paleo/graph.py
def load(self, filename):
        """Load the neural net architecture from JSON format.

            Convert the neural net into a list.
            Arrange the layers into multiple groups,
            such that the next group depend on the previous group.
              [(a), (b, c), (d)]
              [(a), ([b, c], [d, e]), (f)]

            Each node is LayerSpec object.
        """
        with open(filename, 'r') as f:
            net = json.load(f)
        self._create_graph(net)
Load the neural net architecture from JSON format.

            Convert the neural net into a list.
            Arrange the layers into multiple groups,
            such that the next group depend on the previous group.
              [(a), (b, c), (d)]
              [(a), ([b, c], [d, e]), (f)]

            Each node is LayerSpec object.
        
Load the neural net architecture from JSON format.
Convert the neural net into a list.
Arrange the layers into multiple groups,
such that the next group depend on the previous group.

Each node is LayerSpec object.
Load the neural net architecture from JSON format . Convert the neural net into a list . Arrange the layers into multiple groups such that the next group depend on the previous group . Each node is LayerSpec object .
def load(self, filename):
        with open(filename, 'r') as f:
            net = json.load(f)
        self._create_graph(net)
def load ( self , filename ) : with open ( filename , 'r' ) as f : net = json . load ( f ) self . _create_graph ( net )
Load the neural net architecture from JSON format.
Load the neural net architecture from JSON format .
"""Load the neural net architecture from JSON format.

            Convert the neural net into a list.
            Arrange the layers into multiple groups,
            such that the next group depend on the previous group.
              [(a), (b, c), (d)]
              [(a), ([b, c], [d, e]), (f)]

            Each node is LayerSpec object.
        """
self
filename