(dim_input, dim_output)
return net_input, action, precision
produce the placeholder inputs that are used to run ops forward and backwards.
        net_input: usually an observation.
        action: mu, the ground truth actions we're trying to learn.
        precision: precision matrix used to commpute loss.
produce the placeholder inputs that are used to run ops forward and backwards.
        net_input: usually an observation.
        action: mu, the ground truth actions we're trying to learn.
        precision: precision matrix used to commpute loss.
produce the placeholder inputs that are used to run ops forward and backwards . net_input : usually an observation . action : mu the ground truth actions we re trying to learn . precision : precision matrix used to commpute loss .
def get_input_layer(dim_input, dim_output):
    """produce the placeholder inputs that are used to run ops forward and backwards.
        net_input: usually an observation.
        action: mu, the ground truth actions we're trying to learn.
        precision: precision matrix used to commpute loss."""
    net_input = tf.placeholder("float", [None, dim_input], name='nn_input')
    action = tf.placeholder('float', [None, dim_output], name='action')
    precision = tf.placeholder('float', [None, dim_output, dim_output], name='precision')
    return net_input, action, precision
def get_input_layer ( dim_input , dim_output ) : net_input = tf . placeholder ( "float" , [ None , dim_input ] , name = 'nn_input' ) action = tf . placeholder ( 'float' , [ None , dim_output ] , name = 'action' ) precision = tf . placeholder ( 'float' , [ None , dim_output , dim_output ] , name = 'precision' ) return net_input , action , precision