def t_bck_hid(t_params, L, P):
result = 0
for l in range(1, L - 1):
result += t_bh_delta(t_params, P[l], P[l + 1]) + \
sequential.t_b_updbias(t_params[2], t_params[3], P[l]) + \
sequential.t_b_updweights(t_params[2], t_params[3], P[l - 1], P[l])
return result
def t_bck_hid(m_params, n_threads, L, P):
result = 0
for l in range(1, L - 1):
result += t_bh_delta(m_params, n_threads, P[l], P[l + 1]) + \
sequential.t_b_updbias(m_params[1][2], m_params[1][3], P[l]) + \
sequential.t_b_updweights(m_params[1][2], m_params[1][3], P[l - 1], P[l])
return result
def t_bck_out(m_params, n_threads, L, P):
return sequential.t_bo_delta(
m_params[n_threads][2],
m_params[n_threads][3], P[L - 1]) + sequential.t_b_updbias(
m_params[1][2],
m_params[1][3], P[L - 1]) + sequential.t_b_updweights(
m_params[1][2], m_params[1][3], P[L - 2], P[L - 1])
def t_bck_hid(pi, beta, pi_simd, beta_simd, L, P):
result = 0
for l in range(1, L - 1):
result += t_bh_delta(pi, beta, pi_simd, beta_simd, P[l], P[l + 1]) + \
sequential.t_b_updbias(pi_simd, beta_simd, P[l]) + \
sequential.t_b_updweights(pi_simd, beta_simd, P[l - 1], P[l])
return result
def t_bck_hid(pi_1, beta_1, pi_C, beta_C, L, P):
result = 0
for l in range(1, L - 1):
result += sequential.t_bh_delta(
pi_C, beta_C, P[l], P[l + 1]) + sequential.t_b_updbias(
pi_1, beta_1, P[l]) + sequential.t_b_updweights(
pi_1, beta_1, P[l - 1], P[l])
return result
def t_bck_out(pi_1, beta_1, pi_C, beta_C, L, P):
return sequential.t_bo_delta(
pi_C, beta_C, P[L - 1]) + sequential.t_b_updbias(
pi_1, beta_1, P[L - 1]) + sequential.t_b_updweights(
pi_1, beta_1, P[L - 2], P[L - 1])