Tflearn：如何构建ResNet 152层模型并提取倒数第​​二个隐藏层的图像特征？

Question

如何构建152层resnet并提取倒数第​​二个隐藏层的图像特征。

现在我有一个预先训练好的ResNet 152层模型http://download.tensorflow.org/models/resnet_v1_152_2016_08_28.tar.gz 我只是想用这个152层模型来提取图像特征，现在我想提取倒数第​​二个隐藏层的图像特征 （正如代码中所示）。

1. 主要问题是如何构建152层ResNet模型？ （我只看到那套n = 18然后是resnet 是110层。）。或者如何构建50层ResNet模型？

2. 我的提取倒数第​​二个隐藏层的图像特征码是对的吗？

   from __future__ import division, print_function, absolute_import
   
   import tflearn
   from PIL import Image
   import numpy as np
   
   # Residual blocks
   # 32 layers: n=5, 56 layers: n=9, 110 layers: n=18
   n = 18
   
   # Data loading
   from tflearn.datasets import cifar10
   (X, Y), (testX, testY) = cifar10.load_data()
   Y = tflearn.data_utils.to_categorical(Y, 10)
   testY = tflearn.data_utils.to_categorical(testY, 10)
   
   # Real-time data preprocessing
   img_prep = tflearn.ImagePreprocessing()
   img_prep.add_featurewise_zero_center(per_channel=True)
   
   # Real-time data augmentation
   img_aug = tflearn.ImageAugmentation()
   img_aug.add_random_flip_leftright()
   img_aug.add_random_crop([32, 32], padding=4)
   
   # Building Residual Network
   net = tflearn.input_data(shape=[None, 32, 32, 3],
                        data_preprocessing=img_prep,
                        data_augmentation=img_aug)
   net = tflearn.conv_2d(net, 16, 3, regularizer='L2', weight_decay=0.0001)
   net = tflearn.residual_block(net, n, 16)
   net = tflearn.residual_block(net, 1, 32, downsample=True)
   net = tflearn.residual_block(net, n-1, 32)
   net = tflearn.residual_block(net, 1, 64, downsample=True)
   net = tflearn.residual_block(net, n-1, 64)
   net = tflearn.residual_block(net, 1, 64, downsample=True)
   net = tflearn.residual_block(net, n-1, 64)
   
   net = tflearn.batch_normalization(net)
   net = tflearn.activation(net, 'relu')
   output_layer = tflearn.global_avg_pool(net)
   
   # Regression
   net = tflearn.fully_connected(output_layer, 10, activation='softmax')
   mom = tflearn.Momentum(0.1, lr_decay=0.1, decay_step=32000, staircase=True)
   net = tflearn.regression(net, optimizer=mom,
                        loss='categorical_crossentropy')
   # Training
   model = tflearn.DNN(net, checkpoint_path='resnet_v1_152.ckpt',
                   max_checkpoints=10, tensorboard_verbose=0,
                   clip_gradients=0.)
   
   model.fit(X, Y, n_epoch=200, validation_set=(testX, testY),
         snapshot_epoch=False, snapshot_step=500,
         show_metric=True, batch_size=128, shuffle=True,
         run_id='resnet_cifar10')
   model.save('./resnet_v1_152.ckpt')
   
   
   #---------------
   # now extract the penultimate hidden layer's image feature
   img = Image.open(file_path)
   img = img.resize((32, 32), Image.ANTIALIAS)
   img = np.asarray(img, dtype="float32")
   imgs = np.asarray([img])
   model_test = tflearn.DNN(output_layer, session = model.session)
   model_test.load('resnet_v1_152.ckpt', weights_only = True)
   
   predict_y = model_test.predict(imgs)
   
   print('layer\'s feature'.format(predict_y))