def lookup(self, symbol):
if symbol == None:
return None
if type(symbol) == type([]):
return [self.lookup(k) for k in symbol]
if type(symbol) == type({}) or type(symbol) == hc.Config:
return hc.Config({k: self.lookup(symbol[k]) for k in symbol.keys()})
if type(symbol) != type(""):
return symbol
if symbol.startswith('function:'):
return self.lookup_function(symbol)
if symbol.startswith('class:'):
return self.lookup_class(symbol)
if symbol == 'tanh':
return tf.nn.tanh
if symbol == 'sigmoid':
return tf.nn.sigmoid
if symbol == 'batch_norm':
return layer_regularizers.batch_norm_1
if symbol == 'layer_norm':
return layer_regularizers.layer_norm_1
if symbol == "crelu":
return tf.nn.crelu
if symbol == "prelu":
return self.prelu()
if symbol == "selu":
return selu
if symbol == "lrelu":
return lrelu
if symbol == "relu":
return tf.nn.relu
if symbol == 'square':
return tf.square
if symbol == 'reduce_mean':
return tf.reduce_mean
if symbol == 'reduce_min':
return tf.reduce_min
if symbol == 'reduce_sum':
return tf.reduce_sum
if symbol == 'reduce_logsumexp':
return tf.reduce_logsumexp
if symbol == 'reduce_linear':
return self.reduce_linear()
if symbol == 'l1_distance':
return l1_distance
if symbol == 'l2_distance':
return l2_distance
return symbol
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