def recurrence(k, u_tm1, x_t):
bv_t = visible_bias_recurrence(u_tm1)
bh_t = hidden_bias_recurrence(u_tm1)
x_out = CRBM.gibbs_sample(x_t, W, bv_t, bh_t, k=5)
u_t = rnn_recurrence(u_tm1, x_out)
tf.assign(utm1, u_t)
cost = CRBM.free_energy_cost(x_t, x_out, W, bv_t, bh_t)
return u_t, x_out, cost
def recurrence(k, u_tm1, x_t, W, bh, bv, Wuh, Wuv, Wvu, Wuu, bu):
bv_t = visible_bias_recurrence(u_tm1, Wuv, bv)
bh_t = hidden_bias_recurrence(u_tm1, Wuh, bh)
x_out = CRBM.gibbs_sample(x_t, W, bv_t, bh_t, k=1)
u_t = rnn_recurrence(u_tm1, x_out, Wvu, Wuu, bu)
#cost = tf.losses.mean_squared_error(x_t,x_out)
cost = CRBM.free_energy_cost(x_t, x_out, W, bv_t, bh_t)
return u_t, x_out, cost
