def __init__(self, degree, depth, mark=None, max_volterra_order=3, **kwargs): # Check parameters if degree < 1: raise ValueError('!! Degree must be a positive integer') if depth < 0: raise ValueError('!! Depth must be a positive integer') # Call parent's constructor Model.__init__(self, mark) # Initialize fields self.degree = degree self.depth = depth self._max_volterra_order = min(max_volterra_order, degree) self.T = {} self._input = Input([depth], name='input') self._output = None self._target = None self._alpha = 1.1 self._outputs = {} # Initialize operators in each degree orders = kwargs.get('orders', None) if orders is None: orders = list(range(1, self.degree + 1)) self.orders = orders self._init_T()
def __init__(self, mark=None): Model.__init__(self, mark) RNet.__init__(self, 'RecurrentNet') self.superior = self self._default_net = self # Attributes self._state = NestedTensorSlot(self, 'State') # mascot will be initiated as a placeholder with no shape specified # .. and will be put into initializer argument of tf.scan self._mascot = None
def __init__(self, mark=None, **kwargs): # Call parent's initializer Model.__init__(self, mark) Net.__init__(self, 'Bamboo_Broad_Net', inter_type=pedia.fork) assert self._inter_type == pedia.fork self.outputs = None # Private fields self._losses = [] self._metrics = [] self._train_ops = [] self._var_list = [] self._output_list = [] self._branch_index = 0 self._identity_initial = kwargs.get('ientity', False)
def __init__(self, z_dim=None, sample_shape=None, output_shape=None, mark=None, classes=0): # Call parent's constructor Model.__init__(self, mark) self._targets = None self._conditional = classes > 0 self._classes = classes if self._conditional: with self._graph.as_default(): self._targets = tf.placeholder(dtype=tf.float32, shape=[None, classes], name='one_hot_labels') # Define generator and discriminator self.Generator = Net(pedia.Generator) self.Discriminator = Net(pedia.Discriminator) # Alias self.G = self.Generator self.D = self.Discriminator # If z_dim/sample_shape is provided, define the input for # generator/discriminator accordingly if z_dim is not None: self.G.add(Input(sample_shape=[None, z_dim], name='z')) if sample_shape is not None: if (not isinstance(sample_shape, list) and not isinstance(sample_shape, tuple)): raise TypeError('sample shape must be a list or a tuple') self.D.add( Input(sample_shape=[None] + list(sample_shape), name='samples')) self._z_dim = z_dim self._sample_shape = sample_shape self._output_shape = output_shape self._sample_num = None # Private tensors and ops self._G, self._outputs = None, None self._Dr, self._Df = None, None self._logits_Dr, self._logits_Df = None, None self._loss_G, self._loss_D = None, None self._loss_Dr, self._loss_Df = None, None self._train_step_G, self._train_step_D = None, None self._merged_summary_G, self._merged_summary_D = None, None
def __init__(self, mark=None): Model.__init__(self, mark) RNet.__init__(self, 'RecurrentNet') self.superior = self self._default_net = self # Attributes self._state_slot = NestedTensorSlot(self, 'State') # mascot will be initiated as a placeholder with no shape specified # .. and will be put into initializer argument of tf.scan self._mascot = None self._while_loop_free_output = None # TODO: BETA self.last_scan_output = None self.grad_delta_slot = NestedTensorSlot(self, 'GradDelta') self._grad_buffer_slot = NestedTensorSlot(self, 'GradBuffer')
def __init__(self, z_dim=None, sample_shape=None, output_shape=None, mark=None, classes=0): # Call parent's constructor Model.__init__(self, mark) # Fields self._output_shape = output_shape # Define encoder and decoder self.Encoder = Net(pedia.Encoder) self.Decoder = Net(pedia.Decoder) self.Q = self.Encoder self.P = self.Decoder # If z_dim/sample_shape is provided, define the input for # decoder/encoder accordingly if z_dim is not None: self.P.add(Input(sample_shape=[None, z_dim], name='z')) if sample_shape is not None: if (not isinstance(sample_shape, list) and not isinstance(sample_shape, tuple)): raise TypeError('sample shape must be a list or a tuple') self.Q.add( Input(sample_shape=[None] + list(sample_shape), name='samples')) # Placeholders self._sample_num = None self._classes = classes self._conditional = classes > 0 if self._conditional: self._targets = tf.placeholder(dtype=tf.float32, shape=[None, classes], name='one_hot_labels') self._P, self._outputs = None, None # ... pass
def __init__(self, mark=None): Model.__init__(self, mark) Net.__init__(self, 'FeedforwardNet') self.superior = self self._default_net = self
def __init__(self, mark=None): Model.__init__(self, mark) Net.__init__(self, 'FeedforwardNet') self.outputs = None