How to use the cgt.core.infer_shape function in cgt
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joschu / cgt / examples / demo_neural_turing_machine.py View on Github 
gamma_bH = last_out[:,idx:idx+H]; idx += H
e_bhm = last_out[:,idx:idx+h*m]; idx += h*m; e_bhm = e_bhm.reshape([b,h,m])
a_bhm = last_out[:,idx:idx+h*m]; idx += h*m; a_bhm = a_bhm.reshape([b,h,m])
y_bp = last_out[:,idx:idx+p]; idx += p
k_bHm = cgt.tanh(k_bHm)
beta_bH = nn.softplus(beta_bH)
g_bH = cgt.sigmoid(g_bH)
s_bH3 = sum_normalize2(cgt.exp(s_bH3))
gamma_bH = cgt.sigmoid(gamma_bH)+1
e_bhm = cgt.sigmoid(e_bhm)
a_bhm = cgt.tanh(a_bhm)
# y_bp = y_bp
assert infer_shape(k_bHm) == (b,H,m)
assert infer_shape(beta_bH) == (b,H)
assert infer_shape(g_bH) == (b,H)
assert infer_shape(s_bH3) == (b,H,3)
assert infer_shape(gamma_bH) == (b,H)
assert infer_shape(e_bhm) == (b,h,m)
assert infer_shape(a_bhm) == (b,h,m)
assert infer_shape(y_bp) == (b,p)
return nn.Module([r_bhm, X_bk], [k_bHm, beta_bH, g_bH, s_bH3, gamma_bH, e_bhm, a_bhm, y_bp])y_bp = last_out[:,idx:idx+p]; idx += p
k_bHm = cgt.tanh(k_bHm)
beta_bH = nn.softplus(beta_bH)
g_bH = cgt.sigmoid(g_bH)
s_bH3 = sum_normalize2(cgt.exp(s_bH3))
gamma_bH = cgt.sigmoid(gamma_bH)+1
e_bhm = cgt.sigmoid(e_bhm)
a_bhm = cgt.tanh(a_bhm)
# y_bp = y_bp
assert infer_shape(k_bHm) == (b,H,m)
assert infer_shape(beta_bH) == (b,H)
assert infer_shape(g_bH) == (b,H)
assert infer_shape(s_bH3) == (b,H,3)
assert infer_shape(gamma_bH) == (b,H)
assert infer_shape(e_bhm) == (b,h,m)
assert infer_shape(a_bhm) == (b,h,m)
assert infer_shape(y_bp) == (b,p)
return nn.Module([r_bhm, X_bk], [k_bHm, beta_bH, g_bH, s_bH3, gamma_bH, e_bhm, a_bhm, y_bp])a_bhm = last_out[:,idx:idx+h*m]; idx += h*m; a_bhm = a_bhm.reshape([b,h,m])
y_bp = last_out[:,idx:idx+p]; idx += p
k_bHm = cgt.tanh(k_bHm)
beta_bH = nn.softplus(beta_bH)
g_bH = cgt.sigmoid(g_bH)
s_bH3 = sum_normalize2(cgt.exp(s_bH3))
gamma_bH = cgt.sigmoid(gamma_bH)+1
e_bhm = cgt.sigmoid(e_bhm)
a_bhm = cgt.tanh(a_bhm)
# y_bp = y_bp
assert infer_shape(k_bHm) == (b,H,m)
assert infer_shape(beta_bH) == (b,H)
assert infer_shape(g_bH) == (b,H)
assert infer_shape(s_bH3) == (b,H,3)
assert infer_shape(gamma_bH) == (b,H)
assert infer_shape(e_bhm) == (b,h,m)
assert infer_shape(a_bhm) == (b,h,m)
assert infer_shape(y_bp) == (b,p)
return nn.Module([r_bhm, X_bk], [k_bHm, beta_bH, g_bH, s_bH3, gamma_bH, e_bhm, a_bhm, y_bp])k_bHm = cgt.tanh(k_bHm)
beta_bH = nn.softplus(beta_bH)
g_bH = cgt.sigmoid(g_bH)
s_bH3 = sum_normalize2(cgt.exp(s_bH3))
gamma_bH = cgt.sigmoid(gamma_bH)+1
e_bhm = cgt.sigmoid(e_bhm)
a_bhm = cgt.tanh(a_bhm)
# y_bp = y_bp
assert infer_shape(k_bHm) == (b,H,m)
assert infer_shape(beta_bH) == (b,H)
assert infer_shape(g_bH) == (b,H)
assert infer_shape(s_bH3) == (b,H,3)
assert infer_shape(gamma_bH) == (b,H)
assert infer_shape(e_bhm) == (b,h,m)
assert infer_shape(a_bhm) == (b,h,m)
assert infer_shape(y_bp) == (b,p)
return nn.Module([r_bhm, X_bk], [k_bHm, beta_bH, g_bH, s_bH3, gamma_bH, e_bhm, a_bhm, y_bp])def ntm_address(opt, wprev_bhn, M_bnm, k_bhm, beta_bh, g_bh, s_bh3, gamma_bh):
# Content addressing
# Cosine similarity
# take inner product along memory axis k * M
numer_bhn = cgt.einsum("bhm,bnm->bhn", k_bhm, M_bnm)
# compute denominator |k| * |m|
denom_bhn = cgt.broadcast("*",
cgt.norm(k_bhm, axis=2, keepdims=True), # -> shape bh1
cgt.norm(M_bnm, axis=2, keepdims=True).transpose([0,2,1]), # -> bn1 -> b1n
"xx1,x1x"
)
csim_bhn = numer_bhn / denom_bhn
assert infer_shape(csim_bhn) == (opt.b, 2*opt.h, opt.n)
# scale by beta
tmp_bhn = cgt.broadcast("*", beta_bh[:,:,None], csim_bhn, "xx1,xxx")
wc_bhn = sum_normalize2(cgt.exp( tmp_bhn ))
# Interpolation
g_bh1 = g_bh[:,:,None]
wg_bhn = cgt.broadcast("*", wprev_bhn, (1 - g_bh1), "xxx,xx1") \
+ cgt.broadcast("*", wc_bhn, g_bh1, "xxx,xx1")
# Shift
wtil_bhn = circ_conv_1d(wg_bhn, s_bh3, axis=2)
# Sharpening
wfin_bhn = sum_normalize2(cgt.broadcast("**", wtil_bhn, gamma_bh.reshape([opt.b,2*opt.h,1]), "xxx,xx1"))
b,h,n = opt.b, 2*opt.h, opt.n
assert infer_shape(wtil_bhn) == (b,h,n)
assert infer_shape(gamma_bh) == (b,h)
assert infer_shape(gamma_bh[:,:,None]) == (b,h,1)assert infer_shape(csim_bhn) == (opt.b, 2*opt.h, opt.n)
# scale by beta
tmp_bhn = cgt.broadcast("*", beta_bh[:,:,None], csim_bhn, "xx1,xxx")
wc_bhn = sum_normalize2(cgt.exp( tmp_bhn ))
# Interpolation
g_bh1 = g_bh[:,:,None]
wg_bhn = cgt.broadcast("*", wprev_bhn, (1 - g_bh1), "xxx,xx1") \
+ cgt.broadcast("*", wc_bhn, g_bh1, "xxx,xx1")
# Shift
wtil_bhn = circ_conv_1d(wg_bhn, s_bh3, axis=2)
# Sharpening
wfin_bhn = sum_normalize2(cgt.broadcast("**", wtil_bhn, gamma_bh.reshape([opt.b,2*opt.h,1]), "xxx,xx1"))
b,h,n = opt.b, 2*opt.h, opt.n
assert infer_shape(wtil_bhn) == (b,h,n)
assert infer_shape(gamma_bh) == (b,h)
assert infer_shape(gamma_bh[:,:,None]) == (b,h,1)
return wfin_bhnbeta_bH = nn.softplus(beta_bH)
g_bH = cgt.sigmoid(g_bH)
s_bH3 = sum_normalize2(cgt.exp(s_bH3))
gamma_bH = cgt.sigmoid(gamma_bH)+1
e_bhm = cgt.sigmoid(e_bhm)
a_bhm = cgt.tanh(a_bhm)
# y_bp = y_bp
assert infer_shape(k_bHm) == (b,H,m)
assert infer_shape(beta_bH) == (b,H)
assert infer_shape(g_bH) == (b,H)
assert infer_shape(s_bH3) == (b,H,3)
assert infer_shape(gamma_bH) == (b,H)
assert infer_shape(e_bhm) == (b,h,m)
assert infer_shape(a_bhm) == (b,h,m)
assert infer_shape(y_bp) == (b,p)
return nn.Module([r_bhm, X_bk], [k_bHm, beta_bH, g_bH, s_bH3, gamma_bH, e_bhm, a_bhm, y_bp])csim_bhn = numer_bhn / denom_bhn
assert infer_shape(csim_bhn) == (opt.b, 2*opt.h, opt.n)
# scale by beta
tmp_bhn = cgt.broadcast("*", beta_bh[:,:,None], csim_bhn, "xx1,xxx")
wc_bhn = sum_normalize2(cgt.exp( tmp_bhn ))
# Interpolation
g_bh1 = g_bh[:,:,None]
wg_bhn = cgt.broadcast("*", wprev_bhn, (1 - g_bh1), "xxx,xx1") \
+ cgt.broadcast("*", wc_bhn, g_bh1, "xxx,xx1")
# Shift
wtil_bhn = circ_conv_1d(wg_bhn, s_bh3, axis=2)
# Sharpening
wfin_bhn = sum_normalize2(cgt.broadcast("**", wtil_bhn, gamma_bh.reshape([opt.b,2*opt.h,1]), "xxx,xx1"))
b,h,n = opt.b, 2*opt.h, opt.n
assert infer_shape(wtil_bhn) == (b,h,n)
assert infer_shape(gamma_bh) == (b,h)
assert infer_shape(gamma_bh[:,:,None]) == (b,h,1)
return wfin_bhnk_bHm = cgt.tanh(k_bHm)
beta_bH = nn.softplus(beta_bH)
g_bH = cgt.sigmoid(g_bH)
s_bH3 = sum_normalize2(cgt.exp(s_bH3))
gamma_bH = cgt.sigmoid(gamma_bH)+1
e_bhm = cgt.sigmoid(e_bhm)
a_bhm = cgt.tanh(a_bhm)
# y_bp = y_bp
assert infer_shape(k_bHm) == (b,H,m)
assert infer_shape(beta_bH) == (b,H)
assert infer_shape(g_bH) == (b,H)
assert infer_shape(s_bH3) == (b,H,3)
assert infer_shape(gamma_bH) == (b,H)
assert infer_shape(e_bhm) == (b,h,m)
assert infer_shape(a_bhm) == (b,h,m)
assert infer_shape(y_bp) == (b,p)
return nn.Module([r_bhm, X_bk], [k_bHm, beta_bH, g_bH, s_bH3, gamma_bH, e_bhm, a_bhm, y_bp])
