def update_batch_cd1(para, data_v, layer=1):
eta = para['eta']
max_bsize = data_v.shape[0]
if layer == 0: # input layer, otherwise they are binary
data_h, gibbs_v, gibbs_h = sampling_nb(para, data_v)
else:
data_h, gibbs_v, gibbs_h = sampling(para, data_v)
pos_delta_w = np.zeros((para['v_num'], para['h_num']))
neg_delta_w = np.zeros((para['v_num'], para['h_num']))
for i in range(max_bsize):
pos_delta_w += matu.matrix_times(data_v[i], data_h[i])
neg_delta_w += matu.matrix_times(gibbs_v[i], gibbs_h[i])
delta_w_pos = eta * pos_delta_w/np.float(max_bsize)
delta_w_neg = eta * neg_delta_w/np.float(max_bsize)
para['w'] += delta_w_pos
para['w'] -= delta_w_neg
delta_a = data_v - gibbs_v
delta_b = data_h - gibbs_h
delta_a = eta * np.average(delta_a,0)
delta_b = eta * np.average(delta_b,0)
para['a'] += delta_a
para['b'] += delta_b
#print delta_w_pos.max(), delta_w_neg.max()
return para
def update_batch_cd1(para, data_v, layer=1):
eta = para['eta']
max_bsize = data_v.shape[0]
if layer == 0: # input layer, otherwise they are binary
data_h, gibbs_v, gibbs_h = sampling_nb(para, data_v)
else:
data_h, gibbs_v, gibbs_h = sampling(para, data_v)
pos_delta_w = np.zeros((para['v_num'], para['h_num']))
neg_delta_w = np.zeros((para['v_num'], para['h_num']))
for i in range(max_bsize):
pos_delta_w += matu.matrix_times(data_v[i], data_h[i])
neg_delta_w += matu.matrix_times(gibbs_v[i], gibbs_h[i])
delta_w_pos = eta * pos_delta_w / np.float(max_bsize)
delta_w_neg = eta * neg_delta_w / np.float(max_bsize)
para['w'] += delta_w_pos
para['w'] -= delta_w_neg
delta_a = data_v - gibbs_v
delta_b = data_h - gibbs_h
delta_a = eta * np.average(delta_a, 0)
delta_b = eta * np.average(delta_b, 0)
para['a'] += delta_a
para['b'] += delta_b
#print delta_w_pos.max(), delta_w_neg.max()
return para
def update_batch_cd1(para, data_v):
eta = para['eta']
max_bsize = data_v.shape[0]
data_h, gibbs_v, gibbs_h = sampling(para, data_v)
pos_delta_w = np.zeros((para['v_num'], para['h_num']))
neg_delta_w = np.zeros((para['v_num'], para['h_num']))
for i in range(max_bsize):
pos_delta_w += matu.matrix_times(data_v[i], data_h[i])
neg_delta_w += matu.matrix_times(gibbs_v[i], gibbs_h[i])
delta_w_pos = eta * pos_delta_w / np.float(max_bsize)
delta_w_neg = eta * neg_delta_w / np.float(max_bsize)
para['w'] += delta_w_pos
para['w'] -= delta_w_neg
#print delta_w_pos.max(), delta_w_neg.max()
return para
def update_batch_cd1(para, data_v):
eta = para['eta']
max_bsize = data_v.shape[0]
data_h, gibbs_v, gibbs_h = sampling(para, data_v)
pos_delta_w = np.zeros((para['v_num'], para['h_num']))
neg_delta_w = np.zeros((para['v_num'], para['h_num']))
for i in range(max_bsize):
pos_delta_w += matu.matrix_times(data_v[i], data_h[i])
neg_delta_w += matu.matrix_times(gibbs_v[i], gibbs_h[i])
delta_w_pos = eta * pos_delta_w/np.float(max_bsize)
delta_w_neg = eta * neg_delta_w/np.float(max_bsize)
para['w'] += delta_w_pos
para['w'] -= delta_w_neg
#print delta_w_pos.max(), delta_w_neg.max()
return para
def update_unbound_w(w_up, w_down, d_vis):
bsize = d_vis.shape[0]
delta_w = 0
d_hid = ReLU(d_vis, w_up)
g_vis = ReLU(d_hid, w_down)
for ib in range(bsize):
delta_w += matu.matrix_times(d_hid[ib], d_vis[ib] - g_vis[ib])
delta_w /= np.float(bsize)
return delta_w, d_hid
def update_unbound_w(w_up, w_down, d_vis):
bsize = d_vis.shape[0]
delta_w = 0
d_hid = ReLU(d_vis, w_up)
g_vis = ReLU(d_hid, w_down)
for ib in range(bsize):
delta_w += matu.matrix_times(d_hid[ib], d_vis[ib]-g_vis[ib])
delta_w /= np.float(bsize)
return delta_w, d_hid
def update_unbound_w(w_up, w_down, b_in, b_out, d_vis, layer=1):
bsize = d_vis.shape[0]
delta_w = 0
d_hid = sigmoid_sampling(d_vis, w_up, b_out)
if layer == 0:
g_vis = sigmoid(d_hid, w_down, b_in)
else:
g_vis = sigmoid_sampling(d_hid, w_down, b_in)
for ib in range(bsize):
delta_w += matu.matrix_times(d_hid[ib], d_vis[ib]-g_vis[ib])
delta_w = delta_w/np.float(bsize)
delta_b = np.average(d_vis[ib]-g_vis[ib], axis=0)
return delta_w, delta_b, d_hid
def update_unbound_w(w_up, w_down, b_in, b_out, d_vis, layer=1):
bsize = d_vis.shape[0]
delta_w = 0
d_hid = sigmoid_sampling(d_vis, w_up, b_out)
if layer == 0:
g_vis = sigmoid(d_hid, w_down, b_in)
else:
g_vis = sigmoid_sampling(d_hid, w_down, b_in)
for ib in range(bsize):
delta_w += matu.matrix_times(d_hid[ib], d_vis[ib] - g_vis[ib])
delta_w = delta_w / np.float(bsize)
delta_b = np.average(d_vis[ib] - g_vis[ib], axis=0)
return delta_w, delta_b, d_hid
