inception_resnet_v2_modified.py 文件源码

def inference(inputs, keep_prob,
              bottleneck_size=128,
              phase_train=True,
              weight_decay=0.0,
              reuse=None):
    batch_norm_params = {
        'decay': 0.995,
        'epsilon': 0.001,
        'updates_collections': None,
        # 'scale': True}  # [test1: add 'gamma']
        'variables_collections': [tf.GraphKeys.TRAINABLE_VARIABLES]}  # [test2: removed from 'trainable_variables']
    with slim.arg_scope(
            [slim.conv2d, slim.fully_connected],
            weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
            weights_regularizer=slim.l2_regularizer(weight_decay),
            biases_regularizer=slim.l2_regularizer(weight_decay),
            normalizer_fn=slim.batch_norm,
            normalizer_params=batch_norm_params):  # [test4: add weight_decay to biases]):
        return inception_resnet_v2(
            inputs,
            is_training=phase_train,
            keep_prob=keep_prob,
            bottleneck_size=bottleneck_size,
            reuse=reuse)