def _check_dims(y_pred, y_true):
if y_true.shape[-1] == 1 and y_pred.shape[-1] != 1:
n_c = y_pred.shape[-1]
y_true = true_one_hot(y_true, n_c)
if y_true.shape != y_pred.shape:
raise UnsupportedShapeError(y_true.shape, y_pred.shape)
return y_true
def __call__(self, shape):
if isinstance(shape, int):
return self.gain * np.eye(shape)
elif len(shape) == 2:
return self.gain * np.eye(*shape)
else:
raise UnsupportedShapeError(shape, 'N or (N, M)')
def add_data(self, x): """Add features to a dataset.""" x = np.array(x) if self.target is not None: if x.shape[0] != self.target.shape[0]: raise UnsupportedShapeError(x.shape[0], self.target.shape[0]) self.data = x return self
def add_targets(self, y): """add labels to a dataset.""" y = np.array(y) if self.data is not None: if self.data.shape[0] != y.shape[0]: raise UnsupportedShapeError(y.shape[0], self.data.shape[0]) self.target = y return self
def __init__(self, x=None, y=None): self.data = None self.target = None if x is not None: self.data = np.array(x) if y is not None: self.target = np.array(y) if x is not None and y is not None: if self.data.shape[0] != self.target.shape[0]: raise UnsupportedShapeError(x.shape[0], y.shape[0]) self.is_batched = False
def _check_dims(weights, dw):
if weights.shape != dw.shape:
raise UnsupportedShapeError(dw.shape, weights.shape)
def add_input_shape_to_layers(self, n_in):
if n_in != self.n_in:
raise UnsupportedShapeError(n_in, self.n_in)
if self.layers:
return self.layers[-1].n_out