def load(self, filename='model.npz'):
    'Prepare a neural network from a compressed binary containing weights\n        and biases arrays. Size of layers are derived from dimensions of\n        numpy arrays.\n\n        Parameters\n        ----------\n        filename : str, optional\n            Name of the ``.npz`` compressed binary in models directory.\n\n        '
    npz_members = np.load(os.path.join(os.curdir, 'models', filename))
    self.weights = list(npz_members['weights'])
    self.biases = list(npz_members['biases'])
    self.sizes = [b.shape[0] for b in self.biases]
    self.num_layers = len(self.sizes)
    self._zs = [np.zeros(bias.shape) for bias in self.biases]
    self._activations = [np.zeros(bias.shape) for bias in self.biases]
    self.mini_batch_size = int(npz_members['mini_batch_size'])
    self.epochs = int(npz_members['epochs'])
    self.eta = float(npz_members['eta'])
Prepare a neural network from a compressed binary containing weights
and biases arrays. Size of layers are derived from dimensions of
numpy arrays.

Parameters
----------
filename : str, optional
    Name of the ``.npz`` compressed binary in models directory.
def load(self, filename='model.npz'):
    'Prepare a neural network from a compressed binary containing weights\n        and biases arrays. Size of layers are derived from dimensions of\n        numpy arrays.\n\n        Parameters\n        ----------\n        filename : str, optional\n            Name of the ``.npz`` compressed binary in models directory.\n\n        '
    npz_members = np.load(os.path.join(os.curdir, 'models', filename))
    self.weights = list(npz_members['weights'])
    self.biases = list(npz_members['biases'])
    self.sizes = [b.shape[0] for b in self.biases]
    self.num_layers = len(self.sizes)
    self._zs = [np.zeros(bias.shape) for bias in self.biases]
    self._activations = [np.zeros(bias.shape) for bias in self.biases]
    self.mini_batch_size = int(npz_members['mini_batch_size'])
    self.epochs = int(npz_members['epochs'])
    self.eta = float(npz_members['eta'])