def rnnrbm():
#This function builds the RNN-RBM and returns the parameters of the model
x = tf.placeholder(
tf.float32,
[None, n_visible]) #The placeholder variable that holds our data
lr = tf.placeholder(
tf.float32
) #The learning rate. We set and change this value during training.
size_bt = tf.shape(x)[
0] # Not really batch, but the longeur of time series
#parameters to learn, we find that except W, the other four Weight matrices aren't well learned,
#So there isn't a good evolution of RBM in time. We can train a single RBM and produce a similar result.
W = tf.Variable(tf.zeros([n_visible, n_hidden]), name="W")
Wuh = tf.Variable(tf.zeros([n_hidden_recurrent, n_hidden]), name="Wuh")
Wuv = tf.Variable(tf.zeros([n_hidden_recurrent, n_visible]), name="Wuv")
Wvu = tf.Variable(tf.zeros([n_visible, n_hidden_recurrent]), name="Wvu")
Wuu = tf.Variable(tf.zeros([n_hidden_recurrent, n_hidden_recurrent]),
name="Wuu")
bh = tf.Variable(
tf.zeros([1, n_hidden]), name="bh"
) # bh is altually bh_0, we consider only bh_0 as parameters for training .
bv = tf.Variable(tf.zeros([1, n_visible]), name="bv")
bu = tf.Variable(tf.zeros([1, n_hidden_recurrent]), name="bu")
u0 = tf.Variable(tf.zeros([1, n_hidden_recurrent]), name="u0")
BH_t = tf.Variable(tf.zeros([1, n_hidden]), name="BH_t")
BV_t = tf.Variable(tf.zeros([1, n_visible]), name="BV_t")
def recurrence(nest, v_t):
# This function do all the procedures in one RBM.
bv_t = tf.add(bv,
tf.matmul(nest[0],
Wuv)) # generate current bv_t based on u_{t-1}
bh_t = tf.add(bh,
tf.matmul(nest[0],
Wuh)) # generate current bh_t based on u_{t-1}
v_t_sample = RBM.gibbs_sample(
tf.reshape(v_t, [1, n_visible]), W, bv_t, bh_t,
k=1) # sample v_t according generated bias
v_t_sample = tf.reshape(v_t_sample, [1, n_visible])
u_t = tf.sigmoid(bu + tf.matmul(v_t_sample, Wvu) +
tf.matmul(nest[0], Wuu)) # calculate current u_t
return [u_t, bv_t, bh_t, v_t_sample]
Uarr, BV_t, BH_t, V_t_sample = tf.scan(recurrence,
x,
initializer=[
u0,
tf.zeros([1, n_visible]),
tf.zeros([1, n_hidden]),
tf.zeros([1, n_visible])
])
BV_t = tf.reshape(BV_t, [size_bt, n_visible])
BH_t = tf.reshape(BH_t, [size_bt, n_hidden])
V_t_sample = tf.reshape(V_t_sample, [size_bt, n_visible])
#Get the free energy cost from each of the RBMs
cost = tf.reduce_mean(
tf.subtract(RBM.F(x, W, BV_t, BH_t), RBM.F(V_t_sample, W, BV_t, BH_t)))
def reconstruction():
#This function handles reconstructing de trajectory. This function is one of the outputs of the rnnrbm() function
primer = x
Uarr, BV_t, BH_t, V_t_sample = tf.scan(
recurrence, x, initializer=[u0, bv, bh,
tf.zeros([1, n_visible])])
traj = tf.reshape(V_t_sample, [size_bt, n_visible])
return traj
return x, cost, reconstruction, W, Wuh, Wuv, Wvu, Wuu, bh, bv, bu, lr, u0
