def cost(self, X):
if len(X) != 4:
raise ValueError("The number of inputs does not match.")
cost = GMM(X[0], X[1], X[2], X[3])
if self.use_sum:
return cost.sum()
else:
return cost.mean()
def cost(self, X):
if len(X) != 4:
raise ValueError("The number of inputs does not match.")
cost = GMM(X[0], X[1], X[2], X[3])
if self.use_sum:
return cost.sum()
else:
return cost.mean()
((s_t, s_t_is, kl_t, theta_mu_t, theta_sig_t, coeff_t, mll), updates) =\
theano.scan(fn=inner_fn,
sequences=[x],
outputs_info=[main_lstm.get_init_state(batch_size),
main_lstm.get_init_state(batch_size*num_sample),
None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
reshaped_x = x.reshape((x.shape[0]*x.shape[1], -1))
reshaped_theta_mu = theta_mu_t.reshape((theta_mu_t.shape[0]*theta_mu_t.shape[1], -1))
reshaped_theta_sig = theta_sig_t.reshape((theta_sig_t.shape[0]*theta_sig_t.shape[1], -1))
reshaped_coeff = coeff_t.reshape((coeff_t.shape[0]*coeff_t.shape[1], -1))
recon = GMM(reshaped_x, reshaped_theta_mu, reshaped_theta_sig, reshaped_coeff)
recon = recon.reshape((theta_mu_t.shape[0], theta_mu_t.shape[1]))
recon_term = recon.sum(axis=0).mean()
kl_term = kl_t.sum(axis=0).mean()
nll_lower_bound = recon_term + kl_term
nll_lower_bound.name = 'nll_lower_bound'
mll = -mll.sum(axis=0).mean()
mll.name = 'marginal_nll'
outputs = [mll, nll_lower_bound]
monitor_fn = theano.function(inputs=[x],
outputs=outputs,
on_unused_input='ignore',
allow_input_downcast=True)
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'valid_nll_upper_bound'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
debug = int(args['debug'])
num_sequences_per_batch = int(args['numSequences']) #based on appliance
loadParam = args['loadAsKelly']
target_inclusion_prob = float(args['target_inclusion_prob'])
loadAsKelly = True
if (loadParam == 'N' or loadParam == 'n' or loadParam == 'no'
or loadParam == 'NO' or loadParam == 'No'):
loadAsKelly = False
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 100 #150
p_z_dim = 60 #150
p_x_dim = 20 #250
x2s_dim = 40 #250
z2s_dim = 40 #150
target_dim = k #x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, reader = fetch_ukdale(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
flgAggSumScaled=1,
flgFilterZeros=1,
isKelly=loadAsKelly,
seq_per_batch=num_sequences_per_batch,
target_inclusion_prob=target_inclusion_prob)
instancesPlot = {
0: [4, 20],
2: [5, 10]
} #for now use hard coded instancesPlot for kelly sampling
if (not loadAsKelly):
instancesPlot = reader.build_dict_instances_plot(
listDates, batch_size, Xval.shape[0])
train_data = UKdale(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
'''
dissag_pred = FullyConnectedLayer(name='disag_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=num_apps,
unit='relu',
init_W=init_W,
init_b=init_b)
'''
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
def inner_fn(x_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_t, z_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred #, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
s_temp = concatenate(
[s_0[None, :, :], s_temp[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
'''
theta_1_temp = theta_1.fprop([z_1_temp, s_temp], params)
theta_mu_temp = theta_mu.fprop([theta_1_temp], params)
theta_sig_temp = theta_sig.fprop([theta_1_temp], params)
coeff_temp = coeff.fprop([theta_1_temp], params)
corr_temp = corr.fprop([theta_1_temp], params)
binary_temp = binary.fprop([theta_1_temp], params)
'''
s_temp.name = 'h_1' #gisse
z_1_temp.name = 'z_1' #gisse
z_t_temp.name = 'z'
theta_mu_temp.name = 'theta_mu_temp'
theta_sig_temp.name = 'theta_sig_temp'
coeff_temp.name = 'coeff'
#corr_temp.name = 'corr'
#binary_temp.name = 'binary'
if (flgAgg == -1):
prediction.name = 'x_reconstructed'
mse = T.mean((prediction - x)**2) # CHECK RESHAPE with an assertion
mae = T.mean(T.abs(prediction - x))
mse.name = 'mse'
pred_in = x.reshape((x_shape[0] * x_shape[1], -1))
else:
prediction.name = 'pred_' + str(flgAgg)
#[:,:,flgAgg].reshape((y.shape[0],y.shape[1],1)
mse = T.mean(
(prediction - y)**2) # As axis = None is calculated for all
mae = T.mean(T.abs_(prediction - y))
mse.name = 'mse'
mae.name = 'mae'
pred_in = y.reshape((y.shape[0] * y.shape[1], -1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
theta_mu_in = theta_mu_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0] * x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(
pred_in, theta_mu_in, theta_sig_in, coeff_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
#recon = recon * mask
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
#kl_temp = kl_temp * mask
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
max_x = x.max()
mean_x = x.mean()
min_x = x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
max_theta_mu = theta_mu_in.max()
mean_theta_mu = theta_mu_in.mean()
min_theta_mu = theta_mu_in.min()
max_theta_mu.name = 'max_theta_mu'
mean_theta_mu.name = 'mean_theta_mu'
min_theta_mu.name = 'min_theta_mu'
max_theta_sig = theta_sig_in.max()
mean_theta_sig = theta_sig_in.mean()
min_theta_sig = theta_sig_in.min()
max_theta_sig.name = 'max_theta_sig'
mean_theta_sig.name = 'mean_theta_sig'
min_theta_sig.name = 'min_theta_sig'
coeff_max = coeff_in.max()
coeff_min = coeff_in.min()
coeff_mean_max = coeff_in.mean(axis=0).max()
coeff_mean_min = coeff_in.mean(axis=0).min()
coeff_max.name = 'coeff_max'
coeff_min.name = 'coeff_min'
coeff_mean_max.name = 'coeff_mean_max'
coeff_mean_min.name = 'coeff_mean_min'
max_phi_sig = phi_sig_temp.max()
mean_phi_sig = phi_sig_temp.mean()
min_phi_sig = phi_sig_temp.min()
max_phi_sig.name = 'max_phi_sig'
mean_phi_sig.name = 'mean_phi_sig'
min_phi_sig.name = 'min_phi_sig'
max_prior_sig = prior_sig_temp.max()
mean_prior_sig = prior_sig_temp.mean()
min_prior_sig = prior_sig_temp.min()
max_prior_sig.name = 'max_prior_sig'
mean_prior_sig.name = 'mean_prior_sig'
min_prior_sig.name = 'min_prior_sig'
model.inputs = [x, mask, y, y_mask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch),
Monitoring(
freq=monitoring_freq,
ddout=[
nll_upper_bound,
recon_term,
kl_term,
mse,
mae,
theta_mu_temp,
theta_sig_temp,
z_t_temp,
prediction, #corr_temp, binary_temp,
s_temp,
z_1_temp
],
indexSep=5,
indexDDoutPlot=[(0, theta_mu_temp), (2, z_t_temp),
(3, prediction)],
instancesPlot=instancesPlot, #{0:[4,20],2:[5,10]},#, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr, 15: (lr / 10), 70: (lr / 100)}
mainloop = Training(name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
extension=extension,
lr_iterations=lr_iterations)
#mainloop.run()
fLog = open(save_path + '/output.csv', 'w')
fLog.write("log,kl,nll_upper_bound,mse,mae\n")
for i, item in enumerate(mainloop.trainlog.monitor['nll_upper_bound']):
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
c = mainloop.trainlog.monitor['nll_upper_bound'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{},{},{},{},{}\n".format(a, b, c, d, e))
def main(args):
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'valid_nll_upper_bound'
data_path = args['data_path']
save_path = args['save_path']
monitoring_freq = int(args['monitoring_freq'])
force_saving_freq = int(args['force_saving_freq'])
reset_freq = int(args['reset_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
m_batch_size = int(args['m_batch_size'])
x_dim = int(args['x_dim'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k'])
lr = float(args['lr'])
debug = int(args['debug'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 500
p_z_dim = 500
p_x_dim = 500
x2s_dim = 500
z2s_dim = 500
target_dim = x_dim * k
file_name = 'blizzard_unseg_tbptt'
normal_params = np.load(data_path + file_name + '_normal.npz')
X_mean = normal_params['X_mean']
X_std = normal_params['X_std']
model = Model()
train_data = Blizzard_tbptt(name='train',
path=data_path,
frame_size=x_dim,
file_name=file_name,
X_mean=X_mean,
X_std=X_std)
valid_data = Blizzard_tbptt(name='valid',
path=data_path,
frame_size=x_dim,
file_name=file_name,
X_mean=X_mean,
X_std=X_std)
x = train_data.theano_vars()
m_x = valid_data.theano_vars()
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=theano.config.floatX)
m_x.tag.test_value = np.zeros((15, m_batch_size, x_dim), dtype=theano.config.floatX)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
x_2 = FullyConnectedLayer(name='x_2',
parent=['x_1'],
parent_dim=[x2s_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
x_3 = FullyConnectedLayer(name='x_3',
parent=['x_2'],
parent_dim=[x2s_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
x_4 = FullyConnectedLayer(name='x_4',
parent=['x_3'],
parent_dim=[x2s_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_2 = FullyConnectedLayer(name='z_2',
parent=['z_1'],
parent_dim=[z2s_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_3 = FullyConnectedLayer(name='z_3',
parent=['z_2'],
parent_dim=[z2s_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_4 = FullyConnectedLayer(name='z_4',
parent=['z_3'],
parent_dim=[z2s_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_4', 'z_4'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_4', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_2 = FullyConnectedLayer(name='phi_2',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_3 = FullyConnectedLayer(name='phi_3',
parent=['phi_2'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_4 = FullyConnectedLayer(name='phi_4',
parent=['phi_3'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_4'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_4'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_2 = FullyConnectedLayer(name='prior_2',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_3 = FullyConnectedLayer(name='prior_3',
parent=['prior_2'],
parent_dim=[p_z_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_4 = FullyConnectedLayer(name='prior_4',
parent=['prior_3'],
parent_dim=[p_z_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_4'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_4'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_4', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_2 = FullyConnectedLayer(name='theta_2',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_3 = FullyConnectedLayer(name='theta_3',
parent=['theta_2'],
parent_dim=[p_x_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_4 = FullyConnectedLayer(name='theta_4',
parent=['theta_3'],
parent_dim=[p_x_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_4'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_4'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_4'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
nodes = [rnn,
x_1, x_2, x_3, x_4,
z_1, z_2, z_3, z_4,
phi_1, phi_2, phi_3, phi_4, phi_mu, phi_sig,
prior_1, prior_2, prior_3, prior_4, prior_mu, prior_sig,
theta_1, theta_2, theta_3, theta_4, theta_mu, theta_sig, coeff]
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
step_count = sharedX(0, name='step_count')
last_rnn = np.zeros((batch_size, rnn_dim*2), dtype=theano.config.floatX)
rnn_tm1 = sharedX(last_rnn, name='rnn_tm1')
shared_updates = OrderedDict()
shared_updates[step_count] = step_count + 1
s_0 = T.switch(T.eq(T.mod(step_count, reset_freq), 0),
rnn.get_init_state(batch_size), rnn_tm1)
x_shape = x.shape
x_in = x.reshape((x_shape[0]*x_shape[1], -1))
x_1_in = x_1.fprop([x_in], params)
x_2_in = x_2.fprop([x_1_in], params)
x_3_in = x_3.fprop([x_2_in], params)
x_4_in = x_4.fprop([x_3_in], params)
x_4_in = x_4_in.reshape((x_shape[0], x_shape[1], -1))
def inner_fn(x_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_2_t = phi_2.fprop([phi_1_t], params)
phi_3_t = phi_3.fprop([phi_2_t], params)
phi_4_t = phi_4.fprop([phi_3_t], params)
phi_mu_t = phi_mu.fprop([phi_4_t], params)
phi_sig_t = phi_sig.fprop([phi_4_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_2_t = prior_2.fprop([prior_1_t], params)
prior_3_t = prior_3.fprop([prior_2_t], params)
prior_4_t = prior_4.fprop([prior_3_t], params)
prior_mu_t = prior_mu.fprop([prior_4_t], params)
prior_sig_t = prior_sig.fprop([prior_4_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
z_2_t = z_2.fprop([z_1_t], params)
z_3_t = z_3.fprop([z_2_t], params)
z_4_t = z_4.fprop([z_3_t], params)
s_t = rnn.fprop([[x_t, z_4_t], [s_tm1]], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_4_t
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp, z_4_temp), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_4_in],
outputs_info=[s_0, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
shared_updates[rnn_tm1] = s_temp[-1]
s_temp = concatenate([s_0[None, :, :], s_temp[:-1]], axis=0)
theta_1_temp = theta_1.fprop([z_4_temp, s_temp], params)
theta_2_temp = theta_2.fprop([theta_1_temp], params)
theta_3_temp = theta_3.fprop([theta_2_temp], params)
theta_4_temp = theta_4.fprop([theta_3_temp], params)
theta_mu_temp = theta_mu.fprop([theta_4_temp], params)
theta_sig_temp = theta_sig.fprop([theta_4_temp], params)
coeff_temp = coeff.fprop([theta_4_temp], params)
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp)
x_shape = x.shape
x_in = x.reshape((x_shape[0]*x_shape[1], -1))
theta_mu_in = theta_mu_temp.reshape((x_shape[0]*x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0]*x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(x_in, theta_mu_in, theta_sig_in, coeff_in)
recon_term = recon.mean()
kl_term = kl_temp.mean()
nll_upper_bound = recon_term + kl_term
nll_upper_bound.name = 'nll_upper_bound'
m_x_1_temp = x_1.fprop([m_x], params)
m_x_2_temp = x_2.fprop([m_x_1_temp], params)
m_x_3_temp = x_3.fprop([m_x_2_temp], params)
m_x_4_temp = x_4.fprop([m_x_3_temp], params)
m_s_0 = rnn.get_init_state(m_batch_size)
((m_s_temp, m_phi_mu_temp, m_phi_sig_temp, m_prior_mu_temp, m_prior_sig_temp, m_z_4_temp), m_updates) =\
theano.scan(fn=inner_fn,
sequences=[m_x_4_temp],
outputs_info=[m_s_0, None, None, None, None, None])
for k, v in m_updates.iteritems():
k.default_update = v
m_s_temp = concatenate([m_s_0[None, :, :], m_s_temp[:-1]], axis=0)
m_theta_1_temp = theta_1.fprop([m_z_4_temp, m_s_temp], params)
m_theta_2_temp = theta_2.fprop([m_theta_1_temp], params)
m_theta_3_temp = theta_3.fprop([m_theta_2_temp], params)
m_theta_4_temp = theta_4.fprop([m_theta_3_temp], params)
m_theta_mu_temp = theta_mu.fprop([m_theta_4_temp], params)
m_theta_sig_temp = theta_sig.fprop([m_theta_4_temp], params)
m_coeff_temp = coeff.fprop([m_theta_4_temp], params)
m_kl_temp = KLGaussianGaussian(m_phi_mu_temp, m_phi_sig_temp, m_prior_mu_temp, m_prior_sig_temp)
m_x_shape = m_x.shape
m_x_in = m_x.reshape((m_x_shape[0]*m_x_shape[1], -1))
m_theta_mu_in = m_theta_mu_temp.reshape((m_x_shape[0]*m_x_shape[1], -1))
m_theta_sig_in = m_theta_sig_temp.reshape((m_x_shape[0]*m_x_shape[1], -1))
m_coeff_in = m_coeff_temp.reshape((m_x_shape[0]*m_x_shape[1], -1))
m_recon = GMM(m_x_in, m_theta_mu_in, m_theta_sig_in, m_coeff_in)
m_recon_term = m_recon.mean()
m_kl_term = m_kl_temp.mean()
m_nll_upper_bound = m_recon_term + m_kl_term
m_nll_upper_bound.name = 'nll_upper_bound'
m_recon_term.name = 'recon_term'
m_kl_term.name = 'kl_term'
max_x = m_x.max()
mean_x = m_x.mean()
min_x = m_x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
max_theta_mu = m_theta_mu_in.max()
mean_theta_mu = m_theta_mu_in.mean()
min_theta_mu = m_theta_mu_in.min()
max_theta_mu.name = 'max_theta_mu'
mean_theta_mu.name = 'mean_theta_mu'
min_theta_mu.name = 'min_theta_mu'
max_theta_sig = m_theta_sig_in.max()
mean_theta_sig = m_theta_sig_in.mean()
min_theta_sig = m_theta_sig_in.min()
max_theta_sig.name = 'max_theta_sig'
mean_theta_sig.name = 'mean_theta_sig'
min_theta_sig.name = 'min_theta_sig'
max_phi_sig = m_phi_sig_temp.max()
mean_phi_sig = m_phi_sig_temp.mean()
min_phi_sig = m_phi_sig_temp.min()
max_phi_sig.name = 'max_phi_sig'
mean_phi_sig.name = 'mean_phi_sig'
min_phi_sig.name = 'min_phi_sig'
max_prior_sig = m_prior_sig_temp.max()
mean_prior_sig = m_prior_sig_temp.mean()
min_prior_sig = m_prior_sig_temp.min()
max_prior_sig.name = 'max_prior_sig'
mean_prior_sig.name = 'mean_prior_sig'
min_prior_sig.name = 'min_prior_sig'
model.inputs = [x]
model.params = params
model.nodes = nodes
model.set_updates(shared_updates)
optimizer = Adam(
lr=lr
)
monitor_fn = theano.function(inputs=[m_x],
outputs=[m_nll_upper_bound, m_recon_term, m_kl_term,
max_phi_sig, mean_phi_sig, min_phi_sig,
max_prior_sig, mean_prior_sig, min_prior_sig,
max_theta_sig, mean_theta_sig, min_theta_sig,
max_x, mean_x, min_x,
max_theta_mu, mean_theta_mu, min_theta_mu],
on_unused_input='ignore')
extension = [
GradientClipping(batch_size=batch_size, check_nan=1),
EpochCount(epoch),
Monitoring(freq=monitoring_freq,
monitor_fn=monitor_fn,
ddout=[m_nll_upper_bound, m_recon_term, m_kl_term,
max_phi_sig, mean_phi_sig, min_phi_sig,
max_prior_sig, mean_prior_sig, min_prior_sig,
max_theta_sig, mean_theta_sig, min_theta_sig,
max_x, mean_x, min_x,
max_theta_mu, mean_theta_mu, min_theta_mu],
data=[Iterator(train_data, m_batch_size, start=0, end=112640),
Iterator(valid_data, m_batch_size, start=2040064, end=2152704)]),
Picklize(freq=monitoring_freq, force_save_freq=force_saving_freq, path=save_path),
EarlyStopping(freq=monitoring_freq, force_save_freq=force_saving_freq, path=save_path, channel=channel_name),
WeightNorm()
]
mainloop = Training(
name=pkl_name,
data=Iterator(train_data, batch_size, start=0, end=2040064),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
extension=extension
)
mainloop.run()
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'dp_dis1-sch_%d' % trial
channel_name = 'mae'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
typeLoad = int(args['typeLoad'])
debug = int(args['debug'])
kSchedSamp = int(args['kSchedSamp'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 150
p_z_dim = 150
p_x_dim = 150 #250
x2s_dim = 100 #250
y2s_dim = 100
z2s_dim = 100 #150
target_dim = k #x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_dataport(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
trainPer=0.6,
valPer=0.2,
testPer=0.2,
typeLoad=typeLoad,
flgAggSumScaled=1,
flgFilterZeros=1)
print(reader.stdTrain, reader.meanTrain)
instancesPlot = {
0: [4],
2: [10]
} #for now use hard coded instancesPlot for kelly sampling
train_data = Dataport(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = Dataport(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
test_data = Dataport(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xtest,
labels=ytest)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
scheduleSamplingMask = T.fvector('schedMask')
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
""" print (mainloop)
attrs = vars(mainloop)
print ', '.join("%s: %s" % item for item in attrs.items())
print (type(mainloop.model.nodes[1]))
print (type(mainloop.model.params))"""
#for node in mainloop.model.nodes:
# print(node.name)
'''
for node in mainloop.model.nodes:
print("Name:", node.name, "Parent:", node.parent, "Unit:",node.unit,"init_W:", node.init_W, "init_b:", node.init_b)
for param in mainloop.model.params:
print(type(param),param)
'''
"""rnn = LSTM(name='rnn',
parent=['x_1', 'z_1','y_1'],
parent_dim=[x2s_dim, z2s_dim, y_dim],
nout=rnn_dim,
unit='tanh',
init_W=mainloop.model.nodes[0].init_W,
init_U=mainloop.model.nodes[0].init_U,
init_b=mainloop.model.nodes[0].init_b)
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=mainloop.model.nodes[1].init_W,
init_b=mainloop.model.nodes[1].init_b)
y_1 = FullyConnectedLayer(name='y_1',
parent=['y_t'],
parent_dim=[y_dim],
nout=y2s_dim,
unit='relu',
init_W=mainloop.model.nodes[2].init_W,
init_b=mainloop.model.nodes[2].init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=mainloop.model.nodes[3].init_W,
init_b=mainloop.model.nodes[3].init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1','y_1'],
parent_dim=[x2s_dim, rnn_dim,y2s_dim],
nout=q_z_dim,
unit='relu',
init_W=mainloop.model.nodes[4].init_W,
init_b=mainloop.model.nodes[4].init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=mainloop.model.nodes[5].init_W,
init_b=mainloop.model.nodes[5].init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=mainloop.model.nodes[6].init_W,
init_b=mainloop.model.nodes[6].init_b)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['x_1','s_tm1'],
parent_dim=[x2s_dim,rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=mainloop.model.nodes[7].init_W,
init_b=mainloop.model.nodes[7].init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=mainloop.model.nodes[8].init_W,
init_b=mainloop.model.nodes[8].init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=mainloop.model.nodes[9].init_W,
init_b=mainloop.model.nodes[9].init_b)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=mainloop.model.nodes[10].init_W,
init_b=mainloop.model.nodes[10].init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=mainloop.model.nodes[11].init_W,
init_b=mainloop.model.nodes[11].init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=mainloop.model.nodes[12].init_W,
init_b=mainloop.model.nodes[12].init_b)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=mainloop.model.nodes[13].init_W,
init_b=mainloop.model.nodes[13].init_b)"""
#pickle is from experiment gmmAE/18-05-30_16-07_app3
fmodel = open('dp_dis1-sch_1.pkl', 'rb')
mainloop = cPickle.load(fmodel)
fmodel.close()
rnn = mainloop.model.nodes[0]
x_1 = mainloop.model.nodes[1]
y_1 = mainloop.model.nodes[2]
z_1 = mainloop.model.nodes[3]
phi_1 = mainloop.model.nodes[4]
phi_mu = mainloop.model.nodes[5]
phi_sig = mainloop.model.nodes[6]
prior_1 = mainloop.model.nodes[7]
prior_mu = mainloop.model.nodes[8]
prior_sig = mainloop.model.nodes[9]
theta_1 = mainloop.model.nodes[10]
theta_mu = mainloop.model.nodes[11]
theta_sig = mainloop.model.nodes[12]
coeff = mainloop.model.nodes[13]
nodes = [
rnn,
x_1,
y_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = mainloop.model.params
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
y_1_temp = y_1.fprop([y], params)
def inner_fn_val(x_t, s_tm1):
prior_1_t = prior_1.fprop([x_t, s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
pred_t = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
pred_1_t = y_1.fprop([pred_t], params)
s_t = rnn.fprop([[x_t, z_1_t, pred_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, prior_mu_t, prior_sig_t, theta_mu_t, theta_sig_t, coeff_t, pred_t #, y_pred
#corr_temp, binary_temp
((s_temp_val, prior_mu_temp_val, prior_sig_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val, prediction_val), updates_val) =\
theano.scan(fn=inner_fn_val,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
s_temp_val = concatenate([s_0[None, :, :], s_temp_val[:-1]], axis=0)
def inner_fn_train(x_t, y_t, schedSampMask, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1, y_t], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([x_t, s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
if (schedSampMask == 1):
s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params)
else:
y_t_aux = y_1.fprop([pred], params)
s_t = rnn.fprop([[x_t, z_1_t, y_t_aux], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, theta_mu_t, theta_sig_t, coeff_t, pred #, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_fn_train,
sequences=[x_1_temp, y_1_temp, scheduleSamplingMask],
outputs_info=[s_0, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
#s_temp = concatenate([s_0[None, :, :], s_temp[:-1]], axis=0)# seems like this is for creating an additional dimension to s_0
theta_mu_temp.name = 'theta_mu_temp'
theta_sig_temp.name = 'theta_sig_temp'
coeff_temp.name = 'coeff'
if (flgAgg == -1):
prediction.name = 'x_reconstructed'
mse = T.mean((prediction - x)**2) # CHECK RESHAPE with an assertion
mae = T.mean(T.abs(prediction - x))
mse.name = 'mse'
pred_in = x.reshape((x_shape[0] * x_shape[1], -1))
else:
prediction.name = 'pred_' + str(flgAgg)
mse = T.mean(
(prediction - y)**2) # As axis = None is calculated for all
mae = T.mean(T.abs_(prediction - y))
mse.name = 'mse'
mae.name = 'mae'
pred_in = y.reshape((y.shape[0] * y.shape[1], -1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
theta_mu_in = theta_mu_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0] * x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(
pred_in, theta_mu_in, theta_sig_in, coeff_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
######################## TEST (GENERATION) TIME
prediction_val.name = 'generated__' + str(flgAgg)
mse_val = T.mean(
(prediction_val - y)**2) # As axis = None is calculated for all
mae_val = T.mean(T.abs_(prediction_val - y))
#y_unNormalize = (y * reader.stdTrain) + reader.meanTrain # accessing to just an scalar when loading y_dim=1
#prediction_valAux = (prediction_val * reader.stdTrain) + reader.meanTrain
#mse_valUnNorm = T.mean((prediction_valAux - y_unNormalize)**2) # As axis = None is calculated for all
#mae_valUnNorm = T.mean( T.abs_(prediction_valAux - y_unNormalize) )
mse_val.name = 'mse_val'
mae_val.name = 'mae_val'
pred_in_val = y.reshape((y.shape[0] * y.shape[1], -1))
theta_mu_in_val = theta_mu_temp_val.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in_val = theta_sig_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff_in_val = coeff_temp_val.reshape((x_shape[0] * x_shape[1], -1))
recon_val = GMM(
pred_in_val, theta_mu_in_val, theta_sig_in_val, coeff_in_val
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((x_shape[0], x_shape[1]))
recon_val.name = 'gmm_out_val'
recon_term_val = recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term_val'
model.inputs = [x, mask, y, y_mask, scheduleSamplingMask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
header = "epoch,log,kl,nll_upper_bound,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(
freq=monitoring_freq,
ddout=[nll_upper_bound, recon_term, kl_term, mse, mae, prediction],
indexSep=5,
instancesPlot=instancesPlot, #{0:[4,20],2:[5,10]},#, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr, 75: (lr / 10), 150: (lr / 100)}
"""mainloop = Training(
name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[recon_term, kl_term, nll_upper_bound, mse, mae],
n_steps = n_steps,
extension=extension,
lr_iterations=lr_iterations,
k_speedOfconvergence=kSchedSamp
)"""
"""mainloop.restore(
data=Iterator(train_data, batch_size),
cost=nll_upper_bound,
model=model,
optimizer=mainloop.optimizer
)"""
mainloop.restore(name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[recon_term, kl_term, nll_upper_bound, mse, mae],
n_steps=n_steps,
extension=extension,
lr_iterations=lr_iterations,
k_speedOfconvergence=kSchedSamp)
mainloop.run()
data = Iterator(test_data, batch_size)
test_fn = theano.function(
inputs=[x, y], #[x, y],
#givens={x:Xtest},
#on_unused_input='ignore',
#z=( ,200,1)
allow_input_downcast=True,
outputs=[prediction_val, recon_term_val, mse_val,
mae_val] #prediction_val, mse_val, mae_val
,
updates=
updates_val #, allow_input_downcast=True, on_unused_input='ignore'
)
testOutput = []
numBatchTest = 0
for batch in data:
outputGeneration = test_fn(batch[0], batch[2]) #(20, 220, 1)
testOutput.append(outputGeneration[1:])
# outputGeneration[0].shape #(20, 220, 40)
#if (numBatchTest<5):
'''
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_z_0-4".format(numBatchTest))
plt.clf()
plt.figure(2)
plt.plot(np.transpose(outputGeneration[1],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_s_0-4".format(numBatchTest))
plt.clf()
plt.figure(3)
plt.plot(np.transpose(outputGeneration[2],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_theta_0-4".format(numBatchTest))
plt.clf()
'''
plt.figure(4)
plt.plot(np.transpose(outputGeneration[0], [1, 0, 2])[4])
plt.plot(np.transpose(batch[2], [1, 0, 2])[4])
plt.savefig(
save_path +
"/vrnn_dis_generated{}_RealAndPred_0-4".format(numBatchTest))
plt.clf()
plt.figure(4)
plt.plot(np.transpose(batch[0], [1, 0, 2])[4])
plt.savefig(save_path +
"/vrnn_dis_generated{}_Realagg_0-4".format(numBatchTest))
plt.clf()
numBatchTest += 1
testOutput = np.asarray(testOutput)
print(testOutput.shape)
recon_test = testOutput[:, 0].mean()
mse_test = testOutput[:, 1].mean()
mae_test = testOutput[:, 2].mean()
#mseUnNorm_test = testOutput[:, 3].mean()
#maeUnNorm_test = testOutput[:, 4].mean()
fLog = open(save_path + '/output.csv', 'w')
fLog.write(str(lr_iterations) + "\n")
fLog.write(str(windows) + "\n")
fLog.write("logTest,mseTest,maeTest, mseTestUnNorm, maeTestUnNorm\n")
fLog.write("{},{},{}\n".format(recon_test, mse_test, mae_test))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
y2s_dim, z2s_dim))
header = "epoch,log,kl,mse,mae\n"
fLog.write(header)
for i, item in enumerate(mainloop.trainlog.monitor['recon_term']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{:d},{:.2f},{:.2f},{:.3f},{:.3f}\n".format(f, a, b, d, e))
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'nll_upper_bound'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
typeLoad = int(args['typeLoad'])
debug = int(args['debug'])
n_steps_val = n_steps
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
print str(windows)
q_z_dim = 180
p_z_dim = 180
p_x_dim = 200
x2s_dim = 100
z2s_dim = 150
target_dim = k #x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_dataport(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
trainPer=0.6,
valPer=0.2,
testPer=0.2,
typeLoad=typeLoad,
flgAggSumScaled=1,
flgFilterZeros=1)
instancesPlot = {
0: [5]
} #for now use hard coded instancesPlot for kelly sampling
train_data = Dataport(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=ytrain,
labels=Xtrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = Dataport(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=yval,
labels=Xval)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
scheduleSamplingMask = T.fvector('schedMask')
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
'''
dissag_pred = FullyConnectedLayer(name='disag_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=num_apps,
unit='relu',
init_W=init_W,
init_b=init_b)
'''
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
def inner_val_fn(s_tm1):
'''
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
'''
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
pred_t = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
pred_1_t = x_1.fprop([pred_t], params)
s_t = rnn.fprop([[pred_1_t, z_1_t], [s_tm1]], params)
return s_t, pred_t, z_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t
# prior_mu_temp_val, prior_sig_temp_val
((s_temp_val, prediction_val, z_t_temp_val, theta_1_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val), updates_val) =\
theano.scan(fn=inner_val_fn , n_steps=n_steps_val, #already 1 subtracted if doing next step
outputs_info=[s_0, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
def inner_fn(x_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_t, z_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred #, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_1_temp ],
outputs_info=[s_0, None, None, None, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
s_temp = concatenate(
[s_0[None, :, :], s_temp[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
'''
theta_1_temp = theta_1.fprop([z_1_temp, s_temp], params)
theta_mu_temp = theta_mu.fprop([theta_1_temp], params)
theta_sig_temp = theta_sig.fprop([theta_1_temp], params)
coeff_temp = coeff.fprop([theta_1_temp], params)
corr_temp = corr.fprop([theta_1_temp], params)
binary_temp = binary.fprop([theta_1_temp], params)
'''
s_temp.name = 'h' #gisse
z_1_temp.name = 'z2' #gisse
z_t_temp.name = 'z'
theta_mu_temp.name = 'mu'
theta_sig_temp.name = 'sig'
coeff_temp.name = 'coeff'
prediction.name = 'Prediction-' + str(appliances[flgAgg][:-1])
mse = T.mean((prediction - x)**2) # As axis = None is calculated for all
mae = T.mean(T.abs_(prediction - x))
mse.name = 'mse'
mae.name = 'mae'
x_in = x.reshape((batch_size * n_steps, -1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
theta_mu_in = theta_mu_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0] * x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(
x_in, theta_mu_in, theta_sig_in, coeff_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
#recon = recon * mask
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
#kl_temp = kl_temp * mask
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
############## TEST ###############
theta_mu_in_val = theta_mu_temp_val.reshape((batch_size * n_steps, -1))
theta_sig_in_val = theta_sig_temp_val.reshape((batch_size * n_steps, -1))
coeff_in_val = coeff_temp_val.reshape((batch_size * n_steps, -1))
pred_in = prediction_val.reshape((batch_size * n_steps, -1))
recon_val = GMM(
pred_in, theta_mu_in_val, theta_sig_in_val, coeff_in_val
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((batch_size, n_steps))
recon_val.name = 'gmm_out_val'
model.inputs = [x, mask, y, y_mask, scheduleSamplingMask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
header = "epoch,log,kl,nll_upper_bound,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(
freq=monitoring_freq,
ddout=[nll_upper_bound, recon_term, kl_term, mse, mae, prediction],
indexSep=5,
indexDDoutPlot=[(0, theta_mu_temp), (2, z_t_temp),
(3, prediction)],
instancesPlot=instancesPlot, #{0:[4,20],2:[5,10]},#, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {
0: lr,
30: (lr / 10)
} #, 150:(lr/10), 270:(lr/100), 370:(lr/1000)
mainloop = Training(name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
n_steps=n_steps,
extension=extension,
lr_iterations=lr_iterations)
mainloop.run()
test_fn = theano.function(
inputs=[],
outputs=[prediction_val, recon_val],
updates=
updates_val #, allow_input_downcast=True, on_unused_input='ignore'
)
outputGeneration = test_fn()
#{0:[4,20], 2:[5,10]}
'''
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0],[1,0,2])[5])
plt.savefig(save_path+"/vrnn_dis_generated_z_0-4.ps")
plt.figure(2)
plt.plot(np.transpose(outputGeneration[1],[1,0,2])[5])
plt.savefig(save_path+"/vrnn_dis_generated_s_0-4.ps")
plt.figure(3)
plt.plot(np.transpose(outputGeneration[2],[1,0,2])[5])
plt.savefig(save_path+"/vrnn_dis_generated_theta_0-4.ps")
'''
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0], [1, 0, 2])[2])
plt.savefig(save_path + "/vrnn_dis_generated_pred_0-2.ps")
plt.figure(2)
plt.plot(np.transpose(outputGeneration[0], [1, 0, 2])[10])
plt.savefig(save_path + "/vrnn_dis_generated_pred_0-10.ps")
plt.figure(3)
plt.plot(np.transpose(outputGeneration[0], [1, 0, 2])[15])
plt.savefig(save_path + "/vrnn_dis_generated_pred_0-15.ps")
testLogLike = np.asarray(outputGeneration[1]).mean()
fLog = open(save_path + '/output.csv', 'w')
fLog.write(str(lr_iterations) + "\n")
fLog.write(str(windows) + "\n")
fLog.write("Test-log-likelihood\n")
fLog.write("{}\n".format(testLogLike))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
z2s_dim))
fLog.write("epoch,log,kl,nll_upper_bound,mse,mae\n")
for i, item in enumerate(mainloop.trainlog.monitor['nll_upper_bound']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
c = mainloop.trainlog.monitor['nll_upper_bound'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{:d},{:.2f},{:.2f},{:.2f},{:.3f},{:.3f}\n".format(
f, a, b, c, d, e))
fLog.close()
f = open(save_path + '/outputRealGeneration.pkl', 'wb')
cPickle.dump(outputGeneration, f, -1)
f.close()
def main(args):
theano.optimizer = 'fast_compile'
theano.config.exception_verbosity = 'high'
trial = int(args['trial'])
pkl_name = 'rnn_gmm_%d' % trial
channel_name = 'valid_nll'
data_path = args['data_path']
save_path = args['save_path']
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
z_dim = int(args['z_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k'])
lr = float(args['lr'])
debug = int(args['debug'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
x2s_dim = 50 #300
s2x_dim = 50 #300
target_dim = k #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval = fetch_ukdale(data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test)
print("Inside: ", Xtrain.shape, ytrain.shape, Xval.shape, yval.shape)
train_data = UKdale(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars() #mask, y_mask
'''
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
'''
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1'],
parent_dim=[x2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=s2x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[s2x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[s2x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[s2x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
'''
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[s2x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[s2x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
'''
nodes = [rnn, x_1, theta_1, theta_mu, theta_sig, coeff]
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_shape = x.shape
x_1_temp = x_1.fprop([x], params)
def inner_fn(x_t, s_tm1):
s_t = rnn.fprop([[x_t], [s_tm1]], params)
theta_1_t = theta_1.fprop([s_t], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t, coeff_t)
return s_t, theta_mu_t, theta_sig_t, coeff_t, pred
((s_temp, theta_mu_temp, theta_sig_temp, coeff_temp, pred_temp),
updates) = theano.scan(fn=inner_fn,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
s_temp = concatenate([s_0[None, :, :], s_temp[:-1]], axis=0)
'''
theta_1_temp = theta_1.fprop([s_temp], params)
theta_mu_temp = theta_mu.fprop([theta_1_temp], params)
theta_sig_temp = theta_sig.fprop([theta_1_temp], params)
coeff_temp = coeff.fprop([theta_1_temp], params)
corr_temp = corr.fprop([theta_1_temp], params)
binary_temp = binary.fprop([theta_1_temp], params)
'''
x_shape = x.shape
x_in = x.reshape((x_shape[0] * x_shape[1], -1))
theta_mu_in = theta_mu_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0] * x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
if (flgAgg == -1):
pred_temp.name = 'x_reconstructed'
mse = T.mean((pred_temp - x)**2) # CHECK RESHAPE with an assertion
mae = T.mean(T.abs(pred_temp - x))
mse.name = 'mse'
pred_in = x.reshape((x_shape[0] * x_shape[1], -1))
else:
#pred_temp = pred_temp.reshape((pred_temp.shape[0], pred_temp.shape[1]))
pred_temp.name = 'pred_' + str(flgAgg)
#y[:,:,flgAgg].reshape((y.shape[0],y.shape[1],1))
mse = T.mean((pred_temp - y)**2) # CHECK RESHAPE with an assertion
mae = T.mean(T.abs_(pred_temp - y))
mse.name = 'mse'
mae.name = 'mae'
pred_in = y.reshape((y.shape[0] * y.shape[1], -1))
recon = GMM(pred_in, theta_mu_in, theta_sig_in, coeff_in) #, binary_in
recon.name = 'recon'
recon = recon.reshape((y.shape[0], y.shape[1]))
#recon = recon * y_mask #(200, 1000), (1000, 200)
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'nll'
max_x = x.max()
mean_x = x.mean()
min_x = x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
max_theta_mu = theta_mu_in.max()
mean_theta_mu = theta_mu_in.mean()
min_theta_mu = theta_mu_in.min()
max_theta_mu.name = 'max_theta_mu'
mean_theta_mu.name = 'mean_theta_mu'
min_theta_mu.name = 'min_theta_mu'
'''
max_theta_sig = theta_sig_in.max()
mean_theta_sig = theta_sig_in.mean()
min_theta_sig = theta_sig_in.min()
max_theta_sig.name = 'max_theta_sig'
mean_theta_sig.name = 'mean_theta_sig'
min_theta_sig.name = 'min_theta_sig'
coeff_max = coeff_in.max()
coeff_min = coeff_in.min()
coeff_mean_max = coeff_in.mean(axis=0).max()
coeff_mean_min = coeff_in.mean(axis=0).min()
coeff_max.name = 'coeff_max'
coeff_min.name = 'coeff_min'
coeff_mean_max.name = 'coeff_mean_max'
coeff_mean_min.name = 'coeff_mean_min'
'''
model.inputs = [x, mask, y, y_mask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch),
Monitoring(
freq=monitoring_freq,
ddout=[
recon_term,
mse,
mae,
#max_theta_sig, mean_theta_sig, min_theta_sig,
max_x,
mean_x,
min_x,
max_theta_mu,
mean_theta_mu,
min_theta_mu,
#coeff_max, coeff_min, coeff_mean_max, coeff_mean_min,#16
theta_mu_temp,
theta_sig_temp,
pred_temp,
coeff_temp,
s_temp
],
indexSep=9,
indexDDoutPlot=[(0, theta_mu_temp), (2, pred_temp)],
instancesPlot=[10, 100], #, 80,150
savedFolder=save_path,
data=[Iterator(valid_data, batch_size)]),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr, 20: (lr / 10), 150: (lr / 100), 200: (lr / 1000)}
mainloop = Training(name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=recon_term,
outputs=[recon_term],
extension=extension,
lr_iterations=lr_iterations)
mainloop.run()
fLog = open(save_path + '/output.csv', 'w')
fLog.write("log,mse,mae\n")
for i, item in enumerate(mainloop.trainlog.monitor['nll']):
a = mainloop.trainlog.monitor['nll'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{},{},{}\n".format(a, d, e))
for k, v in updates.iteritems():
k.default_update = v
s_t = s_t[:-1]
s_shape = s_t.shape
s_in = T.concatenate([s_0, s_t.reshape((s_shape[0]*s_shape[1], -1))], axis=0)
theta_1_in = theta_1.fprop([s_in])
theta_2_in = theta_2.fprop([theta_1_in])
theta_3_in = theta_3.fprop([theta_2_in])
theta_4_in = theta_4.fprop([theta_3_in])
theta_mu_in = theta_mu.fprop([theta_4_in])
theta_sig_in = theta_sig.fprop([theta_4_in])
coeff_in = coeff.fprop([theta_4_in])
recon = GMM(x_in, theta_mu_in, theta_sig_in, coeff_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon = recon * x_mask
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'nll'
max_x = x.max()
mean_x = x.mean()
min_x = x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
max_theta_mu = theta_mu_in.max()
mean_theta_mu = theta_mu_in.mean()
min_theta_mu = theta_mu_in.min()
theta_2_in = theta_2.fprop([theta_1_in]) theta_3_in = theta_3.fprop([theta_2_in]) theta_4_in = theta_4.fprop([theta_3_in]) theta_mu_in = theta_mu.fprop([theta_4_in]) theta_sig_in = theta_sig.fprop([theta_4_in]) coeff_in = coeff.fprop([theta_4_in]) z_shape = phi_mu_t.shape phi_mu_in = phi_mu_t.reshape((z_shape[0]*z_shape[1], -1)) phi_sig_in = phi_sig_t.reshape((z_shape[0]*z_shape[1], -1)) prior_mu_in = prior_mu_t.reshape((z_shape[0]*z_shape[1], -1)) prior_sig_in = prior_sig_t.reshape((z_shape[0]*z_shape[1], -1)) kl_in = kl.fprop([phi_mu_in, phi_sig_in, prior_mu_in, prior_sig_in]) kl_t = kl_in.reshape((z_shape[0], z_shape[1])) recon = GMM(x_in, theta_mu_in, theta_sig_in, coeff_in) recon = recon.reshape((x_shape[0], x_shape[1])) recon_term = recon.mean() kl_term = kl_t.mean() nll_lower_bound = recon_term + kl_term nll_lower_bound.name = 'nll_lower_bound' mn_x_shape = mn_x.shape mn_x_in = mn_x.reshape((mn_x_shape[0]*mn_x_shape[1], -1)) mn_x_1_in = x_1.fprop([mn_x_in]) mn_x_2_in = x_2.fprop([mn_x_1_in]) mn_x_3_in = x_3.fprop([mn_x_2_in]) mn_x_4_in = x_4.fprop([mn_x_3_in]) mn_x_4_in = mn_x_4_in.reshape((mn_x_shape[0], mn_x_shape[1], -1)) mn_s_0 = main_lstm.get_init_state(mn_batch_size)
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
#pkl_name = 'dp_dis1-nosch_%d' % trial
channel_name = 'mae'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
testRef_file = args['testRef_file']
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
typeLoad = int(args['typeLoad'])
debug = int(args['debug'])
kSchedSamp = int(args['kSchedSamp'])
clipped = int(args['clipped'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "Reading pkl file '%s'" % testRef_file
print "to the save path '%s'" % save_path
q_z_dim = 150
p_z_dim = 150
p_x_dim = 150 #250
x2s_dim = 100 #250
y2s_dim = 100
z2s_dim = 100 #150
target_dim = k #x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_dataport(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
trainPer=0.6,
valPer=0.2,
testPer=0.2,
typeLoad=typeLoad,
flgAggSumScaled=1,
flgFilterZeros=1)
print(reader.stdTrain, reader.meanTrain)
instancesPlot = {
0: [4],
2: [5]
} #for now use hard coded instancesPlot for kelly sampling
train_data = Dataport(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = Dataport(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
test_data = Dataport(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xtest,
labels=ytest)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = test_data.theano_vars()
scheduleSamplingMask = T.fvector('schedMask')
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
fmodel = open(testRef_file, 'rb')
mainloop = cPickle.load(fmodel)
fmodel.close()
#define layers
rnn = mainloop.model.nodes[0]
x_1 = mainloop.model.nodes[1]
y_1 = mainloop.model.nodes[2]
z_1 = mainloop.model.nodes[3]
phi_1 = mainloop.model.nodes[4]
phi_mu = mainloop.model.nodes[5]
phi_sig = mainloop.model.nodes[6]
prior_1 = mainloop.model.nodes[7]
prior_mu = mainloop.model.nodes[8]
prior_sig = mainloop.model.nodes[9]
theta_1 = mainloop.model.nodes[10]
theta_mu = mainloop.model.nodes[11]
theta_sig = mainloop.model.nodes[12]
coeff = mainloop.model.nodes[13]
nodes = [
rnn,
x_1,
y_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = mainloop.model.params
"""params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)"""
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
def inner_fn_val(x_t, s_tm1):
prior_1_t = prior_1.fprop([x_t, s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
pred_t = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
pred_1_t = y_1.fprop([pred_t], params)
s_t = rnn.fprop([[x_t, z_1_t, pred_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, prior_mu_t, prior_sig_t, theta_mu_t, theta_sig_t, coeff_t, pred_t #, y_pred
#corr_temp, binary_temp
((s_temp_val, prior_mu_temp_val, prior_sig_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val, prediction_val), updates_val) =\
theano.scan(fn=inner_fn_val,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
s_temp_val = concatenate([s_0[None, :, :], s_temp_val[:-1]], axis=0)
x_shape = x.shape
######################## TEST (GENERATION) TIME
prediction_val = T.clip(prediction_val, 0.0, np.inf)
prediction_val.name = 'generated__' + str(flgAgg)
mse_val = T.mean(
(prediction_val - y)**2) # As axis = None is calculated for all
mae_val = T.mean(T.abs_(prediction_val - y))
mse_val.name = 'mse_val'
mae_val.name = 'mae_val'
pred_in_val = y.reshape((y.shape[0] * y.shape[1], -1))
theta_mu_in_val = theta_mu_temp_val.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in_val = theta_sig_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff_in_val = coeff_temp_val.reshape((x_shape[0] * x_shape[1], -1))
recon_val = GMM(
pred_in_val, theta_mu_in_val, theta_sig_in_val, coeff_in_val
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((x_shape[0], x_shape[1]))
recon_val.name = 'gmm_out_val'
recon_term_val = recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term_val'
data = Iterator(test_data, batch_size)
test_fn = theano.function(
inputs=[x, y], #[x, y],
allow_input_downcast=True,
outputs=[
prediction_val, theta_mu_in_val, theta_sig_in_val, coeff_in_val,
recon_term_val
] #prediction_val, mse_val, mae_val
,
updates=
updates_val #, allow_input_downcast=True, on_unused_input='ignore'
)
testOutput = []
bestInstsancesPred = []
bestInstsancesDisa = []
bestInstsancesAggr = []
numBatchTest = 0
for batch in data:
#, recon_term_val, mse_val, mae_val
x_test, y_test = batch[0], batch[2]
outputGeneration = test_fn(x_test, y_test) #(20, 220, 1)
#testOutput.append(outputGeneration[1:])
# outputGeneration[0].shape #(20, 220, 40)
if (clipped == 1):
prediction_test = outputGeneration[0].clip(min=0)
theta_mu_test = outputGeneration[1]
theta_sig_test = outputGeneration[2]
coeff_in_test = outputGeneration[3]
realAggTest = np.transpose(x_test, [1, 0, 2])
realDisagTest = np.transpose(
y_test, [1, 0, 2]) # because y_test already transformed
prediction_test = np.transpose(prediction_test, [1, 0, 2])
### test loglike
y_test = y_test.reshape((y_test.shape[0] * y_test.shape[1], -1))
recon_test = GMM_outside(
y_test, theta_mu_test, theta_sig_test, coeff_in_test
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_test = recon_test.reshape((x_test.shape[0], x_test.shape[1]))
recon_term_test = recon_test.sum(axis=0).mean()
## test mse - mae
mse_test = np.mean(
(prediction_test -
realDisagTest)**2) # As axis = None is calculated for all
mae_test = np.mean(np.absolute(prediction_test - realDisagTest))
testOutput.append(
(recon_term_test, mse_test, mae_test, outputGeneration[4]))
batchMae_test = np.mean(np.absolute(prediction_test - realDisagTest),
axis=(1, 2))
idxMin = np.argmin(batchMae_test)
for idx in np.asarray(idxMin).reshape(1, -1)[0, :]:
plt.figure(1)
plt.plot(prediction_test[idx])
plt.plot(realDisagTest[idx])
plt.legend(["Predicted", "Real"])
plt.savefig(
save_path +
"/vrnn_dis1_test-b{}_Pred-Real_0-{}".format(numBatchTest, idx))
plt.clf()
plt.figure(3)
plt.plot(np.transpose(batch[0], [1, 0, 2])[idx])
plt.savefig(
save_path +
"/vrnn_dis1_test-b{}_RealAgg_0-{}".format(numBatchTest, idx))
plt.clf()
bestInstsancesPred.append(prediction_test[idx])
bestInstsancesDisa.append(realDisagTest[idx])
bestInstsancesAggr.append(realAggTest[idx])
numBatchTest += 1
testOutput = np.asarray(testOutput)
recon_testOutside = np.mean(testOutput[:, 0])
mse_test = np.mean(testOutput[:, 1])
mae_test = np.mean(testOutput[:, 2])
recon_testInside = np.mean(testOutput[:, 3])
#mseUnNorm_test = testOutput[:, 3].mean()
#maeUnNorm_test = testOutput[:, 4].mean()
fLog = open(save_path + '/output_test.csv', 'w')
fLog.write(str(windows) + "\n")
fLog.write(
"logTestOutside,logTestInside,mseTest,maeTest, mseTestUnNorm, maeTestUnNorm\n"
)
fLog.write("{},{},{},{}\n".format(recon_testOutside, recon_testInside,
mse_test, mae_test))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
y2s_dim, z2s_dim))
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'dp_dis1-sch_%d' % trial
channel_name = 'mae'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
typeLoad = int(args['typeLoad'])
debug = int(args['debug'])
kSchedSamp = int(args['kSchedSamp'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 150
p_z_dim = 150
p_x_dim = 150 #250
x2s_dim = 100 #250
y2s_dim = 100
z2s_dim = 100 #150
target_dim = k #x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_dataport(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
trainPer=0.6,
valPer=0.2,
testPer=0.2,
typeLoad=typeLoad,
flgAggSumScaled=1,
flgFilterZeros=1)
print(reader.stdTrain, reader.meanTrain)
instancesPlot = {
0: [4],
2: [10]
} #for now use hard coded instancesPlot for kelly sampling
train_data = Dataport(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = Dataport(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
test_data = Dataport(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xtest,
labels=ytest)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
scheduleSamplingMask = T.fvector('schedMask')
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
fmodel = open('dp_dis1-sch_1_best.pkl', 'rb')
mainloop = cPickle.load(fmodel)
fmodel.close()
#attrs = vars(mainloop)
#print ', '.join("%s: %s" % item for item in attrs.items())
"""names = [x.name for x in mainloop.model.nodes]
print(names)
print(mainloop.model.nodes)"""
#define layers
rnn = mainloop.model.nodes[0]
x_1 = mainloop.model.nodes[1]
y_1 = mainloop.model.nodes[2]
z_1 = mainloop.model.nodes[3]
phi_1 = mainloop.model.nodes[4]
phi_mu = mainloop.model.nodes[5]
phi_sig = mainloop.model.nodes[6]
prior_1 = mainloop.model.nodes[7]
prior_mu = mainloop.model.nodes[8]
prior_sig = mainloop.model.nodes[9]
theta_1 = mainloop.model.nodes[10]
theta_mu = mainloop.model.nodes[11]
theta_sig = mainloop.model.nodes[12]
coeff = mainloop.model.nodes[13]
nodes = [
rnn,
x_1,
y_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = mainloop.model.params
"""params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())"""
#params = init_tparams(params)
"""OrderedDict([('W_x_1__rnn', W_x_1__rnn), ('W_z_1__rnn', W_z_1__rnn), ('W_y_1__rnn', W_y_1__rnn),
('U_rnn__rnn', U_rnn__rnn), ('b_rnn', b_rnn), ('W_x_t__x_1', W_x_t__x_1), ('b_x_1', b_x_1),
('W_y_t__y_1', W_y_t__y_1), ('b_y_1', b_y_1), ('W_z_t__z_1', W_z_t__z_1), ('b_z_1', b_z_1),
('W_x_1__phi_1', W_x_1__phi_1), ('W_s_tm1__phi_1', W_s_tm1__phi_1), ('W_y_1__phi_1', W_y_1__phi_1),
('b_phi_1', b_phi_1), ('W_phi_1__phi_mu', W_phi_1__phi_mu), ('b_phi_mu', b_phi_mu),
('W_phi_1__phi_sig', W_phi_1__phi_sig), ('b_phi_sig', b_phi_sig), ('W_x_1__prior_1', W_x_1__prior_1),
('W_s_tm1__prior_1', W_s_tm1__prior_1), ('b_prior_1', b_prior_1), ('W_prior_1__prior_mu', W_prior_1__prior_mu),
('b_prior_mu', b_prior_mu), ('W_prior_1__prior_sig', W_prior_1__prior_sig), ('b_prior_sig', b_prior_sig),
('W_z_1__theta_1', W_z_1__theta_1), ('W_s_tm1__theta_1', W_s_tm1__theta_1), ('b_theta_1', b_theta_1),
('W_theta_1__theta_mu', W_theta_1__theta_mu), ('b_theta_mu', b_theta_mu), ('W_theta_1__theta_sig', W_theta_1__theta_sig),
('b_theta_sig', b_theta_sig), ('W_theta_1__coeff', W_theta_1__coeff), ('b_coeff', b_coeff)])"""
"""w1 = params['W_x_1__rnn']
w2 = params['W_phi_1__phi_sig']
w3 = params['W_theta_1__coeff']
print("Initialized W matricies:")
print("W1:",w1.get_value())
print("W2:",w2.get_value())
print("W3:",w3.get_value())
print("\n\n--------------------------------\n\n")"""
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
y_1_temp = y_1.fprop([y], params)
def inner_fn_val(x_t, s_tm1):
prior_1_t = prior_1.fprop([x_t, s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
pred_t = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
pred_1_t = y_1.fprop([pred_t], params)
s_t = rnn.fprop([[x_t, z_1_t, pred_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, prior_mu_t, prior_sig_t, theta_mu_t, theta_sig_t, coeff_t, pred_t #, y_pred
#corr_temp, binary_temp
((s_temp_val, prior_mu_temp_val, prior_sig_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val, prediction_val), updates_val) =\
theano.scan(fn=inner_fn_val,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
s_temp_val = concatenate([s_0[None, :, :], s_temp_val[:-1]], axis=0)
x_shape = x.shape
######################## TEST (GENERATION) TIME
prediction_val.name = 'generated__' + str(flgAgg)
mse_val = T.mean(
(prediction_val - y)**2) # As axis = None is calculated for all
mae_val = T.mean(T.abs_(prediction_val - y))
mse_val.name = 'mse_val'
mae_val.name = 'mae_val'
pred_in_val = y.reshape((y.shape[0] * y.shape[1], -1))
theta_mu_in_val = theta_mu_temp_val.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in_val = theta_sig_temp_val.reshape(
(x_shape[0] * x_shape[1], -1))
coeff_in_val = coeff_temp_val.reshape((x_shape[0] * x_shape[1], -1))
recon_val = GMM(
pred_in_val, theta_mu_in_val, theta_sig_in_val, coeff_in_val
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((x_shape[0], x_shape[1]))
recon_val.name = 'gmm_out_val'
recon_term_val = recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term_val'
model.inputs = [x, mask, y, y_mask, scheduleSamplingMask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
header = "epoch,log,kl,nll_upper_bound,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(
freq=monitoring_freq,
#ddout=[nll_upper_bound, recon_term, kl_term, mse, mae, prediction],
indexSep=5,
instancesPlot=instancesPlot, #{0:[4,20],2:[5,10]},#, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
"""params = OrderedDict()
for node in mainloop.model.nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)"""
""" w11 = params['W_x_1__rnn']
w21 = params['W_phi_1__phi_sig']
w31 = params['W_theta_1__coeff']
print("Pickle W matricies:")
print("W1:",w11.get_value())
print("W2:",w21.get_value())
print("W3:",w31.get_value())"""
"""mainloop.restore(
name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
#cost=nll_upper_bound,
#outputs=[recon_term, kl_term, nll_upper_bound, mse, mae],
n_steps = n_steps,
extension=extension,
#lr_iterations=lr_iterations,
k_speedOfconvergence=kSchedSamp
)"""
data = Iterator(test_data, batch_size)
test_fn = theano.function(
inputs=[x, y], #[x, y],
#givens={x:Xtest},
#on_unused_input='ignore',
#z=( ,200,1)
allow_input_downcast=True,
outputs=[prediction_val, recon_term_val, mse_val,
mae_val] #prediction_val, mse_val, mae_val
,
updates=
updates_val #, allow_input_downcast=True, on_unused_input='ignore'
)
testOutput = []
numBatchTest = 0
for batch in data:
outputGeneration = test_fn(batch[0], batch[2]) #(20, 220, 1)
testOutput.append(outputGeneration[1:])
plt.figure(4)
plt.plot(np.transpose(outputGeneration[0], [1, 0, 2])[4])
plt.plot(np.transpose(batch[2], [1, 0, 2])[4])
plt.savefig(
save_path +
"/vrnn_dis_generated{}_RealAndPred_0-4".format(numBatchTest))
plt.clf()
plt.figure(4)
plt.plot(np.transpose(batch[0], [1, 0, 2])[4])
plt.savefig(save_path +
"/vrnn_dis_generated{}_Realagg_0-4".format(numBatchTest))
plt.clf()
numBatchTest += 1
testOutput = np.asarray(testOutput)
print(testOutput.shape)
recon_test = testOutput[:, 0].mean()
mse_test = testOutput[:, 1].mean()
mae_test = testOutput[:, 2].mean()
#mseUnNorm_test = testOutput[:, 3].mean()
#maeUnNorm_test = testOutput[:, 4].mean()
fLog = open(save_path + '/output.csv', 'w')
#fLog.write(str(lr_iterations)+"\n")
fLog.write(str(windows) + "\n")
fLog.write("logTest,mseTest,maeTest, mseTestUnNorm, maeTestUnNorm\n")
fLog.write("{},{},{}\n".format(recon_test, mse_test, mae_test))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
y2s_dim, z2s_dim))
header = "epoch,log,kl,mse,mae\n"
fLog.write(header)
for i, item in enumerate(mainloop.trainlog.monitor['recon_term']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{:d},{:.2f},{:.2f},{:.3f},{:.3f}\n".format(f, a, b, d, e))
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'dp_dis1-nosch_%d' % trial
channel_name = 'mae'
data_path = args['data_path']
save_path = args['save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps# int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
typeLoad = int(args['typeLoad'])
debug = int(args['debug'])
kSchedSamp = int(args['kSchedSamp'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 150
p_z_dim = 150
p_x_dim = 150#250
x2s_dim = 100#250
y2s_dim = 100
z2s_dim = 100#150
target_dim = k#x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_dataport(data_path, windows, appliances,numApps=flgAgg, period=period,
n_steps= n_steps, stride_train = stride_train, stride_test = stride_test,
trainPer=0.6, valPer=0.2, testPer=0.2, typeLoad=typeLoad,
flgAggSumScaled = 1, flgFilterZeros = 1)
print(reader.stdTrain, reader.meanTrain)
instancesPlot = {0:[4], 2:[5]} #for now use hard coded instancesPlot for kelly sampling
train_data = Dataport(name='train',
prep='normalize',
cond=True,# False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = Dataport(name='valid',
prep='normalize',
cond=True,# False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels = yval)
test_data = Dataport(name='valid',
prep='normalize',
cond=True,# False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xtest,
labels = ytest)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y , y_mask = train_data.theano_vars()
scheduleSamplingMask = T.fvector('schedMask')
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
pickelModel = '/home/gissella/Documents/Research/Disaggregation/PecanStreet-dataport/VRNN_theano_version/output/gmmAE/18-05-30_16-27_app6/dp_dis1-sch_1_best.pkl'
fmodel = open(pickelModel, 'rb')
mainloop = cPickle.load(fmodel)
fmodel.close()
#define layers
rnn = mainloop.model.nodes[0]
x_1 = mainloop.model.nodes[1]
y_1 = mainloop.model.nodes[2]
z_1 = mainloop.model.nodes[3]
phi_1 = mainloop.model.nodes[4]
phi_mu = mainloop.model.nodes[5]
phi_sig = mainloop.model.nodes[6]
prior_1 = mainloop.model.nodes[7]
prior_mu = mainloop.model.nodes[8]
prior_sig = mainloop.model.nodes[9]
theta_1 = mainloop.model.nodes[10]
theta_mu = mainloop.model.nodes[11]
theta_sig = mainloop.model.nodes[12]
coeff = mainloop.model.nodes[13]
nodes = [rnn,
x_1, y_1, z_1, #dissag_pred,
phi_1, phi_mu, phi_sig,
prior_1, prior_mu, prior_sig,
theta_1, theta_mu, theta_sig, coeff]#, corr, binary
params = mainloop.model.params
"""params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)"""
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
y_1_temp = y_1.fprop([y], params)
def inner_fn_val(x_t, s_tm1):
prior_1_t = prior_1.fprop([x_t,s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
pred_t = GMM_sample(theta_mu_t, theta_sig_t, coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
pred_1_t = y_1.fprop([pred_t], params)
s_t = rnn.fprop([[x_t, z_1_t, pred_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, prior_mu_t, prior_sig_t, theta_mu_t, theta_sig_t, coeff_t, pred_t#, y_pred
#corr_temp, binary_temp
((s_temp_val, prior_mu_temp_val, prior_sig_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val, prediction_val), updates_val) =\
theano.scan(fn=inner_fn_val,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
s_temp_val = concatenate([s_0[None, :, :], s_temp_val[:-1]], axis=0)
x_shape = x.shape
######################## TEST (GENERATION) TIME
prediction_val.name = 'generated__'+str(flgAgg)
mse_val = T.mean((prediction_val - y)**2) # As axis = None is calculated for all
mae_val = T.mean( T.abs_(prediction_val - y) )
mse_val.name = 'mse_val'
mae_val.name = 'mae_val'
pred_in_val = y.reshape((y.shape[0]*y.shape[1],-1))
theta_mu_in_val = theta_mu_temp_val.reshape((x_shape[0]*x_shape[1], -1))
theta_sig_in_val = theta_sig_temp_val.reshape((x_shape[0]*x_shape[1], -1))
coeff_in_val = coeff_temp_val.reshape((x_shape[0]*x_shape[1], -1))
recon_val = GMM(pred_in_val, theta_mu_in_val, theta_sig_in_val, coeff_in_val)# BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((x_shape[0], x_shape[1]))
recon_val.name = 'gmm_out_val'
recon_term_val= recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term_val'
model.inputs = [x, mask, y, y_mask, scheduleSamplingMask]
model.params = params
model.nodes = nodes
data=Iterator(test_data, batch_size)
test_fn = theano.function(inputs=[x, y],#[x, y],
allow_input_downcast=True,
outputs=[prediction_val, recon_term_val, mse_val, mae_val]#prediction_val, mse_val, mae_val
,updates=updates_val#, allow_input_downcast=True, on_unused_input='ignore'
)
testOutput = []
numBatchTest = 0
for batch in data:
outputGeneration = test_fn(batch[0], batch[2])#(20, 220, 1)
testOutput.append(outputGeneration[1:])
# outputGeneration[0].shape #(20, 220, 40)
#if (numBatchTest<5):
'''
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_z_0-4".format(numBatchTest))
plt.clf()
plt.figure(2)
plt.plot(np.transpose(outputGeneration[1],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_s_0-4".format(numBatchTest))
plt.clf()
plt.figure(3)
plt.plot(np.transpose(outputGeneration[2],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_theta_0-4".format(numBatchTest))
plt.clf()
'''
plt.figure(4)
plt.plot(np.transpose(outputGeneration[0],[1,0,2])[4])
plt.plot(np.transpose(batch[2],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_RealAndPred_0-4".format(numBatchTest))
plt.clf()
plt.figure(4)
plt.plot(np.transpose(batch[0],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_Realagg_0-4".format(numBatchTest))
plt.clf()
numBatchTest+=1
testOutput = np.asarray(testOutput)
print(testOutput.shape)
recon_test = testOutput[:, 0].mean()
mse_test = testOutput[:, 1].mean()
mae_test = testOutput[:, 2].mean()
#mseUnNorm_test = testOutput[:, 3].mean()
#maeUnNorm_test = testOutput[:, 4].mean()
fLog = open(save_path+'/output.csv', 'w')
fLog.write(str(lr_iterations)+"\n")
fLog.write(str(windows)+"\n")
fLog.write("logTest,mseTest,maeTest, mseTestUnNorm, maeTestUnNorm\n")
fLog.write("{},{},{}\n".format(recon_test,mse_test,mae_test))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{},{}\n".format(q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim))
header = "epoch,log,kl,mse,mae\n"
fLog.write(header)
for i , item in enumerate(mainloop.trainlog.monitor['recon_term']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{:d},{:.2f},{:.2f},{:.3f},{:.3f}\n".format(f,a,b,d,e))
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'dp_dis1-nosch_%d' % trial
channel_name = 'mae'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
typeLoad = int(args['typeLoad'])
debug = int(args['debug'])
kSchedSamp = int(args['kSchedSamp'])
typeActivFunc = args['typeActivFunc']
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 150
p_z_dim = 150
p_x_dim = 150 #250
x2s_dim = 100 #250
y2s_dim = 100
z2s_dim = 100 #150
target_dim = k #x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_dataport(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
trainPer=0.6,
valPer=0.2,
testPer=0.2,
typeLoad=typeLoad,
flgAggSumScaled=1,
flgFilterZeros=1)
print(reader.stdTrain, reader.meanTrain)
instancesPlot = {
0: [4],
2: [5]
} #for now use hard coded instancesPlot for kelly sampling
train_data = Dataport(
name='train',
prep='normalize',
cond=True, # False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = Dataport(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels=yval)
test_data = Dataport(
name='valid',
prep='normalize',
cond=True, # False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xtest,
labels=ytest)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
scheduleSamplingMask = T.fvector('schedMask')
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
y_1 = FullyConnectedLayer(name='y_1',
parent=['y_t'],
parent_dim=[y_dim],
nout=y2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1', 'y_1'],
parent_dim=[x2s_dim, z2s_dim, y_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1', 'y_1'],
parent_dim=[x2s_dim, rnn_dim, y2s_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit=typeActivFunc,
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
y_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
y_1_temp = y_1.fprop([y], params)
def inner_fn_train(x_t, y_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1, y_t], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([x_t, s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, theta_mu_t, theta_sig_t, coeff_t, pred #, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_fn_train,
sequences=[x_1_temp, y_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
#s_temp = concatenate([s_0[None, :, :], s_temp[:-1]], axis=0)# seems like this is for creating an additional dimension to s_0
theta_mu_temp.name = 'theta_mu_temp'
theta_sig_temp.name = 'theta_sig_temp'
coeff_temp.name = 'coeff'
if (flgAgg == -1):
prediction.name = 'x_reconstructed'
mse = T.mean((prediction - x)**2) # CHECK RESHAPE with an assertion
mae = T.mean(T.abs(prediction - x))
mse.name = 'mse'
pred_in = x.reshape((x_shape[0] * x_shape[1], -1))
else:
prediction.name = 'pred_' + str(flgAgg)
mse = T.mean(
(prediction - y)**2) # As axis = None is calculated for all
mae = T.mean(T.abs_(prediction - y))
mse.name = 'mse'
mae.name = 'mae'
pred_in = y.reshape((y.shape[0] * y.shape[1], -1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
theta_mu_in = theta_mu_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0] * x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(
pred_in, theta_mu_in, theta_sig_in, coeff_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
model.inputs = [x, mask, y, y_mask, scheduleSamplingMask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
header = "epoch,log,kl,nll_upper_bound,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(
freq=monitoring_freq,
ddout=[
nll_upper_bound, recon_term, kl_term, mse, mae, theta_mu_temp,
prediction
],
indexSep=5,
instancesPlot=instancesPlot, #{0:[4,20],2:[5,10]},#, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr}
mainloop = Training(
name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[recon_term, kl_term, nll_upper_bound, mse, mae],
n_steps=n_steps,
extension=extension,
lr_iterations=lr_iterations,
k_speedOfconvergence=kSchedSamp)
mainloop.run()
fLog = open(save_path + '/output.csv', 'w')
fLog.write(str(lr_iterations) + "\n")
fLog.write(str(windows) + "\n")
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
y2s_dim, z2s_dim))
header = "epoch,log,kl,mse,mae\n"
fLog.write(header)
for i, item in enumerate(mainloop.trainlog.monitor['recon_term']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{:d},{:.2f},{:.2f},{:.3f},{:.3f}\n".format(f, a, b, d, e))
def main(args):
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'valid_nll_upper_bound'
data_path = args['data_path']
save_path = args['save_path']
monitoring_freq = int(args['monitoring_freq'])
force_saving_freq = int(args['force_saving_freq'])
reset_freq = int(args['reset_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
m_batch_size = int(args['m_batch_size'])
x_dim = int(args['x_dim'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k'])
lr = float(args['lr'])
debug = int(args['debug'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 500
p_z_dim = 500
p_x_dim = 500
x2s_dim = 500
z2s_dim = 500
target_dim = x_dim * k
file_name = 'blizzard_unseg_tbptt'
normal_params = np.load(data_path + file_name + '_normal.npz')
X_mean = normal_params['X_mean']
X_std = normal_params['X_std']
model = Model()
train_data = Blizzard_tbptt(name='train',
path=data_path,
frame_size=x_dim,
file_name=file_name,
X_mean=X_mean,
X_std=X_std)
valid_data = Blizzard_tbptt(name='valid',
path=data_path,
frame_size=x_dim,
file_name=file_name,
X_mean=X_mean,
X_std=X_std)
x = train_data.theano_vars()
m_x = valid_data.theano_vars()
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=theano.config.floatX)
m_x.tag.test_value = np.zeros((15, m_batch_size, x_dim), dtype=theano.config.floatX)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
x_2 = FullyConnectedLayer(name='x_2',
parent=['x_1'],
parent_dim=[x2s_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
x_3 = FullyConnectedLayer(name='x_3',
parent=['x_2'],
parent_dim=[x2s_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
x_4 = FullyConnectedLayer(name='x_4',
parent=['x_3'],
parent_dim=[x2s_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_2 = FullyConnectedLayer(name='z_2',
parent=['z_1'],
parent_dim=[z2s_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_3 = FullyConnectedLayer(name='z_3',
parent=['z_2'],
parent_dim=[z2s_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_4 = FullyConnectedLayer(name='z_4',
parent=['z_3'],
parent_dim=[z2s_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_4', 'z_4'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_4', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_2 = FullyConnectedLayer(name='phi_2',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_3 = FullyConnectedLayer(name='phi_3',
parent=['phi_2'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_4 = FullyConnectedLayer(name='phi_4',
parent=['phi_3'],
parent_dim=[q_z_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_4'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_4'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_2 = FullyConnectedLayer(name='prior_2',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_3 = FullyConnectedLayer(name='prior_3',
parent=['prior_2'],
parent_dim=[p_z_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_4 = FullyConnectedLayer(name='prior_4',
parent=['prior_3'],
parent_dim=[p_z_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_4'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_4'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_4', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_2 = FullyConnectedLayer(name='theta_2',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_3 = FullyConnectedLayer(name='theta_3',
parent=['theta_2'],
parent_dim=[p_x_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_4 = FullyConnectedLayer(name='theta_4',
parent=['theta_3'],
parent_dim=[p_x_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_4'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_4'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_4'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
nodes = [rnn,
x_1, x_2, x_3, x_4,
z_1, z_2, z_3, z_4,
phi_1, phi_2, phi_3, phi_4, phi_mu, phi_sig,
prior_1, prior_2, prior_3, prior_4, prior_mu, prior_sig,
theta_1, theta_2, theta_3, theta_4, theta_mu, theta_sig, coeff]
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
step_count = sharedX(0, name='step_count')
last_rnn = np.zeros((batch_size, rnn_dim*2), dtype=theano.config.floatX)
rnn_tm1 = sharedX(last_rnn, name='rnn_tm1')
shared_updates = OrderedDict()
shared_updates[step_count] = step_count + 1
s_0 = T.switch(T.eq(T.mod(step_count, reset_freq), 0),
rnn.get_init_state(batch_size), rnn_tm1)
x_shape = x.shape
x_in = x.reshape((x_shape[0]*x_shape[1], -1))
x_1_in = x_1.fprop([x_in], params)
x_2_in = x_2.fprop([x_1_in], params)
x_3_in = x_3.fprop([x_2_in], params)
x_4_in = x_4.fprop([x_3_in], params)
x_4_in = x_4_in.reshape((x_shape[0], x_shape[1], -1))
def inner_fn(x_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_2_t = phi_2.fprop([phi_1_t], params)
phi_3_t = phi_3.fprop([phi_2_t], params)
phi_4_t = phi_4.fprop([phi_3_t], params)
phi_mu_t = phi_mu.fprop([phi_4_t], params)
phi_sig_t = phi_sig.fprop([phi_4_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_2_t = prior_2.fprop([prior_1_t], params)
prior_3_t = prior_3.fprop([prior_2_t], params)
prior_4_t = prior_4.fprop([prior_3_t], params)
prior_mu_t = prior_mu.fprop([prior_4_t], params)
prior_sig_t = prior_sig.fprop([prior_4_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
z_2_t = z_2.fprop([z_1_t], params)
z_3_t = z_3.fprop([z_2_t], params)
z_4_t = z_4.fprop([z_3_t], params)
s_t = rnn.fprop([[x_t, z_4_t], [s_tm1]], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_4_t
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp, z_4_temp), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_4_in],
outputs_info=[s_0, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
shared_updates[rnn_tm1] = s_temp[-1]
s_temp = concatenate([s_0[None, :, :], s_temp[:-1]], axis=0)
theta_1_temp = theta_1.fprop([z_4_temp, s_temp], params)
theta_2_temp = theta_2.fprop([theta_1_temp], params)
theta_3_temp = theta_3.fprop([theta_2_temp], params)
theta_4_temp = theta_4.fprop([theta_3_temp], params)
theta_mu_temp = theta_mu.fprop([theta_4_temp], params)
theta_sig_temp = theta_sig.fprop([theta_4_temp], params)
coeff_temp = coeff.fprop([theta_4_temp], params)
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp)
x_shape = x.shape
x_in = x.reshape((x_shape[0]*x_shape[1], -1))
theta_mu_in = theta_mu_temp.reshape((x_shape[0]*x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0]*x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(x_in, theta_mu_in, theta_sig_in, coeff_in)
recon_term = recon.mean()
kl_term = kl_temp.mean()
nll_upper_bound = recon_term + kl_term
nll_upper_bound.name = 'nll_upper_bound'
m_x_1_temp = x_1.fprop([m_x], params)
m_x_2_temp = x_2.fprop([m_x_1_temp], params)
m_x_3_temp = x_3.fprop([m_x_2_temp], params)
m_x_4_temp = x_4.fprop([m_x_3_temp], params)
m_s_0 = rnn.get_init_state(m_batch_size)
((m_s_temp, m_phi_mu_temp, m_phi_sig_temp, m_prior_mu_temp, m_prior_sig_temp, m_z_4_temp), m_updates) =\
theano.scan(fn=inner_fn,
sequences=[m_x_4_temp],
outputs_info=[m_s_0, None, None, None, None, None])
for k, v in m_updates.iteritems():
k.default_update = v
m_s_temp = concatenate([m_s_0[None, :, :], m_s_temp[:-1]], axis=0)
m_theta_1_temp = theta_1.fprop([m_z_4_temp, m_s_temp], params)
m_theta_2_temp = theta_2.fprop([m_theta_1_temp], params)
m_theta_3_temp = theta_3.fprop([m_theta_2_temp], params)
m_theta_4_temp = theta_4.fprop([m_theta_3_temp], params)
m_theta_mu_temp = theta_mu.fprop([m_theta_4_temp], params)
m_theta_sig_temp = theta_sig.fprop([m_theta_4_temp], params)
m_coeff_temp = coeff.fprop([m_theta_4_temp], params)
m_kl_temp = KLGaussianGaussian(m_phi_mu_temp, m_phi_sig_temp, m_prior_mu_temp, m_prior_sig_temp)
m_x_shape = m_x.shape
m_x_in = m_x.reshape((m_x_shape[0]*m_x_shape[1], -1))
m_theta_mu_in = m_theta_mu_temp.reshape((m_x_shape[0]*m_x_shape[1], -1))
m_theta_sig_in = m_theta_sig_temp.reshape((m_x_shape[0]*m_x_shape[1], -1))
m_coeff_in = m_coeff_temp.reshape((m_x_shape[0]*m_x_shape[1], -1))
m_recon = GMM(m_x_in, m_theta_mu_in, m_theta_sig_in, m_coeff_in)
m_recon_term = m_recon.mean()
m_kl_term = m_kl_temp.mean()
m_nll_upper_bound = m_recon_term + m_kl_term
m_nll_upper_bound.name = 'nll_upper_bound'
m_recon_term.name = 'recon_term'
m_kl_term.name = 'kl_term'
max_x = m_x.max()
mean_x = m_x.mean()
min_x = m_x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
max_theta_mu = m_theta_mu_in.max()
mean_theta_mu = m_theta_mu_in.mean()
min_theta_mu = m_theta_mu_in.min()
max_theta_mu.name = 'max_theta_mu'
mean_theta_mu.name = 'mean_theta_mu'
min_theta_mu.name = 'min_theta_mu'
max_theta_sig = m_theta_sig_in.max()
mean_theta_sig = m_theta_sig_in.mean()
min_theta_sig = m_theta_sig_in.min()
max_theta_sig.name = 'max_theta_sig'
mean_theta_sig.name = 'mean_theta_sig'
min_theta_sig.name = 'min_theta_sig'
max_phi_sig = m_phi_sig_temp.max()
mean_phi_sig = m_phi_sig_temp.mean()
min_phi_sig = m_phi_sig_temp.min()
max_phi_sig.name = 'max_phi_sig'
mean_phi_sig.name = 'mean_phi_sig'
min_phi_sig.name = 'min_phi_sig'
max_prior_sig = m_prior_sig_temp.max()
mean_prior_sig = m_prior_sig_temp.mean()
min_prior_sig = m_prior_sig_temp.min()
max_prior_sig.name = 'max_prior_sig'
mean_prior_sig.name = 'mean_prior_sig'
min_prior_sig.name = 'min_prior_sig'
model.inputs = [x]
model.params = params
model.nodes = nodes
model.set_updates(shared_updates)
optimizer = Adam(
lr=lr
)
monitor_fn = theano.function(inputs=[m_x],
outputs=[m_nll_upper_bound, m_recon_term, m_kl_term,
max_phi_sig, mean_phi_sig, min_phi_sig,
max_prior_sig, mean_prior_sig, min_prior_sig,
max_theta_sig, mean_theta_sig, min_theta_sig,
max_x, mean_x, min_x,
max_theta_mu, mean_theta_mu, min_theta_mu],
on_unused_input='ignore')
extension = [
GradientClipping(batch_size=batch_size, check_nan=1),
EpochCount(epoch),
Monitoring(freq=monitoring_freq,
monitor_fn=monitor_fn,
ddout=[m_nll_upper_bound, m_recon_term, m_kl_term,
max_phi_sig, mean_phi_sig, min_phi_sig,
max_prior_sig, mean_prior_sig, min_prior_sig,
max_theta_sig, mean_theta_sig, min_theta_sig,
max_x, mean_x, min_x,
max_theta_mu, mean_theta_mu, min_theta_mu],
data=[Iterator(train_data, m_batch_size, start=0, end=112640),
Iterator(valid_data, m_batch_size, start=2040064, end=2152704)]),
Picklize(freq=monitoring_freq, force_save_freq=force_saving_freq, path=save_path),
EarlyStopping(freq=monitoring_freq, force_save_freq=force_saving_freq, path=save_path, channel=channel_name),
WeightNorm()
]
mainloop = Training(
name=pkl_name,
data=Iterator(train_data, batch_size, start=0, end=2040064),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
extension=extension
)
mainloop.run()
theano.scan(fn=inner_fn,
sequences=[x],
outputs_info=[coder.get_init_state(),
None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
reshaped_x = x.reshape((x.shape[0]*x.shape[1], -1))
reshaped_theta_mu = theta_mu_t.reshape((theta_mu_t.shape[0]*theta_mu_t.shape[1], -1))
reshaped_theta_sig = theta_sig_t.reshape((theta_sig_t.shape[0]*theta_sig_t.shape[1], -1))
reshaped_coeff = coeff_t.reshape((coeff_t.shape[0]*coeff_t.shape[1], -1))
reshaped_mask = mask.flatten()
kl_term = kl_t.reshape((kl_t.shape[0]*kl_t.shape[1], -1))
recon_term = GMM(reshaped_x, reshaped_theta_mu, reshaped_theta_sig, reshaped_coeff)
# Apply mask
kl_term = kl_term[reshaped_mask.nonzero()].mean()
recon_term = recon_term[reshaped_mask.nonzero()].mean()
cost = recon_term + kl_term
cost.name = 'cost'
recon_term.name = 'recon_term'
kl_term.name = 'kl_term'
max_x = x.max()
mean_x = x.mean()
min_x = x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
def main(args):
theano.optimizer = 'fast_compile'
theano.config.exception_verbosity = 'high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'mse_val'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
genCase = int(args['genCase'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps # int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
debug = int(args['debug'])
num_sequences_per_batch = int(args['numSequences']) #based on appliance
loadParam = args['loadAsKelly']
target_inclusion_prob = float(args['target_inclusion_prob'])
loadAsKelly = True
if (loadParam == 'N' or loadParam == 'n' or loadParam == 'no'
or loadParam == 'NO' or loadParam == 'No'):
loadAsKelly = False
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 60 #150
p_z_dim = 60 #150
p_x_dim = 30 #250
x2s_dim = 40 #250
z2s_dim = 40 #150
target_dim = k #x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, reader = fetch_ukdale(
data_path,
windows,
appliances,
numApps=flgAgg,
period=period,
n_steps=n_steps,
stride_train=stride_train,
stride_test=stride_test,
flgAggSumScaled=1,
flgFilterZeros=1,
isKelly=loadAsKelly,
seq_per_batch=num_sequences_per_batch,
target_inclusion_prob=target_inclusion_prob)
instancesPlot = {
0: [10, 20],
2: [20, 30]
} #for now use hard coded instancesPlot for kelly sampling
if (not loadAsKelly):
instancesPlot = reader.build_dict_instances_plot(
listDates, batch_size, Xval.shape[0])
############# We switch x with y
train_data = UKdale(
name='train',
prep='normalize',
cond=False,
#path=data_path,
validTime=0,
inputX=ytrain,
labels=Xtrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=False,
#path=data_path,
X_mean=X_mean,
X_std=X_std,
validTime=1,
inputX=yval,
labels=Xval)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask = train_data.theano_vars()
#valTime = train_data.theano_valTime_vars()
if (genCase == 1):
inputX = x[:-1, :]
targetX = x[1:, :]
n_steps = n_steps - 1
else:
inputX = x
targetX = x
inputX.name = 'x_original'
if debug:
inputX.tag.test_value = np.zeros((15, batch_size, x_dim),
dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize()) #Creates matrices
params = init_tparams(params) #Make the parameters theano.shared
s_0_tr = rnn.get_init_state(batch_size)
s_0_val = T.zeros((batch_size, 2 * rnn_dim), dtype=theano.config.floatX)
s_0_val = T.unbroadcast(
s_0_val, *range(s_0_val.ndim)
) #[0,1] this is to raise an error if length of dimensions are not 1
#x_1_temp = x_1.fprop([x], params)
def inner_val_fn(s_tm1):
'''
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
'''
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
x_t = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
x_1_t = x_1.fprop([x_t], params)
s_t = rnn.fprop([[x_1_t, z_1_t], [s_tm1]], params)
return s_t, x_t, z_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t
# prior_mu_temp_val, prior_sig_temp_val
((s_temp_val, prediction_val, z_t_temp_val, theta_1_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val), updates_val) =\
theano.scan(fn=inner_val_fn , n_steps=n_steps, #already 1 subtracted if doing next step
outputs_info=[s_0_val, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
def inner_train_fn(x_t, s_tm1):
x_1_t = x_1.fprop([x_t], params)
phi_1_t = phi_1.fprop([x_1_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_1_t, z_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred #, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_train_fn, sequences=[inputX],#[x_1_temp],
outputs_info=[s_0_tr, None, None, None, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
######### TRAINING GRAPH #########
s_temp = concatenate(
[s_0_tr[None, :, :], s_temp[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
s_temp.name = 'h_1' #gisse
z_1_temp.name = 'z_1' #gisse
z_t_temp.name = 'z'
theta_mu_temp.name = 'theta_mu_temp'
theta_sig_temp.name = 'theta_sig_temp'
coeff_temp.name = 'coeff'
prediction.name = 'pred_' + str(flgAgg)
mse = T.mean(
(prediction - targetX)**2) # As axis = None is calculated for all
mae = T.mean(T.abs_(prediction - targetX))
mse.name = 'mse'
mae.name = 'mae'
x_in = inputX.reshape((batch_size * n_steps, -1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
target_shape = x[:, 1:].shape
theta_mu_in = theta_mu_temp.reshape((batch_size * n_steps, -1))
theta_sig_in = theta_sig_temp.reshape((batch_size * n_steps, -1))
coeff_in = coeff_temp.reshape((batch_size * n_steps, -1))
recon = GMM(
x_in, theta_mu_in, theta_sig_in, coeff_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((batch_size, n_steps))
recon.name = 'gmm_out'
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
######### TESTING GRAPH #########
s_temp_val = concatenate(
[s_0_val[None, :, :], s_temp_val[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
s_temp_val.name = 'h_1_val' #gisse
#z_1_temp_val.name = 'z_1_val'#gisse
z_t_temp_val.name = 'z_val'
theta_mu_temp_val.name = 'theta_mu_temp_val'
theta_sig_temp_val.name = 'theta_sig_temp_val'
coeff_temp_val.name = 'coeff_val'
prediction_val.name = 'generated_' + str(flgAgg)
mse_val = T.mean(
(prediction_val - targetX)**2) # As axis = None is calculated for all
mae_val = T.mean(T.abs_(prediction_val - targetX))
mse_val.name = 'mse_val'
mae_val.name = 'mae_val'
x_in_val = inputX.reshape((batch_size * n_steps, -1))
# No sense in calculate distance to a distribution because we are not calculating phi
#kl_temp_val = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp)
theta_mu_in_val = theta_mu_temp_val.reshape((batch_size * n_steps, -1))
theta_sig_in_val = theta_sig_temp_val.reshape((batch_size * n_steps, -1))
coeff_in_val = coeff_temp_val.reshape((batch_size * n_steps, -1))
recon_val = GMM(
x_in_val, theta_mu_in_val, theta_sig_in_val, coeff_in_val
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((batch_size, n_steps))
recon_val.name = 'gmm_out_val'
recon_term_val = recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term_val'
######################################
model.inputs = [x, mask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
header = "epoch,log,kl,nll,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(
freq=monitoring_freq,
ddout=[
recon_term_val, mse_val, mae_val, prediction_val,
theta_mu_temp_val
],
indexSep=3,
indexDDoutPlot=[(0, prediction_val)],
instancesPlot=instancesPlot, #, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
lr_iterations = {0: lr}
mainloop = Training(
name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[recon_term, kl_term, nll_upper_bound, mse, mae],
extension=extension,
lr_iterations=lr_iterations)
mainloop.run()
fLog = open(save_path + '/output.csv', 'w')
fLog.write(str(lr_iterations) + "\n")
fLog.write(str(windows) + "\n")
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{}\n".format(q_z_dim, p_z_dim, p_x_dim, x2s_dim,
z2s_dim))
header = "epoch,log,mse,mae\n"
fLog.write(header)
for i, item in enumerate(mainloop.trainlog.monitor['recon_term_val']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term_val'][i]
d = mainloop.trainlog.monitor['mse_val'][i]
e = mainloop.trainlog.monitor['mae_val'][i]
fLog.write("{},{},{},{}\n".format(f, a, d, e))
def main(args):
#theano.optimizer='fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'nll_upper_bound'
data_path = args['data_path']
save_path = args['save_path'] #+'/gmm/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
flgMSE = int(args['flgMSE'])
genCase = int(args['genCase'])
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = n_steps#int(args['stride_test'])
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
flgAgg = int(args['flgAgg'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
debug = int(args['debug'])
num_sequences_per_batch = int(args['numSequences']) #based on appliance
typeLoad = int(args['typeLoad'])
target_inclusion_prob = float(args['target_inclusion_prob'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 150
p_z_dim = 150
p_x_dim = 200
x2s_dim = 100
y2s_dim = 100
z2s_dim = 100
target_dim = k#x_dim #(x_dim-1)*k
model = Model()
Xtrain, ytrain, Xval, yval, Xtest, ytest, reader = fetch_ukdale(data_path, windows, appliances,numApps=flgAgg, period=period,
n_steps= n_steps, stride_train = stride_train, stride_test = stride_test,
typeLoad= typeLoad, flgAggSumScaled = 1, flgFilterZeros = 1,
seq_per_batch=num_sequences_per_batch, target_inclusion_prob=target_inclusion_prob)
instancesPlot = {0:[4,20], 2:[5,10]} #for now use hard coded instancesPlot for kelly sampling
if(typeLoad==0):
instancesPlot = reader.build_dict_instances_plot(listDates, batch_size, Xval.shape[0])
train_data = UKdale(name='train',
prep='normalize',
cond=True,# False
#path=data_path,
inputX=Xtrain,
labels=ytrain)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(name='valid',
prep='normalize',
cond=True,# False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xval,
labels = yval)
test_data = UKdale(name='valid',
prep='normalize',
cond=True,# False
#path=data_path,
X_mean=X_mean,
X_std=X_std,
inputX=Xtest,
labels = ytest)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y , y_mask = train_data.theano_vars()
if (genCase ==1):
inputX = x[:-1,:]
targetX = x[1:,:]
n_steps = n_steps-1
else:
inputX = x
targetX = x
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
y_1 = FullyConnectedLayer(name='y_1',
parent=['y_t'],
parent_dim=[y_dim],
nout=y2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1','y_1'],
parent_dim=[x2s_dim, z2s_dim, y_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1','y_1'],
parent_dim=[x2s_dim, rnn_dim,y2s_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax',
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [rnn,
x_1, y_1, z_1, #dissag_pred,
phi_1, phi_mu, phi_sig,
prior_1, prior_mu, prior_sig,
theta_1, theta_mu, theta_sig, coeff]#, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
y_1_temp = y_1.fprop([y], params)
def inner_fn_val(x_t, s_tm1):
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(prior_mu_t, prior_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
pred_t = GMM_sample(theta_mu_t, theta_sig_t, coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
pred_1_t = y_1.fprop([pred_t], params)
s_t = rnn.fprop([[x_t, z_1_t, pred_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred_t#, y_pred
#corr_temp, binary_temp
((s_temp_val, prior_mu_temp_val, prior_sig_temp_val, z_t_temp_val, z_1_temp_val, theta_1_temp_val, theta_mu_temp_val, theta_sig_temp_val, coeff_temp_val, prediction_val), updates_val) =\
theano.scan(fn=inner_fn_val,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None, None])
for k, v in updates_val.iteritems():
k.default_update = v
s_temp_val = concatenate([s_0[None, :, :], s_temp_val[:-1]], axis=0)
def inner_fn_train(x_t, y_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1,y_t], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
pred = GMM_sample(theta_mu_t, theta_sig_t, coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_t, z_1_t, y_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, pred#, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp, theta_mu_temp, theta_sig_temp, coeff_temp, prediction), updates) =\
theano.scan(fn=inner_fn_train,
sequences=[x_1_temp, y_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
s_temp = concatenate([s_0[None, :, :], s_temp[:-1]], axis=0)# seems like this is for creating an additional dimension to s_0
s_temp.name = 'h_1'#gisse
z_1_temp.name = 'z_1'#gisse
z_t_temp.name = 'z'
theta_mu_temp.name = 'theta_mu_temp'
theta_sig_temp.name = 'theta_sig_temp'
coeff_temp.name = 'coeff'
if (flgAgg == -1 ):
prediction.name = 'x_reconstructed'
mse = T.mean((prediction - x)**2) # CHECK RESHAPE with an assertion
mae = T.mean( T.abs(prediction - x) )
mse.name = 'mse'
pred_in = x.reshape((x_shape[0]*x_shape[1], -1))
else:
prediction.name = 'pred_'+str(flgAgg)
mse = T.mean((prediction - y)**2) # As axis = None is calculated for all
mae = T.mean( T.abs_(prediction - y) )
mse.name = 'mse'
mae.name = 'mae'
pred_in = y.reshape((y.shape[0]*y.shape[1],-1))
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp)
x_shape = x.shape
theta_mu_in = theta_mu_temp.reshape((x_shape[0]*x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0]*x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0]*x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
recon = GMM(pred_in, theta_mu_in, theta_sig_in, coeff_in)# BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
nll_upper_bound = recon_term + kl_term #+ mse
if (flgMSE):
nll_upper_bound = nll_upper_bound + mse
nll_upper_bound.name = 'nll_upper_bound'
######################## TEST (GENERATION) TIME
prediction_val.name = 'generated__'+str(flgAgg)
mse_val = T.mean((prediction_val - y)**2) # As axis = None is calculated for all
mae_val = T.mean( T.abs_(prediction_val - y) )
mse_val.name = 'mse_val'
mae_val.name = 'mae_val'
pred_in_val = y.reshape((y.shape[0]*y.shape[1],-1))
theta_mu_in_val = theta_mu_temp_val.reshape((x_shape[0]*x_shape[1], -1))
theta_sig_in_val = theta_sig_temp_val.reshape((x_shape[0]*x_shape[1], -1))
coeff_in_val = coeff_temp_val.reshape((x_shape[0]*x_shape[1], -1))
recon_val = GMM(pred_in_val, theta_mu_in_val, theta_sig_in_val, coeff_in_val)# BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon_val = recon_val.reshape((x_shape[0], x_shape[1]))
recon_val.name = 'gmm_out_val'
recon_term_val= recon_val.sum(axis=0).mean()
recon_term_val.name = 'recon_term_val'
model.inputs = [x, mask, y, y_mask]
model.params = params
model.nodes = nodes
optimizer = Adam(
lr=lr
)
header = "epoch,log,kl,nll_upper_bound,mse,mae\n"
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch, save_path, header),
Monitoring(freq=monitoring_freq,
ddout=[nll_upper_bound, recon_term, kl_term, mse, mae,
theta_mu_temp,prediction],
indexSep=5,
instancesPlot = instancesPlot, #{0:[4,20],2:[5,10]},#, 80,150
data=[Iterator(valid_data, batch_size)],
savedFolder = save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq, path=save_path, channel=channel_name),
WeightNorm()
]
lr_iterations = {0:lr, 75:(lr/10), 150:(lr/100)}
mainloop = Training(
name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[recon_term, kl_term, nll_upper_bound, mse, mae],
extension=extension,
lr_iterations=lr_iterations
)
mainloop.run()
z_t_temp_val.name='z_temp_val'
s_temp_val.name='s_temp_val'
theta_mu_temp_val.name='mu_temp'
data=Iterator(test_data, batch_size)
test_fn = theano.function(inputs=[x, y],#[x, y],
#givens={x:Xtest},
#on_unused_input='ignore',
#z=( ,200,1)
allow_input_downcast=True,
outputs=[z_t_temp_val, s_temp_val, theta_mu_temp_val, prediction_val, recon_term_val, mse_val, mae_val]#prediction_val, mse_val, mae_val
,updates=updates_val#, allow_input_downcast=True, on_unused_input='ignore'
)
testOutput = []
numBatchTest = 0
for batch in data:
outputGeneration = test_fn(batch[0], batch[2])
testOutput.append(outputGeneration[4:])
#{0:[4,20], 2:[5,10]}
#if (numBatchTest==0):
'''
plt.figure(1)
plt.plot(np.transpose(outputGeneration[0],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_z_0-4".format(numBatchTest))
plt.clf()
plt.figure(2)
plt.plot(np.transpose(outputGeneration[1],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_s_0-4".format(numBatchTest))
plt.clf()
plt.figure(3)
plt.plot(np.transpose(outputGeneration[2],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_theta_0-4".format(numBatchTest))
plt.clf()
'''
plt.figure(4)
plt.plot(np.transpose(outputGeneration[3],[1,0,2])[4])
plt.plot(np.transpose(batch[2],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_RealAndPred_0-4".format(numBatchTest))
plt.clf()
plt.figure(4)
plt.plot(np.transpose(batch[0],[1,0,2])[4])
plt.savefig(save_path+"/vrnn_dis_generated{}_Realagg_0-4".format(numBatchTest))
plt.clf()
numBatchTest+=1
testOutput = np.asarray(testOutput)
print(testOutput.shape)
recon_test = this_mean = testOutput[:, 0].mean()
mse_test = this_mean = testOutput[:, 1].mean()
mae_test = this_mean = testOutput[:, 2].mean()
fLog = open(save_path+'/output.csv', 'w')
fLog.write(str(lr_iterations)+"\n")
fLog.write(str(windows)+"\n")
fLog.write("logTest,mseTest,maeTest\n")
fLog.write("{},{},{}\n".format(recon_test,mse_test,mae_test))
fLog.write("q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim\n")
fLog.write("{},{},{},{},{},{}\n".format(q_z_dim,p_z_dim,p_x_dim,x2s_dim,y2s_dim,z2s_dim))
header = "epoch,log,kl,mse,mae\n"
fLog.write(header)
for i , item in enumerate(mainloop.trainlog.monitor['recon_term']):
f = mainloop.trainlog.monitor['epoch'][i]
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
d = mainloop.trainlog.monitor['mse'][i]
e = mainloop.trainlog.monitor['mae'][i]
fLog.write("{:d},{:.2f},{:.2f},{:.3f},{:.3f}\n".format(f,a,b,d,e))
def main(args):
theano.optimizer = 'fast_compile'
#theano.config.exception_verbosity='high'
trial = int(args['trial'])
pkl_name = 'vrnn_gmm_%d' % trial
channel_name = 'valid_nll_upper_bound'
data_path = args['data_path']
save_path = args[
'save_path'] #+'/aggVSdisag_distribEach/'+datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
period = int(args['period'])
n_steps = int(args['n_steps'])
stride_train = int(args['stride_train'])
stride_test = int(args['stride_test'])
# monitoring is a value that represents how many batches from training have to be seen to measure the validation set
monitoring_freq = int(args['monitoring_freq'])
epoch = int(args['epoch'])
batch_size = int(args['batch_size'])
x_dim = int(args['x_dim'])
y_dim = int(args['y_dim'])
z_dim = int(args['z_dim'])
rnn_dim = int(args['rnn_dim'])
k = int(args['num_k']) #a mixture of K Gaussian functions
lr = float(args['lr'])
debug = int(args['debug'])
print "trial no. %d" % trial
print "batch size %d" % batch_size
print "learning rate %f" % lr
print "saving pkl file '%s'" % pkl_name
print "to the save path '%s'" % save_path
q_z_dim = 8 #150
p_z_dim = 8 #150
p_x_dim = 7 #250
x2s_dim = 7 #250
z2s_dim = 8 #150
target_dim = y_dim * k #(x_dim-1)*k
model = Model()
train_data = UKdale(
name='train',
prep='normalize',
cond=True, # False
path=data_path,
period=period,
n_steps=n_steps,
x_dim=x_dim,
stride_train=stride_train,
stride_test=stride_test)
X_mean = train_data.X_mean
X_std = train_data.X_std
valid_data = UKdale(
name='valid',
prep='normalize',
cond=True, # False
path=data_path,
X_mean=X_mean,
X_std=X_std,
period=period,
n_steps=n_steps,
x_dim=x_dim,
stride_train=stride_train,
stride_test=stride_test)
init_W = InitCell('rand')
init_U = InitCell('ortho')
init_b = InitCell('zeros')
init_b_sig = InitCell('const', mean=0.6)
x, mask, y, y_mask = train_data.theano_vars()
x.name = 'x_original'
if debug:
x.tag.test_value = np.zeros((15, batch_size, x_dim), dtype=np.float32)
temp = np.ones((15, batch_size), dtype=np.float32)
temp[:, -2:] = 0.
mask.tag.test_value = temp
x_1 = FullyConnectedLayer(name='x_1',
parent=['x_t'],
parent_dim=[x_dim],
nout=x2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
z_1 = FullyConnectedLayer(name='z_1',
parent=['z_t'],
parent_dim=[z_dim],
nout=z2s_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
rnn = LSTM(name='rnn',
parent=['x_1', 'z_1'],
parent_dim=[x2s_dim, z2s_dim],
nout=rnn_dim,
unit='tanh',
init_W=init_W,
init_U=init_U,
init_b=init_b)
phi_1 = FullyConnectedLayer(name='phi_1',
parent=['x_1', 's_tm1'],
parent_dim=[x2s_dim, rnn_dim],
nout=q_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
phi_mu = FullyConnectedLayer(name='phi_mu',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
phi_sig = FullyConnectedLayer(name='phi_sig',
parent=['phi_1'],
parent_dim=[q_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
prior_1 = FullyConnectedLayer(name='prior_1',
parent=['s_tm1'],
parent_dim=[rnn_dim],
nout=p_z_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
prior_mu = FullyConnectedLayer(name='prior_mu',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
prior_sig = FullyConnectedLayer(name='prior_sig',
parent=['prior_1'],
parent_dim=[p_z_dim],
nout=z_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
theta_1 = FullyConnectedLayer(name='theta_1',
parent=['z_1', 's_tm1'],
parent_dim=[z2s_dim, rnn_dim],
nout=p_x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='linear',
init_W=init_W,
init_b=init_b)
theta_sig = FullyConnectedLayer(name='theta_sig',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
coeff = FullyConnectedLayer(
name='coeff',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='softmax', #to ensure that the sum adds to one
init_W=init_W,
init_b=init_b)
corr = FullyConnectedLayer(name='corr',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=k,
unit='tanh',
init_W=init_W,
init_b=init_b)
binary = FullyConnectedLayer(name='binary',
parent=['theta_1'],
parent_dim=[p_x_dim],
nout=1,
unit='sigmoid',
init_W=init_W,
init_b=init_b)
nodes = [
rnn,
x_1,
z_1, #dissag_pred,
phi_1,
phi_mu,
phi_sig,
prior_1,
prior_mu,
prior_sig,
theta_1,
theta_mu,
theta_sig,
coeff
] #, corr, binary
params = OrderedDict()
for node in nodes:
if node.initialize() is not None:
params.update(node.initialize())
params = init_tparams(params)
s_0 = rnn.get_init_state(batch_size)
x_1_temp = x_1.fprop([x], params)
def inner_fn(x_t, s_tm1):
phi_1_t = phi_1.fprop([x_t, s_tm1], params)
phi_mu_t = phi_mu.fprop([phi_1_t], params)
phi_sig_t = phi_sig.fprop([phi_1_t], params)
prior_1_t = prior_1.fprop([s_tm1], params)
prior_mu_t = prior_mu.fprop([prior_1_t], params)
prior_sig_t = prior_sig.fprop([prior_1_t], params)
z_t = Gaussian_sample(
phi_mu_t, phi_sig_t
) #in the original code it is gaussian. GMM is for the generation
z_1_t = z_1.fprop([z_t], params)
theta_1_t = theta_1.fprop([z_1_t, s_tm1], params)
theta_mu_t = theta_mu.fprop([theta_1_t], params)
theta_sig_t = theta_sig.fprop([theta_1_t], params)
coeff_t = coeff.fprop([theta_1_t], params)
#corr_t = corr.fprop([theta_1_t], params)
#binary_t = binary.fprop([theta_1_t], params)
# I was missing this reshape that is done before BiGMM in the original code
'''
theta_mu_in = theta_mu_t.reshape((x_t[0]*x_t[1], -1))
theta_sig_in = theta_sig_t.reshape((x_t[0]*x_t[1], -1))
coeff_in = coeff_t.reshape((x_t[0]*x_t[1], -1))
'''
y_pred = GMM_sampleY(
theta_mu_t, theta_sig_t,
coeff_t) #Gaussian_sample(theta_mu_t, theta_sig_t)
s_t = rnn.fprop([[x_t, z_1_t], [s_tm1]], params)
#y_pred = dissag_pred.fprop([s_t], params)
return s_t, phi_mu_t, phi_sig_t, prior_mu_t, prior_sig_t, z_t, z_1_t, theta_1_t, theta_mu_t, theta_sig_t, coeff_t, y_pred
#corr_temp, binary_temp
((s_temp, phi_mu_temp, phi_sig_temp, prior_mu_temp, prior_sig_temp,z_t_temp, z_1_temp, theta_1_temp, theta_mu_temp, theta_sig_temp, coeff_temp, y_pred_temp), updates) =\
theano.scan(fn=inner_fn,
sequences=[x_1_temp],
outputs_info=[s_0, None, None, None, None, None, None, None, None, None, None, None])
for k, v in updates.iteritems():
k.default_update = v
s_temp = concatenate(
[s_0[None, :, :], s_temp[:-1]], axis=0
) # seems like this is for creating an additional dimension to s_0
'''
theta_1_temp = theta_1.fprop([z_1_temp, s_temp], params)
theta_mu_temp = theta_mu.fprop([theta_1_temp], params)
theta_sig_temp = theta_sig.fprop([theta_1_temp], params)
coeff_temp = coeff.fprop([theta_1_temp], params)
corr_temp = corr.fprop([theta_1_temp], params)
binary_temp = binary.fprop([theta_1_temp], params)
'''
s_temp.name = 'h_1' #gisse
z_1_temp.name = 'z_1' #gisse
z_t_temp.name = 'z'
theta_mu_temp.name = 'theta_mu'
theta_sig_temp.name = 'theta_sig'
coeff_temp.name = 'coeff'
#corr_temp.name = 'corr'
#binary_temp.name = 'binary'
#x_pred_temp.name = 'x_reconstructed'
y_pred_temp.name = 'disaggregation'
#mse = T.mean((y_pred_temp - y)**2) # cause mse can be 26000
#mse.name = 'mse'
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
x_shape = x.shape
y_shape = y.shape
x_in = x.reshape((x_shape[0] * x_shape[1], -1))
y_in = y.reshape((y_shape[0] * y_shape[1], -1))
theta_mu_in = theta_mu_temp.reshape((x_shape[0] * x_shape[1], -1))
theta_sig_in = theta_sig_temp.reshape((x_shape[0] * x_shape[1], -1))
coeff_in = coeff_temp.reshape((x_shape[0] * x_shape[1], -1))
#corr_in = corr_temp.reshape((x_shape[0]*x_shape[1], -1))
#binary_in = binary_temp.reshape((x_shape[0]*x_shape[1], -1))
#print("Printing shapes")
#print (y_in.shape, theta_mu_in.shape, theta_sig_in.shape, coeff_in.shape)
recon = GMM(
y_in, theta_mu_in, theta_sig_in, coeff_in
) # BiGMM(x_in, theta_mu_in, theta_sig_in, coeff_in, corr_in, binary_in)
recon = recon.reshape((x_shape[0], x_shape[1]))
recon.name = 'gmm_out'
#recon = recon * mask
recon_term = recon.sum(axis=0).mean()
recon_term.name = 'recon_term'
#kl_temp = kl_temp * mask
kl_term = kl_temp.sum(axis=0).mean()
kl_term.name = 'kl_term'
#nll_upper_bound_0 = recon_term + kl_term
#nll_upper_bound_0.name = 'nll_upper_bound_0'
nll_upper_bound = recon_term + kl_term #+ mse
nll_upper_bound.name = 'nll_upper_bound'
max_x = x.max()
mean_x = x.mean()
min_x = x.min()
max_x.name = 'max_x'
mean_x.name = 'mean_x'
min_x.name = 'min_x'
max_theta_mu = theta_mu_in.max()
mean_theta_mu = theta_mu_in.mean()
min_theta_mu = theta_mu_in.min()
max_theta_mu.name = 'max_theta_mu'
mean_theta_mu.name = 'mean_theta_mu'
min_theta_mu.name = 'min_theta_mu'
max_theta_sig = theta_sig_in.max()
mean_theta_sig = theta_sig_in.mean()
min_theta_sig = theta_sig_in.min()
max_theta_sig.name = 'max_theta_sig'
mean_theta_sig.name = 'mean_theta_sig'
min_theta_sig.name = 'min_theta_sig'
coeff_max = coeff_in.max()
coeff_min = coeff_in.min()
coeff_mean_max = coeff_in.mean(axis=0).max()
coeff_mean_min = coeff_in.mean(axis=0).min()
coeff_max.name = 'coeff_max'
coeff_min.name = 'coeff_min'
coeff_mean_max.name = 'coeff_mean_max'
coeff_mean_min.name = 'coeff_mean_min'
max_phi_sig = phi_sig_temp.max()
mean_phi_sig = phi_sig_temp.mean()
min_phi_sig = phi_sig_temp.min()
max_phi_sig.name = 'max_phi_sig'
mean_phi_sig.name = 'mean_phi_sig'
min_phi_sig.name = 'min_phi_sig'
max_prior_sig = prior_sig_temp.max()
mean_prior_sig = prior_sig_temp.mean()
min_prior_sig = prior_sig_temp.min()
max_prior_sig.name = 'max_prior_sig'
mean_prior_sig.name = 'mean_prior_sig'
min_prior_sig.name = 'min_prior_sig'
model.inputs = [x, mask, y, y_mask]
model.params = params
model.nodes = nodes
optimizer = Adam(lr=lr)
extension = [
GradientClipping(batch_size=batch_size),
EpochCount(epoch),
Monitoring(
freq=monitoring_freq,
ddout=[
nll_upper_bound,
recon_term,
kl_term, #mse,
max_phi_sig,
mean_phi_sig,
min_phi_sig,
max_prior_sig,
mean_prior_sig,
min_prior_sig,
max_theta_sig,
mean_theta_sig,
min_theta_sig,
max_x,
mean_x,
min_x,
max_theta_mu,
mean_theta_mu,
min_theta_mu,
coeff_max,
coeff_min,
coeff_mean_max,
coeff_mean_min, #23
theta_mu_temp,
theta_sig_temp,
z_t_temp,
y_pred_temp, #corr_temp, binary_temp,
coeff_temp, #22
s_temp,
z_1_temp
],
indexSep=22,
indexDDoutPlot=[(0, theta_mu_temp), (2, z_t_temp), (3, y_pred_temp)
], # adding indexes of ddout for the plotting
instancesPlot=[0, 150],
data=[Iterator(valid_data, batch_size)],
savedFolder=save_path),
Picklize(freq=monitoring_freq, path=save_path),
EarlyStopping(freq=monitoring_freq,
path=save_path,
channel=channel_name),
WeightNorm()
]
mainloop = Training(name=pkl_name,
data=Iterator(train_data, batch_size),
model=model,
optimizer=optimizer,
cost=nll_upper_bound,
outputs=[nll_upper_bound],
extension=extension)
mainloop.run()
fLog = open(save_path + '/output.csv', 'w')
print('Printing')
print(len(mainloop.trainlog.monitor['nll_upper_bound']))
fLog.write("log,kl,mse,nll_upper_bound\n")
for i, item in enumerate(mainloop.trainlog.monitor['nll_upper_bound']):
a = mainloop.trainlog.monitor['recon_term'][i]
b = mainloop.trainlog.monitor['kl_term'][i]
#c = mainloop.trainlog.monitor['mse'][i]
d = mainloop.trainlog.monitor['nll_upper_bound']
#print(a,b)
fLog.write("{},{},{}\n".format(a, b, d))
fLog.close()
theta_2_in = theta_2.fprop([theta_1_in]) theta_3_in = theta_3.fprop([theta_2_in]) theta_4_in = theta_4.fprop([theta_3_in]) theta_mu_in = theta_mu.fprop([theta_4_in]) theta_sig_in = theta_sig.fprop([theta_4_in]) coeff_in = coeff.fprop([theta_4_in]) z_shape = phi_mu_t.shape phi_mu_in = phi_mu_t.reshape((z_shape[0]*z_shape[1], -1)) phi_sig_in = phi_sig_t.reshape((z_shape[0]*z_shape[1], -1)) prior_mu_in = prior_mu_t.reshape((z_shape[0]*z_shape[1], -1)) prior_sig_in = prior_sig_t.reshape((z_shape[0]*z_shape[1], -1)) kl_in = kl.fprop([phi_mu_in, phi_sig_in, prior_mu_in, prior_sig_in]) kl_t = kl_in.reshape((z_shape[0], z_shape[1])) recon = GMM(x_in, theta_mu_in, theta_sig_in, coeff_in) recon = recon.reshape((x_shape[0], x_shape[1])) recon_term = recon.mean() kl_term = kl_t.mean() nll_lower_bound = recon_term + kl_term nll_lower_bound.name = 'nll_lower_bound' recon_term.name = 'recon_term' kl_term.name = 'kl_term' kl_ratio = kl_term / T.abs_(recon_term) kl_ratio.name = 'kl_term proportion' max_x = x.max() mean_x = x.mean() min_x = x.min() max_x.name = 'max_x'
