本文整理汇总了Python中cle.cle.cost.Gaussian.mean方法的典型用法代码示例。如果您正苦于以下问题：Python Gaussian.mean方法的具体用法？Python Gaussian.mean怎么用？Python Gaussian.mean使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类cle.cle.cost.Gaussian的用法示例。
在下文中一共展示了Gaussian.mean方法的6个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: cost
# 需要导入模块: from cle.cle.cost import Gaussian [as 别名]
# 或者: from cle.cle.cost.Gaussian import mean [as 别名]
def cost(self, X):
if len(X) != 3:
raise ValueError("The number of inputs does not match.")
cost = Gaussian(X[0], X[1], X[2])
if self.use_sum:
return cost.sum()
else:
return cost.mean()示例2: Gaussian
# 需要导入模块: from cle.cle.cost import Gaussian [as 别名]
# 或者: from cle.cle.cost.Gaussian import mean [as 别名]
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])
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 = Gaussian(x_in, theta_mu_in, theta_sig_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)
((mn_s_t, mn_phi_mu_t, mn_phi_sig_t, mn_prior_mu_t, mn_prior_sig_t, mn_z_4_t), mn_updates) =\
theano.scan(fn=inner_fn,示例3: main
# 需要导入模块: from cle.cle.cost import Gaussian [as 别名]
# 或者: from cle.cle.cost.Gaussian import mean [as 别名]
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',
#.........这里部分代码省略.........
示例4: Gaussian
# 需要导入模块: from cle.cle.cost import Gaussian [as 别名]
# 或者: from cle.cle.cost.Gaussian import mean [as 别名]
decoder_tm1 = dec_t[-1]
dec_shape = dec_t.shape
dec_in = dec_t.reshape((dec_shape[0]*dec_shape[1], -1))
theta_mu_in = theta_mu.fprop([dec_in])
theta_sig_in = theta_sig.fprop([dec_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))
kl_in = kl.fprop([phi_mu_in, phi_sig_in])
kl_t = kl_in.reshape((z_shape[0], z_shape[1]))
recon = Gaussian(x_in, theta_mu_in, theta_sig_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'
mean_x.name = 'mean_x'
min_x.name = 'min_x'示例5: main
# 需要导入模块: from cle.cle.cost import Gaussian [as 别名]
# 或者: from cle.cle.cost.Gaussian import mean [as 别名]
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']
data_path = os.path.expanduser(args['data_path'])
save_path = os.path.expanduser(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_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',
#.........这里部分代码省略.........
示例6: main
# 需要导入模块: from cle.cle.cost import Gaussian [as 别名]
# 或者: from cle.cle.cost.Gaussian import mean [as 别名]
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"]
data_path = os.path.expanduser(args["data_path"])
save_path = os.path.expanduser(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_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,
#.........这里部分代码省略.........