inception_resnet_v1.py 文件源码

def block35(x, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None):
    """Builds the 35x35 resx block."""
    with tf.variable_scope(scope, 'Block35', [x], reuse=reuse):
        with tf.variable_scope('Branch_0'):
            tower_conv = slim.conv2d(x, 32, 1, scope='Conv2d_1x1')
        with tf.variable_scope('Branch_1'):
            tower_conv1_0 = slim.conv2d(x, 32, 1, scope='Conv2d_0a_1x1')
            tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3')
        with tf.variable_scope('Branch_2'):
            tower_conv2_0 = slim.conv2d(x, 32, 1, scope='Conv2d_0a_1x1')
            tower_conv2_1 = slim.conv2d(tower_conv2_0, 32, 3, scope='Conv2d_0b_3x3')
            tower_conv2_2 = slim.conv2d(tower_conv2_1, 32, 3, scope='Conv2d_0c_3x3')
        # tensor dimension: NxWxHxC, concat at dim-c
        mixed = tf.concat([tower_conv, tower_conv1_1, tower_conv2_2], 3)
        # output_num of up should be equal to input_num of layer
        up = slim.conv2d(mixed, x.get_shape()[3], 1, normalizer_fn=None, activation_fn=None, scope='Conv2d_1x1')
        x += scale * up
        if activation_fn:
            x = activation_fn(x)
    return x


# Inception-ResNet-B
# (2 branches)