def get_monitoring_channels(self, V):
theano_rng = RandomStreams(42)
norms = theano_norms(self.weights)
H = self.mean_h_given_v(V)
h = H.mean(axis=0)
return { 'bias_hid_min' : T.min(self.bias_hid),
'bias_hid_mean' : T.mean(self.bias_hid),
'bias_hid_max' : T.max(self.bias_hid),
'bias_vis_min' : T.min(self.bias_vis),
'bias_vis_mean' : T.mean(self.bias_vis),
'bias_vis_max': T.max(self.bias_vis),
'h_min' : T.min(h),
'h_mean': T.mean(h),
'h_max' : T.max(h),
'W_min' : T.min(self.weights),
'W_max' : T.max(self.weights),
'W_norms_min' : T.min(norms),
'W_norms_max' : T.max(norms),
'W_norms_mean' : T.mean(norms),
'reconstruction_error' : self.reconstruction_error(V, theano_rng) }
def get_monitoring_channels(self, V):
theano_rng = RandomStreams(42)
norms = theano_norms(self.weights)
H = self.mean_h_given_v(V)
h = H.mean(axis=0)
return {
'bias_hid_min': T.min(self.bias_hid),
'bias_hid_mean': T.mean(self.bias_hid),
'bias_hid_max': T.max(self.bias_hid),
'bias_vis_min': T.min(self.bias_vis),
'bias_vis_mean': T.mean(self.bias_vis),
'bias_vis_max': T.max(self.bias_vis),
'h_min': T.min(h),
'h_mean': T.mean(h),
'h_max': T.max(h),
'W_min': T.min(self.weights),
'W_max': T.max(self.weights),
'W_norms_min': T.min(norms),
'W_norms_max': T.max(norms),
'W_norms_mean': T.mean(norms),
'reconstruction_error': self.reconstruction_error(V, theano_rng)
}
def get_monitoring_channels(self, V):
vb, hb, weights = self.get_params()
norms = theano_norms(weights)
return {'W_min': tensor.min(weights),
'W_max': tensor.max(weights),
'W_norm_mean': tensor.mean(norms),
'bias_hid_min' : tensor.min(hb),
'bias_hid_mean' : tensor.mean(hb),
'bias_hid_max' : tensor.max(hb),
'bias_vis_min' : tensor.min(vb),
'bias_vis_mean' : tensor.mean(vb),
'bias_vis_max': tensor.max(vb),
}
def get_monitoring_channels(self, V):
vb, hb, weights = self.get_params()
norms = theano_norms(weights)
return {
'W_min': tensor.min(weights),
'W_max': tensor.max(weights),
'W_norm_mean': tensor.mean(norms),
'bias_hid_min': tensor.min(hb),
'bias_hid_mean': tensor.mean(hb),
'bias_hid_max': tensor.max(hb),
'bias_vis_min': tensor.min(vb),
'bias_vis_mean': tensor.mean(vb),
'bias_vis_max': tensor.max(vb),
}