如何获得某一层训练CNN模型的输出[张量流]

我有一个 CNN 模型 image classification 我已经在我的数据集上训练过了。模型是这样的

Convolution 
Relu
pooling

Convolution 
Relu
Convolution 
Relu
pooling

flat

fully connected (FC1)
Relu
fully connected (FC2)
softmax

训练后,我想得到我输入到预训练模型的图像的特征向量,也就是说,我想得到 FC1 层。我浏览了一下网页,但找不到任何有用的建议,有没有什么好的帮助。

训练脚本

# input
x = tf.placeholder(tf.float32, shape=[None, img_size_h, img_size_w, num_channels], name='x')
# lables
y_true     = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')
y_true_cls = tf.argmax(y_true, axis=1)

y_pred = build_model(x)     # Builds model architecture
y_pred_cls = tf.argmax(y_pred, axis=1)

cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=y_pred, labels=y_true)
cost = tf.reduce_mean(cross_entropy)

optimizer = tf.train.MomentumOptimizer(learn_rate, 0.9, use_locking=False, use_nesterov=True).minimize(cost)

accuracy  = tf.reduce_mean(tf.cast(tf.equal(y_pred_cls, y_true_cls), tf.float32))

sess = tf.Session()

sess.run(tf.global_variables_initializer())

tf_saver = tf.train.Saver()

train(num_iteration)    # Trains the network and saves the model

sess.close()

测试脚本

sess = tf.Session()

tf_saver = tf.train.import_meta_graph('model/model.meta')
tf_saver.restore(sess, tf.train.latest_checkpoint('model'))

x = tf.get_default_graph().get_tensor_by_name('x:0')

y_true = tf.get_default_graph().get_tensor_by_name('y_true:0')
y_true_cls = tf.argmax(y_true, axis=1)

y_pred = tf.get_default_graph().get_tensor_by_name('y_pred:0')      # refers to FC2 in the model
y_pred_cls = tf.argmax(y_pred, axis=1)

correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy           = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

images, labels = read_data()     # read data for testing

feed_dict_test  = {x: images, y_true: labels}

test_acc = sess.run(accuracy, feed_dict=feed_dict_test)

sess.close()