def __init__( self, fname, **params ):
Model.__init__( self, fname, **params )
self.k = self.get_parameter( "k" )
self.d = self.get_parameter( "d" )
self.n_views = self.get_parameter( "v" )
self.weights = self.get_parameter( "w" )
self.means = self.get_parameter( "M" )
self.sigmas = self.get_parameter( "S" )
def __init__(self, **params):
"""Create a mixture model for components using given weights"""
Model.__init__(self, **params)
self.k = self["k"]
self.d = self["d"]
self.weights = self["w"]
self.means = self["M"]
self.sigmas = self["S"]
def __init__( self, fname, **params ):
"""Create a mixture model for components using given weights"""
Model.__init__( self, fname, **params )
self.k = self.get_parameter( "k" )
self.d = self.get_parameter( "d" )
self.weights = self.get_parameter( "w" )
self.means = self.get_parameter( "M" )
self.sigmas = self.get_parameter( "S" )
def __init__(self):
print("unet init")
Model.__init__(self)
self.learning_rate = tf.train.exponential_decay(
0.0001, tf.Variable(0, trainable=False), 10, 0.8, staircase=True)
self.loss = Pixelwise_weighted_loss().compute_loss
self.optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
self.metric = IOU()
def __init__(self,
genres,
label_probs,
image_shape,
filter_counts,
unit_counts,
resize_shape=None):
self.label_probs = label_probs
self.image_shape = image_shape
self.filter_counts = filter_counts
self.unit_counts = unit_counts
Model.__init__(self, genres, resize_shape)
def __init__(self, **params):
"""Create a mixture model for components using given weights"""
Model.__init__(self, **params)
self.k = self["k"]
self.d = self["d"]
self.weights = self["w"]
self.means = self["M"] # Draw as a multinomial distribution
assert allclose(self.weights.sum(), 1.)
assert allclose(self.means.sum(0), 1.)
# symbolic means and observed variables
self.sym_means = sp.symbols('x1:'+str(self.d+1))
self.sym_obs = self.sym_means
def __init__(self, **params):
"""Create a mixture model for components using given weights"""
Model.__init__(self, **params)
self.k = self["k"]
self.d = self["d"]
self.weights = self["w"]
self.betas = self["B"] # Draw as a multinomial distribution
assert allclose(self.weights.sum(), 1.)
self.mean = self["xM"]
self.sigma = self["xS"]
self.sigma_val = self["xSigma"]
self.sym_betas = sp.symbols('b1:' + str(self.d + 1))
self.sym_obs = sp.symbols('x1:' + str(self.d + 1) + 'y')
def __init__(self, **params):
"""Create a mixture model for components using given weights"""
Model.__init__(self, **params)
self.k = self["k"]
self.d = self["d"]
self.weights = self["w"]
self.betas = self["B"] # Draw as a multinomial distribution
assert allclose(self.weights.sum(), 1.)
self.mean = self["xM"]
self.sigma = self["xS"]
self.sigma_val = self["xSigma"]
self.sym_betas = sp.symbols('b1:'+str(self.d+1))
self.sym_obs = sp.symbols('x1:'+str(self.d+1) + 'y')
def __init__(self,
name,
depth=5,
lr=0.001,
max_length=822,
kernel_size=5,
filters=100,
regularization_factor=0.001,
keep_prob=0.5,
batch_size=200,
hidden_size=150):
self.lr = lr
self.regularization_factor = regularization_factor
self.keep_prob = keep_prob
self.batch_size = batch_size
self.hidden_size = hidden_size
self.filters = filters
self.kernel_size = kernel_size
self.depth = depth
Model.__init__(self, name, max_length)
def __init__(self, genres, label_probs, image_shape, hidden_layer_sizes, resize_shape=None):
self.label_probs = label_probs
self.image_shape = image_shape
self.hidden_layer_sizes = hidden_layer_sizes
Model.__init__(self, genres, resize_shape)
def __init__(self, genres, label_probs, image_shape, resize_shape=None): self.label_probs = label_probs self.image_shape = image_shape Model.__init__(self, genres, resize_shape)
def __init__(self):
Model.__init__(self, 'book')
def __init__(self):
print("unet init")
Model.__init__(self)
def __init__(self):
print("unet init")
Model.__init__(self)
self.learning_rate = 0.000001
self.loss = Pixelwise_weighted_loss().compute_loss
self.optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
