build_inception_v4.py 文件源码

def _build_inception_v4(
        inputs,
        stack_counts=[4, 7, 3],
        dropout_keep_prob=0.8,
        num_classes=1000,
        is_training=True,
        scope=''):
    """Inception v4 from http://arxiv.org/abs/

    Args:
      inputs: a tensor of size [batch_size, height, width, channels].
      dropout_keep_prob: dropout keep_prob.
      num_classes: number of predicted classes.
      is_training: whether is training or not.
      scope: Optional scope for op_scope.

    Returns:
      a list containing 'logits' Tensors and a dict of Endpoints.
    """
    # endpoints will collect relevant activations for external use, for example, summaries or losses.
    endpoints = {}
    name_scope_net = tf.name_scope(scope, 'Inception_v4', [inputs])
    arg_scope_train = arg_scope([layers.batch_norm, layers.dropout], is_training=is_training)
    arg_scope_conv = arg_scope([layers.conv2d, layers.max_pool2d, layers.avg_pool2d], stride=1, padding='SAME')
    with name_scope_net, arg_scope_train, arg_scope_conv:

        net = _block_stem(inputs, endpoints)
        # 35 x 35 x 384

        with tf.variable_scope('Scale1'):
            net = _stack(net, endpoints, fn=_block_a, count=stack_counts[0], scope='BlockA')
            # 35 x 35 x 384

        with tf.variable_scope('Scale2'):
            net = _block_a_reduce(net, endpoints)
            # 17 x 17 x 1024
            net = _stack(net, endpoints, fn=_block_b, count=stack_counts[1], scope='BlockB')
            # 17 x 17 x 1024

        with tf.variable_scope('Scale3'):
            net = _block_b_reduce(net, endpoints)
            # 8 x 8 x 1536
            net = _stack(net, endpoints, fn=_block_c, count=stack_counts[2], scope='BlockC')
            # 8 x 8 x 1536

        logits = _block_output(net, endpoints, num_classes, dropout_keep_prob, scope='Output')
        endpoints['Predictions'] = tf.nn.softmax(logits, name='Predictions')

        return logits, endpoints