprop(params):
"""
Forward pass of the NTM.
"""
W = params # aliasing for brevity
xs, zhs, hs, ys, ps, ts, zos, os = {}, {}, {}, {}, {}, {}, {}, {}
def l():
"""
Silly utility function that should be called in init.
"""
return [{} for _ in xrange(self.heads)]
rs = l()
zk_rs = l()
k_rs, beta_rs, g_rs, s_rs, gamma_rs = l(),l(),l(),l(),l()
k_ws, beta_ws, g_ws, s_ws, gamma_ws = l(),l(),l(),l(),l()
adds, erases = l(),l()
w_ws, w_rs = l(),l() # read weights and write weights
for idx in range(self.heads):
rs[idx][-1] = self.W['rsInit' + str(idx)] # stores values read from memory
w_ws[idx][-1] = softmax(self.W['w_wsInit' + str(idx)])
w_rs[idx][-1] = softmax(self.W['w_rsInit' + str(idx)])
mems = {} # the state of the memory at every timestep
mems[-1] = self.W['memsInit']
loss = 0
for t in xrange(len(inputs)):
xs[t] = np.reshape(np.array(inputs[t]),inputs[t].shape[::-1])
rsum = 0
for idx in range(self.heads):
rsum = rsum + np.dot(W['rh' + str(idx)], np.reshape(rs[idx][t-1],(self.M,1)))
zhs[t] = np.dot(W['xh'], xs[t]) + rsum + W['bh']
hs[t] = np.tanh(zhs[t])
zos[t] = np.dot(W['ho'], hs[t]) + W['bo']
os[t] = np.tanh(zos[t])
for idx in range(self.heads):
# parameters to the read head
zk_rs[idx][t] =np.dot(W['ok_r' + str(idx)],os[t]) + W['bk_r' + str(idx)]
k_rs[idx][t] = np.tanh(zk_rs[idx][t])
beta_rs[idx][t] = softplus(np.dot(W['obeta_r' + str(idx)],os[t])
+ W['bbeta_r' + str(idx)])
g_rs[idx][t] = sigmoid(np.dot(W['og_r' + str(idx)],os[t]) + W['bg_r' + str(idx)])
s_rs[idx][t] = softmax(np.dot(W['os_r' + str(idx)],os[t]) + W['bs_r' + str(idx)])
gamma_rs[idx][t] = 1 + sigmoid(np.dot(W['ogamma_r' + str(idx)], os[t])
+ W['bgamma_r' + str(idx)])
# parameters to the write head
k_ws[idx][t] = np.tanh(np.dot(W['ok_w' + str(idx)],os[t]) + W['bk_w' + str(idx)])
beta_ws[idx][t] = softplus(np.dot(W['obeta_w' + str(idx)], os[t])
+ W['bbeta_w' + str(idx)])
g_ws[idx][t] = sigmoid(np.dot(W['og_w' + str(idx)],os[t]) + W['bg_w' + str(idx)])
s_ws[idx][t] = softmax(np.dot(W['os_w' + str(idx)],os[t]) + W['bs_w' + str(idx)])
gamma_ws[idx][t] = 1 + sigmoid(np.dot(W['ogamma_w' + str(idx)], os[t])
+ W['bgamma_w' + str(idx)])
# the erase and add vectors
# these are also parameters to the write head
# but they describe "what" is to be written rather than "where"
adds[idx][t] = np.tanh(np.dot(W['oadds' + str(idx)], os[t]) + W['badds' + str(idx)])
erases[idx][t] = sigmoid(np.dot(W['oerases' + str(idx)], os[t]) + W['erases' + str(idx)])
w_ws[idx][t] = addressing.create_weights( k_ws[idx][t]
, beta_ws[idx][t]
, g_ws[idx][t]
, s_ws[idx][t]
, gamma_ws[idx][t]
, w_ws[idx][t-1]
, mems[t-1])
w_rs[idx][t] = addressing.create_weights( k_rs[idx][t]
, beta_rs[idx][t]
, g_rs[idx][t]
, s_rs[idx][t]
, gamma_rs[idx][t]
, w_rs[idx][t-1]
, mems[t-1])
ys[t] = np.dot(W['oy'], os[t]) + W['by']
ps[t] = sigmoid(ys[t])
one = np.ones(ps[t].shape)
ts[t] = np.reshape(np.array(targets[t]),(self.out_size,1))
epsilon = 2**-23 # to prevent log(0)
a = np.multiply(ts[t] , np.log2(ps[t] + epsilon))
b = np.multiply(one - ts[t], np.log2(one-ps[t] + epsilon))
loss = loss - (a + b)
for idx in range(self.heads):
# read from the memory
rs[idx][t] = memory.read(mems[t-1],w_rs[idx][t])
# write into the memory
mems[t] = memory.write(mems[t-1],w_ws[idx][t],erases[idx][t],adds[idx][t])
self.stats = [loss, mems, ps, ys, os, zos, hs, zhs, xs, rs, w_rs, w_ws, adds, erases]
return np.sum(loss)
def manual_grads(params):
"""
Compute the gradient of the loss WRT the parameters
Ordering of the operations is reverse of that in fprop()
"""
deltas = {}
for key, val in params.iteritems():
deltas[key] = np.zeros_like(val)
[loss, mems, ps, ys, os, zos, hs, zhs, xs, rs, w_rs,
w_ws, adds, erases, k_rs, k_ws, g_rs, g_ws, wc_rs, wc_ws,
zbeta_rs, zbeta_ws, zs_rs, zs_ws, wg_rs, wg_ws] = self.stats
dd = {}
drs = {}
dzh = {}
dmem = {} # might not need this, since we have dmemtilde
dmemtilde = {}
du_r = {}
du_w = {}
dwg_r = {}
dwg_w = {}
for t in reversed(xrange(len(targets))):
dy = np.copy(ps[t])
dy -= targets[t].T # backprop into y
deltas['oy'] += np.dot(dy, os[t].T)
deltas['by'] += dy
if t < len(targets) - 1:
# r[t] affects cost through zh[t+1] via Wrh
drs[t] = np.dot(self.W['rh'].T, dzh[t + 1])
# right now, mems[t] influences cost through rs[t+1], via w_rs[t+1]
dmem[t] = np.dot( w_rs[t + 1], drs[t + 1].reshape((self.M,1)).T )
# and also through mems at next step
W = np.reshape(w_ws[t+1], (w_ws[t+1].shape[0], 1))
E = np.reshape(erases[t+1], (erases[t+1].shape[0], 1))
WTE = np.dot(W, E.T)
KEEP = np.ones(mems[0].shape) - WTE
dmem[t] += np.multiply(dmemtilde[t+1], KEEP)
# and also through its influence on the content weighting next step
dmem[t] += du_r[t+1] + du_w[t+1]
dmemtilde[t] = dmem[t]
# erases[t] affects cost through mems[t], via w_ws[t]
derase = np.dot(np.multiply(dmemtilde[t], -mems[t-1]).T, w_ws[t])
# zerase affects just erases through a sigmoid
dzerase = derase * (erases[t] * (1 - erases[t]))
# adds[t] affects costs through mems[t], via w_ws
dadd = np.dot(dmem[t].T, w_ws[t])
# zadds affects just adds through a tanh
dzadd = dadd * (1 - adds[t] * adds[t])
# dbadds is just dzadds
deltas['badds'] += dzadd
deltas['oadds'] += np.dot(dzadd, os[t].T)
deltas['berases'] += dzerase
deltas['oerases'] += np.dot(dzerase, os[t].T)
# # read weights affect what is read, via what's in mems[t-1]
# dwc_r = np.dot(mems[t-1], drs[t])
# # write weights affect mem[t] through adding
# dwc_w = np.dot(dmem[t], adds[t])
# # they also affect memtilde[t] through erasing
# dwc_w += np.dot(np.multiply(dmemtilde[t], -mems[t-1]), erases[t])
dw_r = np.dot(mems[t-1], drs[t])
dw_r += dwg_r[t+1] * (1 - g_rs[t+1])
# write weights affect mem[t] through adding
dw_w = np.dot(dmem[t], adds[t])
# they also affect memtilde[t] through erasing
dw_w += np.dot(np.multiply(dmemtilde[t], -mems[t-1]), erases[t])
dw_w += dwg_w[t+1] * (1 - g_ws[t+1])
sgwr = np.zeros((self.N, self.N))
sgww = np.zeros((self.N, self.N))
for i in range(self.N):
sgwr[i,i] = softmax(zs_rs[t])[0]
sgwr[i,(i+1) % self.N] = softmax(zs_rs[t])[2]
sgwr[i,(i-1) % self.N] = softmax(zs_rs[t])[1]
sgww[i,i] = softmax(zs_ws[t])[0]
sgww[i,(i+1) % self.N] = softmax(zs_ws[t])[2]
sgww[i,(i-1) % self.N] = softmax(zs_ws[t])[1]
# right now, shifted weights are final weight
dws_r = dw_r
dws_w = dw_w
dwg_r[t] = np.dot(sgwr.T, dws_r)
dwg_w[t] = np.dot(sgww.T, dws_w)
dwc_r = dwg_r[t] * g_rs[t]
dwc_w = dwg_w[t] * g_ws[t]
"""
We need dw/dK
now w has N elts and K has N elts
and we want, for every elt of W, the grad of that elt w.r.t. each
of the N elts of K. that gives us N * N things
"""
# first, we must build up the K values (should be taken from fprop)
K_rs = []
K_ws = []
for i in range(self.N):
K_rs.append(cosine_sim(mems[t-1][i, :], k_rs[t]))
K_ws.append(cosine_sim(mems[t-1][i, :], k_ws[t]))
# then, we populate the grads
dwdK_r = np.zeros((self.N, self.N))
dwdK_w = np.zeros((self.N, self.N))
# for every row in the memory
for i in range(self.N):
# for every element in the weighting
for j in range(self.N):
dwdK_r[i,j] += softmax_grads(K_rs, softplus(zbeta_rs[t]), i, j)
dwdK_w[i,j] += softmax_grads(K_ws, softplus(zbeta_ws[t]), i, j)
# compute dK for all i in N
# K is the evaluated cosine similarity for the i-th row of mem matrix
dK_r = np.zeros_like(w_rs[0])
dK_w = np.zeros_like(w_ws[0])
# for all i in N (for every row that we've simmed)
for i in range(self.N):
# for every j in N (for every elt of the weighting)
for j in range(self.N):
# specifically, dwdK_r will change, and for write as well
dK_r[i] += dwc_r[j] * dwdK_r[i,j]
dK_w[i] += dwc_w[j] * dwdK_w[i,j]
"""
dK_r_dk_rs is a list of N things
each elt of the list corresponds to grads of K_idx
w.r.t. the key k_t
so it should be a length N list of M by 1 vectors
"""
dK_r_dk_rs = []
dK_r_dmem = []
for i in range(self.N):
# let k_rs be u, Mem[i] be v
u = np.reshape(k_rs[t], (self.M,))
v = mems[t-1][i, :]
dK_r_dk_rs.append( dKdu(u,v) )
dK_r_dmem.append( dKdu(v,u))
dK_w_dk_ws = []
dK_w_dmem = []
for i in range(self.N):
# let k_ws be u, Mem[i] be v
u = np.reshape(k_ws[t], (self.M,))
v = mems[t-1][i, :]
dK_w_dk_ws.append( dKdu(u,v) )
dK_w_dmem.append( dKdu(v,u))
# compute delta for keys
dk_r = np.zeros_like(k_rs[0])
dk_w = np.zeros_like(k_ws[0])
# for every one of M elt of dk_r
for i in range(self.M):
# for every one of the N Ks
for j in range(self.N):
# add delta K_r[j] * dK_r[j] / dk_r[i]
# add influence on through K_r[j]
dk_r[i] += dK_r[j] * dK_r_dk_rs[j][i]
dk_w[i] += dK_w[j] * dK_w_dk_ws[j][i]
# these represent influence of mem on next K
"""
Let's let du_r[t] represent the
influence of mems[t-1] on the cost through the K values
this is analogous to dk_w, but, k only every affects that
whereas mems[t-1] will also affect what is read at time t+1
and through memtilde at time t+1
"""
du_r[t] = np.zeros_like(mems[0])
du_w[t] = np.zeros_like(mems[0])
# for every row in mems[t-1]
for i in range(self.N):
# for every elt of this row (one of M)
for j in range(self.M):
du_r[t][i,j] = dK_r[i] * dK_r_dmem[i][j]
du_w[t][i,j] = dK_w[i] * dK_w_dmem[i][j]
# key values are activated as tanh
dzk_r = dk_r * (1 - k_rs[t] * k_rs[t])
dzk_w = dk_w * (1 - k_ws[t] * k_ws[t])
deltas['ok_r'] += np.dot(dzk_r, os[t].T)
deltas['ok_w'] += np.dot(dzk_w, os[t].T)
deltas['bk_r'] += dzk_r
deltas['bk_w'] += dzk_w
dg_r = np.dot(dwg_r[t].T, (wc_rs[t] - w_rs[t-1]) )
dg_w = np.dot(dwg_w[t].T, (wc_ws[t] - w_ws[t-1]) )
# compute dzg_r, dzg_w
dzg_r = dg_r * (g_rs[t] * (1 - g_rs[t]))
dzg_w = dg_w * (g_ws[t] * (1 - g_ws[t]))
deltas['og_r'] += np.dot(dzg_r, os[t].T)
deltas['og_w'] += np.dot(dzg_w, os[t].T)
deltas['bg_r'] += dzg_r
deltas['bg_w'] += dzg_w
# compute dbeta, which affects w_content through interaction with Ks
dwcdbeta_r = np.zeros_like(w_rs[0])
dwcdbeta_w = np.zeros_like(w_ws[0])
for i in range(self.N):
dwcdbeta_r[i] = beta_grads(K_rs, softplus(zbeta_rs[t]), i)
dwcdbeta_w[i] = beta_grads(K_ws, softplus(zbeta_ws[t]), i)
dbeta_r = np.zeros_like(zbeta_rs[0])
dbeta_w = np.zeros_like(zbeta_ws[0])
for i in range(self.N):
dbeta_r[0] += dwc_r[i] * dwcdbeta_r[i]
dbeta_w[0] += dwc_w[i] * dwcdbeta_w[i]
# beta is activated from zbeta by softplus, grad of which is sigmoid
dzbeta_r = dbeta_r * sigmoid(zbeta_rs[t])
dzbeta_w = dbeta_w * sigmoid(zbeta_ws[t])
deltas['obeta_r'] += np.dot(dzbeta_r, os[t].T)
deltas['obeta_w'] += np.dot(dzbeta_w, os[t].T)
deltas['bbeta_r'] += dzbeta_r
deltas['bbeta_w'] += dzbeta_w
sgsr = np.zeros((self.N, 3))
sgsw = np.zeros((self.N, 3))
for i in range(self.N):
sgsr[i,1] = wg_rs[t][(i - 1) % self.N]
sgsr[i,0] = wg_rs[t][i]
sgsr[i,2] = wg_rs[t][(i + 1) % self.N]
sgsw[i,1] = wg_ws[t][(i - 1) % self.N]
sgsw[i,0] = wg_ws[t][i]
sgsw[i,2] = wg_ws[t][(i + 1) % self.N]
ds_r = np.dot(sgsr.T, dws_r)
ds_w = np.dot(sgsw.T, dws_w)
shift_act_jac_r = np.zeros((3,3))
shift_act_jac_w = np.zeros((3,3))
bf = np.array([[1.0]])
for i in range(3):
for j in range(3):
shift_act_jac_r[i,j] = softmax_grads(zs_rs[t], bf, i, j)
shift_act_jac_w[i,j] = softmax_grads(zs_ws[t], bf, i, j)
dzs_r = np.dot(shift_act_jac_r.T, ds_r)
dzs_w = np.dot(shift_act_jac_w.T, ds_w)
deltas['os_r'] += np.dot(dzs_r, os[t].T)
deltas['os_w'] += np.dot(dzs_w, os[t].T)
deltas['bs_r'] += dzs_r
deltas['bs_w'] += dzs_w
else:
drs[t] = np.zeros_like(rs[0])
dmemtilde[t] = np.zeros_like(mems[0])
du_r[t] = np.zeros_like(mems[0])
du_w[t] = np.zeros_like(mems[0])
dwg_r[t] = np.zeros_like(w_rs[0])
dwg_w[t] = np.zeros_like(w_ws[0])
# o affects y through Woy
do = np.dot(params['oy'].T, dy)
if t < len(targets) - 1:
# and also zadd through Woadds
do += np.dot(params['oadds'].T, dzadd)
do += np.dot(params['oerases'].T, dzerase)
# and also through the keys
do += np.dot(params['ok_r'].T, dzk_r)
do += np.dot(params['ok_w'].T, dzk_w)
# and also through the interpolators
do += np.dot(params['og_r'].T, dzg_r)
do += np.dot(params['og_w'].T, dzg_w)
# and also through beta
do += np.dot(params['obeta_r'].T, dzbeta_r)
do += np.dot(params['obeta_w'].T, dzbeta_w)
# and also through the shift values
do += np.dot(params['os_r'].T, dzs_r)
do += np.dot(params['os_w'].T, dzs_w)
# compute deriv w.r.t. pre-activation of o
dzo = do * (1 - os[t] * os[t])
deltas['ho'] += np.dot(dzo, hs[t].T)
deltas['bo'] += dzo
# compute hidden dh
dh = np.dot(params['ho'].T, dzo)
# compute deriv w.r.t. pre-activation of h
dzh[t] = dh * (1 - hs[t] * hs[t])
deltas['xh'] += np.dot(dzh[t], xs[t].T)
deltas['bh'] += dzh[t]
# Wrh affects zh via rs[t-1]
deltas['rh'] += np.dot(dzh[t], rs[t-1].reshape((self.M, 1)).T)
return deltas
