conv1 = tf.layers.conv3d(inputs= inputs_, filters=16, kernel_size=(3,3,3), padding= padding, strides = stride, activation=tf.nn.relu)
maxpool1 = tf.layers.max_pooling3d(conv1, pool_size=(2,2,2), strides=(2,2,2), padding= padding)
conv2 = tf.layers.conv3d(inputs=maxpool1, filters=32, kernel_size=(3,3,3), padding= padding, strides = stride, activation=tf.nn.relu)
maxpool2 = tf.layers.max_pooling3d(conv2, pool_size=(3,3,3), strides=(3,3,3), padding= padding)
conv3 = tf.layers.conv3d(inputs=maxpool2, filters=96, kernel_size=(2,2,2), padding= padding , strides = stride, activation=tf.nn.relu)
maxpool3 = tf.layers.max_pooling3d(conv3, pool_size=(2,2,2), strides=(2,2,2), padding= padding)
#latent internal representation
#decoder
unpool1 = K.resize_volumes(maxpool3,2,2,2,"channels_last")
deconv1 = tf.layers.conv3d_transpose(inputs=unpool1, filters=96, kernel_size=(2,2,2), padding= padding , strides = stride, activation=tf.nn.relu)
unpool2 = K.resize_volumes(deconv1,3,3,3,"channels_last")
deconv2 = tf.layers.conv3d_transpose(inputs=unpool2, filters=32, kernel_size=(3,3,3), padding= padding , strides = stride, activation=tf.nn.relu)
unpool3 = K.resize_volumes(deconv2,2,2,2,"channels_last")
deconv3 = tf.layers.conv3d_transpose(inputs=unpool3, filters=16, kernel_size=(3,3,3), padding= padding , strides = stride, activation=tf.nn.relu)
如何保留编码器参数以提取特征？我知道tf.train.saver，它保存了所有参数，但不适合在这里使用。