googlenet_model.py 文件源码

def aux_logit_layer( inputs, num_classes, is_training ):
    with tf.variable_scope("pool2d"):
        pooled = layers.avg_pool2d(inputs, [ 5, 5 ], stride = 3 )
    with tf.variable_scope("conv11"):
        conv11 = layers.conv2d( pooled, 128, [1, 1] )
    with tf.variable_scope("flatten"):
        flat = tf.reshape( conv11, [-1, 2048] )
    with tf.variable_scope("fc"):
        fc = layers.fully_connected( flat, 1024, activation_fn=None )
    with tf.variable_scope("drop"):
        drop = layers.dropout( fc, 0.3, is_training = is_training )
    with tf.variable_scope( "linear" ):
        linear = layers.fully_connected( drop, num_classes, activation_fn=None )
    with tf.variable_scope("soft"):
        soft = tf.nn.softmax( linear )
    return soft