original_docstring: Returns initial random weights for symmetry breaking.
          Values are kept within a range, between -epsilon and epsilon.
          L_num denotes a number of units in layer L,
          while L1_num denotes a number of units in layer L + 1
          
docstring: Returns initial random weights for symmetry breaking.
  Values are kept within a range, between -epsilon and epsilon.
  L_num denotes a number of units in layer L,
  while L1_num denotes a number of units in layer L + 1
docstring_tokens: Returns initial random weights for symmetry breaking . Values are kept within a range between - epsilon and epsilon . L_num denotes a number of units in layer L while L1_num denotes a number of units in layer L + 1
code: def init_weights(L_num, L1_num, epsilon=None):
          """Returns initial random weights for symmetry breaking.
          Values are kept within a range, between -epsilon and epsilon.
          L_num denotes a number of units in layer L,
          while L1_num denotes a number of units in layer L + 1
          """
  
          # The way to choose right epsilon is as follows:
          # (6**0.5) / ((L_in + L_out) ** 0.5)
          if epsilon is None:
              epsilon = (6 ** 0.5) / ((L_num + L1_num) ** 0.5)
  
          theta = np.random.rand(L1_num, L_num) * 2 * epsilon - epsilon
  
          return theta
code_tokens: def init_weights ( L_num , L1_num , epsilon = None ) : if epsilon is None : epsilon = ( 6 ** 0.5 ) / ( ( L_num + L1_num ) ** 0.5 ) theta = np . random . rand ( L1_num , L_num ) * 2 * epsilon - epsilon return theta
short_docstring: Returns initial random weights for symmetry breaking.
short_docstring_tokens: Returns initial random weights for symmetry breaking .
comment: """Returns initial random weights for symmetry breaking.
          Values are kept within a range, between -epsilon and epsilon.
          L_num denotes a number of units in layer L,
          while L1_num denotes a number of units in layer L + 1
          """$SEP$# The way to choose right epsilon is as follows:$SEP$# (6**0.5) / ((L_in + L_out) ** 0.5)