parameters: (inp,
                      inp_size,
                      layer_size,
                      name,
                      activation=nn_ops.relu,
                      dtype=dtypes.float32)
return_statement: return output
docstring: Helper method to create a fully connected hidden layer.
docstring_summary: Helper method to create a fully connected hidden layer.
docstring_tokens: Helper method to create a fully connected hidden layer .
function: def fully_connected(inp,
                      inp_size,
                      layer_size,
                      name,
                      activation=nn_ops.relu,
                      dtype=dtypes.float32):
    """Helper method to create a fully connected hidden layer."""
    wt = variable_scope.get_variable(
        name="{}_weight".format(name), shape=[inp_size, layer_size], dtype=dtype)
    bias = variable_scope.get_variable(
        name="{}_bias".format(name),
        shape=[layer_size],
        initializer=init_ops.zeros_initializer())
    output = nn_ops.xw_plus_b(inp, wt, bias)
    if activation is not None:
      assert callable(activation)
      output = activation(output)
    return output
function_tokens: def fully_connected ( inp , inp_size , layer_size , name , activation = nn_ops . relu , dtype = dtypes . float32 ) : wt = variable_scope . get_variable ( name = "{}_weight" . format ( name ) , shape = [ inp_size , layer_size ] , dtype = dtype ) bias = variable_scope . get_variable ( name = "{}_bias" . format ( name ) , shape = [ layer_size ] , initializer = init_ops . zeros_initializer ( ) ) output = nn_ops . xw_plus_b ( inp , wt , bias ) if activation is not None : assert callable ( activation ) output = activation ( output ) return output