def __init__(self, name): # Call parent's constructor Net.__init__(self, name) # Attributes self._inter_type = self.RECURRENT self._train_state_buffer = None self._eval_state_buffer = None self._state_size = None self._init_state = None self._weights = None self._bias = None self._weight_initializer = None self._bias_initializer = None # For real-time training TODO: BETA self.repeater_tensors = None # registered in sub-classes self._grad_tensors = None self._new_state_tensor = None # self._gradient_buffer_placeholder = None self._gradient_buffer_array = None self._custom_vars = None # Gate activations should be registered here self._gate_dict = OrderedDict()
def __init__(self, name): # Call parent's constructor Net.__init__(self, name) # Attributes self._inter_type = self.RECURRENT self._state_array = None self._state_size = None self._init_state = None self._kernel = None self._bias = None self._weight_initializer = None self._bias_initializer = 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_T(self): # Add empty nets to each degree for n in self.orders: self.T[n] = Net('T{}'.format(n)) self.T[n].add(self._input) # Initialize volterra part for order in range(1, self._max_volterra_order + 1): self.T[order].add(Homogeneous(order))
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