def parse(self, name, mcp, prev_layers, model):
dic = WeightLayerParser.parse(self, name, mcp, prev_layers, model)
dic['usesActs'] = False
dic['channels'] = mcp.safe_get_int(name, 'channels')
dic['in_nodes'] = mcp.safe_get_int(name, 'in_nodes')
dic['out_nodes'] = mcp.safe_get_int(name, 'out_nodes')
pixels = dic['numInputs'][0] / (dic['in_nodes']*dic['channels'])
dic['outputs'] = dic['out_nodes'] * dic['channels'] * pixels
# assert dic['numInputs'] == [ dic['in_nodes'] * dic['channels'] ] # there is only one input. This input is of size in_nodes*channels
self.verify_num_range(dic['outputs'], 'outputs', 1, None)
self.make_weights(dic['initW'], [dic['in_nodes']], [dic['out_nodes']*dic['channels']], order='F')
self.make_biases(1, dic['out_nodes']*dic['channels'], order='F')
print "Initialized filter-fully-connected layer '%s', producing %d outputs in %d channels" % (name, dic['out_nodes'], dic['channels'])
return dic
def parse(self, name, mcp, prev_layers, model):
dic = WeightLayerParser.parse(self, name, mcp, prev_layers, model)
dic['usesActs'] = False
dic['channels'] = mcp.safe_get_int(name, 'channels')
dic['in_nodes'] = mcp.safe_get_int(name, 'in_nodes')
dic['out_nodes'] = mcp.safe_get_int(name, 'out_nodes')
pixels = dic['numInputs'][0] / (dic['in_nodes'] * dic['channels'])
dic['outputs'] = dic['out_nodes'] * dic['channels'] * pixels
# assert dic['numInputs'] == [ dic['in_nodes'] * dic['channels'] ] # there is only one input. This input is of size in_nodes*channels
self.verify_num_range(dic['outputs'], 'outputs', 1, None)
self.make_weights(dic['initW'], [dic['in_nodes']],
[dic['out_nodes'] * dic['channels']],
order='F')
self.make_biases(1, dic['out_nodes'] * dic['channels'], order='F')
print "Initialized filter-fully-connected layer '%s', producing %d outputs in %d channels" % (
name, dic['out_nodes'], dic['channels'])
return dic
def __init__(self):
WeightLayerParser.__init__(self)
def __init__(self):
WeightLayerParser.__init__(self)
