def Show(self):
if not self.proto.hyperparams.enable_display:
return
if self.node1.is_input:
if self.conv:
visualize.display_convw(self.params['weight'].asarray(),
self.proto.receptive_field_width,
self.proto.display_rows,
self.proto.display_cols, self.conv_filter_fig,
title=self.name)
visualize.display_hidden(self.params['weight'].asarray(),
self.fig,
title=self.name)
else:
if len(self.node1.proto.shape) < 3:
visualize.display_wsorted(self.params['weight'].asarray(),
self.proto.receptive_field_width,
self.proto.display_rows,
self.proto.display_cols, self.fig,
title=self.name)
else:
visualize.display_convw(self.params['weight'].asarray().T,
self.proto.receptive_field_width,
self.proto.display_rows,
self.proto.display_cols, self.fig,
title=self.name)
def Show(self, train=False):
"""Displays useful statistics about the model."""
if not self.proto.hyperparams.enable_display:
return
"""
if self.is_input and hasattr(self, 'neg_state'):
visualize.display_w(self.neg_state.asarray(), self.proto.shape[0],
10, self.batchsize/10, self.fig, title='neg particles')
elif self.is_input:
if len(self.proto.shape) == 3:
edge = self.outgoing_edge[0]
visualize.display_convw(self.state.asarray().T,
self.proto.shape[0],
16, self.batchsize/16, self.fig,
title=self.name)
else:
visualize.display_w(self.state.asarray(), self.proto.shape[0],
10, self.batchsize/10, self.fig, title='data')
else:
"""
f = 1
if self.hyperparams.dropout and not train:
f = 1 / (1 - self.hyperparams.dropout_prob)
if self.is_input:
visualize.display_hidden(self.data.asarray(), self.fig, title=self.name)
else:
visualize.display_hidden(f * self.state.asarray(), self.fig, title=self.name)
def Show(self, train=False):
"""Displays useful statistics about the model."""
if not self.proto.hyperparams.enable_display:
return
f = 1
if self.hyperparams.dropout and not train:
f = 1 / (1 - self.hyperparams.dropout_prob)
if self.is_input:
visualize.display_hidden(self.data.asarray(), self.fig, title=self.name)
else:
visualize.display_hidden(f*self.state.asarray(), self.fig, title=self.name)
def Show(self):
if not self.proto.hyperparams.enable_display:
return
"""
if self.is_input and hasattr(self, 'neg_state'):
visualize.display_w(self.neg_state.asarray(), self.proto.shape[0],
10, self.batchsize/10, self.fig, title='neg particles')
elif self.is_input:
if len(self.proto.shape) == 3:
edge = self.outgoing_edge[0]
visualize.display_convw(self.state.asarray().T,
self.proto.shape[0],
16, self.batchsize/16, self.fig,
title=self.name)
else:
visualize.display_w(self.state.asarray(), self.proto.shape[0],
10, self.batchsize/10, self.fig, title='data')
else:
"""
if self.is_input:
visualize.display_hidden(self.data.asarray(), self.fig, title=self.name)
else:
visualize.display_hidden(self.pos_state.asarray(), 2*self.fig, title=self.name + "_positive")
visualize.display_hidden(self.neg_state.asarray(), 2*self.fig_neg, title=self.name + "_negative")
#visualize.display_hidden(self.params['bias'].asarray(), 2*self.fig_neg, title=self.name + "_bias")
visualize.display_w(self.pos_state.asarray(), self.proto.shape[0],
self.batchsize, 1, 2*self.fig+1,
title=self.name + "_positive", vmin=0, vmax=1)
visualize.display_w(self.neg_sample.asarray(), self.proto.shape[0],
self.batchsize, 1, 2*self.fig_neg+1,
title=self.name + "_negative", vmin=0, vmax=1)
"""
def Show(self):
if not self.proto.hyperparams.enable_display:
return
"""
if self.is_input and hasattr(self, 'neg_state'):
visualize.display_w(self.neg_state.asarray(), self.proto.shape[0],
10, self.batchsize/10, self.fig, title='neg particles')
elif self.is_input:
if len(self.proto.shape) == 3:
edge = self.outgoing_edge[0]
visualize.display_convw(self.state.asarray().T,
self.proto.shape[0],
16, self.batchsize/16, self.fig,
title=self.name)
else:
visualize.display_w(self.state.asarray(), self.proto.shape[0],
10, self.batchsize/10, self.fig, title='data')
else:
"""
if self.is_input:
visualize.display_hidden(self.data.asarray(),
self.fig,
title=self.name)
else:
visualize.display_hidden(self.pos_state.asarray(),
2 * self.fig,
title=self.name + "_positive")
visualize.display_hidden(self.neg_state.asarray(),
2 * self.fig_neg,
title=self.name + "_negative")
#visualize.display_hidden(self.params['bias'].asarray(), 2*self.fig_neg, title=self.name + "_bias")
visualize.display_w(self.pos_state.asarray(),
self.proto.shape[0],
self.batchsize,
1,
2 * self.fig + 1,
title=self.name + "_positive",
vmin=0,
vmax=1)
visualize.display_w(self.neg_sample.asarray(),
self.proto.shape[0],
self.batchsize,
1,
2 * self.fig_neg + 1,
title=self.name + "_negative",
vmin=0,
vmax=1)
"""