def main(args):
trial = int(args['trial'])
pkl_name = 'rnn_gauss_%d' % trial
channel_name = 'valid_nll'
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'])
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 = 800
s2x_dim = 800
target_dim = x_dim
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)
rnn = LSTM(name='rnn',
parent=['x_4'],
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_2 = FullyConnectedLayer(name='theta_2',
parent=['theta_1'],
parent_dim=[s2x_dim],
nout=s2x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_3 = FullyConnectedLayer(name='theta_3',
parent=['theta_2'],
parent_dim=[s2x_dim],
nout=s2x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_4 = FullyConnectedLayer(name='theta_4',
parent=['theta_3'],
parent_dim=[s2x_dim],
nout=s2x_dim,
unit='relu',
init_W=init_W,
init_b=init_b)
theta_mu = FullyConnectedLayer(name='theta_mu',
parent=['theta_4'],
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_4'],
parent_dim=[s2x_dim],
nout=target_dim,
unit='softplus',
cons=1e-4,
init_W=init_W,
init_b=init_b_sig)
nodes = [
rnn, x_1, x_2, x_3, x_4, theta_1, theta_2, theta_3, theta_4, theta_mu,
theta_sig
]
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_1_temp = x_1.fprop([x], params)
x_2_temp = x_2.fprop([x_1_temp], params)
x_3_temp = x_3.fprop([x_2_temp], params)
x_4_temp = x_4.fprop([x_3_temp], params)
def inner_fn(x_t, s_tm1):
s_t = rnn.fprop([[x_t], [s_tm1]], params)
return s_t
(s_temp, updates) = theano.scan(fn=inner_fn,
sequences=[x_4_temp],
outputs_info=[s_0])
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([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)
recon = Gaussian(x, theta_mu_temp, theta_sig_temp)
recon_term = recon.mean()
recon_term.name = 'nll'
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_updates) = theano.scan(fn=inner_fn,
sequences=[m_x_4_temp],
outputs_info=[m_s_0])
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_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_recon = Gaussian(m_x, m_theta_mu_temp, m_theta_sig_temp)
m_recon_term = m_recon.mean()
m_recon_term.name = 'nll'
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_temp.max()
mean_theta_mu = m_theta_mu_temp.mean()
min_theta_mu = m_theta_mu_temp.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_temp.max()
mean_theta_sig = m_theta_sig_temp.mean()
min_theta_sig = m_theta_sig_temp.min()
max_theta_sig.name = 'max_theta_sig'
mean_theta_sig.name = 'mean_theta_sig'
min_theta_sig.name = 'min_theta_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_recon_term, 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_recon_term, 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=recon_term,
outputs=[recon_term],
extension=extension)
mainloop.run()
def main(args):
trial = int(args['trial'])
pkl_name = 'vrnn_gauss_%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'])
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 = 600
x2s_dim = 600
z2s_dim = 500
target_dim = x_dim
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)
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
]
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
# Resets / Initializes the cell-state or the memory-state of each LSTM to
# zero.
s_0 = T.switch(T.eq(T.mod(step_count, reset_freq), 0),
rnn.get_init_state(batch_size), rnn_tm1)
# Forward Propagate the input to get more complex features for
# every time step.
x_1_temp = x_1.fprop([x], params)
x_2_temp = x_2.fprop([x_1_temp], params)
x_3_temp = x_3.fprop([x_2_temp], params)
x_4_temp = x_4.fprop([x_3_temp], params)
def inner_fn(x_t, s_tm1):
# Generate the mean and standard deviation of the
# latent variables Z_t | X_t for every time-step of the LSTM.
# This is a function of the input and the hidden state of the previous
# time step.
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 on the latent variables at every time-step
# Dependent only on the hidden-step.
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)
# Sample from the latent distibution with mean phi_mu_t
# and std phi_sig_t
z_t = Gaussian_sample(phi_mu_t, phi_sig_t)
# h_t = f(h_(t-1)), z_t, x_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
# Iterate over every time-step
((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_temp],
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)
# Generate the output distribution at every time-step.
# This is as a function of the latent variables and the hidden-state at
# every time-step.
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)
kl_temp = KLGaussianGaussian(phi_mu_temp, phi_sig_temp, prior_mu_temp,
prior_sig_temp)
recon = Gaussian(x, theta_mu_temp, theta_sig_temp)
recon_term = recon.mean()
kl_term = kl_temp.mean()
nll_upper_bound = recon_term + kl_term
nll_upper_bound.name = 'nll_upper_bound'
# Forward-propagation of the validation data.
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)
# Get the hidden-states, conditional mean, standard deviation, prior mean
# and prior standard deviation of the latent variables at every time-step.
((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
# Get the inferred mean (X_t | Z_t) at every time-step of the validation
# data.
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)
# Compute the data log-likelihood + KL-divergence on the validation data.
m_kl_temp = KLGaussianGaussian(m_phi_mu_temp, m_phi_sig_temp,
m_prior_mu_temp, m_prior_sig_temp)
m_recon = Gaussian(m_x, m_theta_mu_temp, m_theta_sig_temp)
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_temp.max()
mean_theta_mu = m_theta_mu_temp.mean()
min_theta_mu = m_theta_mu_temp.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_temp.max()
mean_theta_sig = m_theta_sig_temp.mean()
min_theta_sig = m_theta_sig_temp.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()