def get_weights_topo(self):
if not isinstance(self.input_space, Conv2DSpace):
raise NotImplementedError()
W, = self.transformer.get_params()
W = W.T
W = W.reshape((self.detector_layer_dim, self.input_space.shape[0],
self.input_space.shape[1], self.input_space.nchannels))
W = Conv2DSpace.convert(W, self.input_space.axes, ('b', 0, 1, 'c'))
return function([], W)()
def get_weights_topo(self):
if not isinstance(self.input_space, Conv2DSpace):
raise NotImplementedError()
W ,= self.transformer.get_params()
W = W.T
W = W.reshape((self.dim, self.input_space.shape[0],
self.input_space.shape[1], self.input_space.nchannels))
W = Conv2DSpace.convert(W, self.input_space.axes, ('b', 0, 1, 'c'))
return function([], W)()
def get_weights_topo(self):
warnings.warn("BoltzmannIsingHidden.get_weights_topo returns the BOLTZMANN weights, is that what we want?")
if not isinstance(self.input_space, Conv2DSpace):
raise NotImplementedError()
W = self.W
W = W.T
W = W.reshape((self.detector_layer_dim, self.input_space.shape[0],
self.input_space.shape[1], self.input_space.nchannels))
W = Conv2DSpace.convert(W, self.input_space.axes, ('b', 0, 1, 'c'))
return function([], W)()
def expr(self, model, data, **kwargs):
space, sources = self.get_data_specs(model)
space.validate(data)
(X, Y) = data
input_space = model.get_input_space()
output_space = model.get_output_space()
X = Conv2DSpace.convert(X, input_space.axes, output_space.axes)
Y = Y.reshape((Y.shape[0], output_space.num_channels,
output_space.shape[0], output_space.shape[1]))
Y_hat = X[:, -1, (input_space.shape[0] - output_space.shape[0]) /
2:(input_space.shape[0] - output_space.shape[0]) / 2 +
output_space.shape[0],
(input_space.shape[1] - output_space.shape[1]) /
2:(input_space.shape[1] - output_space.shape[1]) / 2 +
output_space.shape[1], ]
Y_hat = Y_hat.dimshuffle(0, 'x', 1, 2)
return T.sqr(Y - Y_hat).sum(axis=(1, 2, 3)).mean()
def expr(self, model, data, ** kwargs):
space, sources = self.get_data_specs(model)
space.validate(data)
(X, Y) = data
input_space = model.get_input_space()
output_space = model.get_output_space()
X = Conv2DSpace.convert(X, input_space.axes, output_space.axes)
Y = Y.reshape((Y.shape[0],
output_space.num_channels,
output_space.shape[0],
output_space.shape[1]))
Y_hat = X[:, -1,
(input_space.shape[0]-output_space.shape[0])/2:
(input_space.shape[0]-output_space.shape[0])/2+output_space.shape[0],
(input_space.shape[1]-output_space.shape[1])/2:
(input_space.shape[1]-output_space.shape[1])/2+output_space.shape[1],
]
Y_hat = Y_hat.dimshuffle(0, 'x', 1, 2)
return T.sqr(Y - Y_hat).sum(axis=(1,2,3)).mean()
