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