evaluation_y.py 文件源码

def get_eval_ops(logits, labels, one_hot=False, scope='', calc_accuracy=True):
    """Evaluate the quality of the logits at predicting the label.
      Args:
        logits: Logits tensor, float - [batch_size, NUM_CLASSES].
        labels: Labels tensor, int32 - [batch_size], with values in the
          range [0, NUM_CLASSES).
      Returns:
        A scalar int32 tensor with the number of examples (out of batch_size)
        that were predicted correctly.
      """
    print('Evaluation Ops..')
    with tf.name_scope(scope):
        # For a classifier model, we can use the in_top_k Op.
        # It returns a bool tensor with shape [batch_size] that is true for
        # the examples where the label's is was in the top k (here k=1)
        # of all logits for that example.
        # labels = tf.cast(labels, tf.int64)
        if one_hot:
            labels = tf.argmax(labels, 1)
        top_1_op = tf.nn.in_top_k(logits, labels, 1)
        num_correct = tf.reduce_sum(tf.cast(top_1_op, tf.float32))

        if calc_accuracy:
            acc_percent = tf.divide(num_correct, labels.shape[0].value)
        else:
            acc_percent = tf.constant(0.0)

        # =============
        y_const = tf.constant(-1, dtype=labels.dtype)
        y_greater = tf.greater(labels, y_const)
        n_all = tf.reduce_sum(tf.cast(y_greater, tf.float32))

        return top_1_op, acc_percent * 100.0, num_correct, n_all, labels


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