خطا دراجرای یک مدل آموزش دیده در تنسورفلو

Question

سلام و عرض ادب
من میخوام یه تصویری که میخونم رو به یک مدل مثلا iالکس نت بدم
مراحلی تاکنون انجام دادم به شرح زیر است

# خواندن تصویر
from scipy import misc
import tensorflow as tf

img = misc.imread('img652.jpg')
print (img.shape)    # (32, 32, 3)

img_tf = tf.Variable(img)
#print img_tf.get_shape().as_list()  # [32, 32, 3]
#Then you can run your graph:

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
im = sess.run(img_tf)
import matplotlib.pyplot as plt
fig = plt.figure()
plt.imshow(im)
plt.imshow(img)
plt.show()

تا اینجا مشکلی نیست

# تبدیل تصویر به تانسور
(x = tf.placeholder(tf.float32, [None, 1024

مدلل که ازش استفاده میکنم الکس نت هست

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf

slim = tf.contrib.slim
trunc_normal = lambda stddev: 
tf.truncated_normal_initializer(0.0, stddev)


def alexnet_v2_arg_scope(weight_decay=0.0005):
 with slim.arg_scope([slim.conv2d, slim.fully_connected],
 activation_fn=tf.nn.relu,
 biases_initializer=tf.constant_initializer(0.1),
 weights_regularizer=slim.l2_regularizer(weight_decay)):
    with slim.arg_scope([slim.conv2d], padding='SAME'):
      with slim.arg_scope([slim.max_pool2d], padding='VALID') as arg_sc:
        return arg_sc


def alexnet_v2(inputs,
               num_classes=1000,
               is_training=True,
               dropout_keep_prob=0.5,
               spatial_squeeze=True,
               scope='alexnet_v2'):
  """AlexNet version 2.
  Described in: http://arxiv.org/pdf/1404.5997v2.pdf
  Parameters from:
  github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
  layers-imagenet-1gpu.cfg
  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 224x224. To use in fully
        convolutional mode, set spatial_squeeze to false.
        The LRN layers have been removed and change the initializers from
        random_normal_initializer to xavier_initializer.
  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    num_classes: number of predicted classes.
    is_training: whether or not the model is being trained.
    dropout_keep_prob: the probability that activations are kept in the dropout
      layers during training.
    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
      outputs. Useful to remove unnecessary dimensions for classification.
    scope: Optional scope for the variables.
  Returns:
    the last op containing the log predictions and end_points dict.
  """
  with tf.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
    end_points_collection = sc.name + '_end_points'
    # Collect outputs for conv2d, fully_connected and max_pool2d.
    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                        outputs_collections=[end_points_collection]):
      net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
                        scope='conv1')
      net = slim.max_pool2d(net, [3, 3], 2, scope='pool1')
      net = slim.conv2d(net, 192, [5, 5], scope='conv2')
      net = slim.max_pool2d(net, [3, 3], 2, scope='pool2')
      net = slim.conv2d(net, 384, [3, 3], scope='conv3')
      net = slim.conv2d(net, 384, [3, 3], scope='conv4')
      net = slim.conv2d(net, 256, [3, 3], scope='conv5')
      net = slim.max_pool2d(net, [3, 3], 2, scope='pool5')

      # Use conv2d instead of fully_connected layers.
      with slim.arg_scope([slim.conv2d],
                          weights_initializer=trunc_normal(0.005),
                          biases_initializer=tf.constant_initializer(0.1)):
        net = slim.conv2d(net, 4096, [5, 5], padding='VALID',
                          scope='fc6')
        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                           scope='dropout6')
        net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                           scope='dropout7')
        net = slim.conv2d(net, num_classes, [1, 1],
                          activation_fn=None,
                          normalizer_fn=None,
                          biases_initializer=tf.zeros_initializer(),
                          scope='fc8')

      # Convert end_points_collection into a end_point dict.
      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
      if spatial_squeeze:
        net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
        end_points[sc.name + '/fc8'] = net
      return net, end_points
alexnet_v2.default_image_size = 224

##

الان تصویر رو چطور به مدل بدم؟
سوالم اینه برای دیدن اینکه ویژکی های استخراج شده رو ببینم خروجی کدوم لایه و با چه دستوری میشه این کار رو انجام داد؟
ممنون