def test_get_default_graph(self): graph1 = graph.get_default_graph() graph2 = graph.get_default_graph() self.assertEqual(graph1.__class__, Graph) self.assertEqual(graph2.__class__, Graph) self.assertEqual(graph1, graph2)
def __init__(self, variableOp, value, name="UpdateVariableOp"): self.variableOp = variableOp self.value = value self.name = name self.graph = graph.get_default_graph() self.graph.add_to_graph(self)
def __init__(self, value, is_trainable=True, name="VariableOp"): super(VariableOp, self).__init__(name) self._value = value self._is_trainable = is_trainable self._graph = graph.get_default_graph() self._graph.add_to_graph(self) if self._is_trainable: self._graph.add_to_trainable_variable(self.get_name(), self)
def __init__(self, dtype=None, shape=None, name="PlaceholderOp"): super(PlaceholderOp, self).__init__(name) # TODO self._dtype = dtype self._shape = shape self._value = None self._graph = graph.get_default_graph() self._graph.add_to_graph(self)
def __init__(self, input, name="Cubic"): if not isinstance(input, Op): self.op = ConstantOp(input) else: self.op = input self.name = name self.graph = graph.get_default_graph() self.graph.add_to_graph(self)
def __init__(self, input, power, name="PowerOp"): super(PowerOp, self).__init__(name) if not isinstance(input, Op): self._op = ConstantOp(input) else: self._op = input self._power = power self._graph = graph.get_default_graph() self._graph.add_to_graph(self)
def __init__(self, *inputs): self.name = "MultipleN" self.ops = [] for input in inputs: if not isinstance(input, Op): input = ConstantOp(input) self.ops.append(input) self.graph = graph.get_default_graph() self.graph.add_to_graph(self)
def __init__(self, dtype=None, shape=None, name="Placeholder"): super(PlaceholderOp, self).__init__(name) # TODO: Use dtype and shape self._dtype = dtype self._shape = shape # The value is None util Session.run() with feed_dict parameter self._value = None # TODO: Support other graph instance self._graph = graph.get_default_graph() self._graph.add_to_graph(self)
def __init__(self, input_nodes=[], name=None): self.input_nodes = input_nodes for input in input_nodes: input.output_nodes.append(self) self.output_nodes = [] self.output_value = None self.name = name self.graph = graph.get_default_graph() self.graph.add_to_graph(self) self._isscalar = False
def __init__(self, input1, input2, name="MultipleOp"): super(MultipleOp, self).__init__(self) if not isinstance(input1, Op): self._op1 = ConstantOp(input1) else: self._op1 = input1 if not isinstance(input2, Op): self._op2 = ConstantOp(input2) else: self._op2 = input2 self._graph = graph.get_default_graph() self._graph.add_to_graph(self)
def __init__(self, *inputs): # TODO: Deprecated op # TODO: Support user defined name in the parameter self.name = "AddN" self.ops = [] for input in inputs: if not isinstance(input, Op): input = ConstantOp(input) self.ops.append(input) self.graph = graph.get_default_graph() self.graph.add_to_graph(self)
def get_variable(name="Variable", value=None, shape=None, dtype=None, initializer=None, regularizer=None, reuse=None, trainable=None): _graph = graph.get_default_graph() if name in _graph.get_name_op_map(): return _graph.get_name_op_map()[name] else: variable = VariableOp(value=value, name=name) return variable
def __init__(self, name="LocalVariablesInitializer"): super(LocalVariablesInitializerOp, self).__init__(name) self._graph = graph.get_default_graph() self._graph.add_to_graph(self)
def __init__(self, variableop_value_map, name="UpdateVariableNOp"): self.variableop_value_map = variableop_value_map self.name = name self.graph = graph.get_default_graph() self.graph.add_to_graph(self)
def __init__(self, value, name="Constant"): super(ConstantOp, self).__init__(name) self._value = value self._graph = graph.get_default_graph() self._graph.add_to_graph(self)
def __init__(self, learning_rate=0.01, name="GradientDescent"): super(GradientDescentOptimizer, self).__init__(name) self.learning_rate = learning_rate self.graph = graph.get_default_graph()