为预先训练的模型添加新的输出

Question

我很困惑为预先训练的模型添加新的类，直到现在还原预先训练的检查点并创建大小为m * C + 1的矩阵和长度为C + 1的向量的矩阵，然后初始化第一个从现有权重中提取C行/元素，并通过仅在Optimizer.minimize（）中训练FC层来冻结上一层。 但是，当我运行代码时，出现此错误：

Traceback (most recent call last):
File "/home/tensorflow/tensorflow/models/image/mnist/new_dataset/Nets.py", line 482, in <module>
new_op_w = optimizer_new.minimize(loss, var_list = resize_var_w)
File "/home/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.py", line 279, in minimize
grad_loss=grad_loss)
File "/home/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/training/optimizer.py", line 337, in compute_gradients
processors = [_get_processor(v) for v in var_list]
File "/home/tensorflow/local/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 502, in __iter__
raise TypeError("'Tensor' object is not iterable.")
TypeError: 'Tensor' object is not iterable.

那就是代码：

with tf.Session(graph=graph) as sess:
  if os.path.isfile(ckpt):
       aver.restore(sess, 'path_to_checkpoint.ckpt')
       w_b_new = {

        'weight_4': tf.Variable(tf.random_normal([num_hidden, 1], stddev=0.1), name = 'weight_4'),
        'bias_4'  : tf.Variable(tf.constant(1.0, shape=[1]), name = 'bias_4'),}
       change_1 = tf.unstack(w_b_not['weight_4'])
       change_2 = tf.unstack(w_b_not['bias_4'])
       change_3 = tf.unstack(w_b_new['weight_4'])
       change_4 = tf.unstack(w_b_new['bias_4'])
       changestep1 = []
       for i in range(len(change_1)):
        changestep1.append(tf.unstack(change_1[i]))          
       changestep3 = []
       for i in range(len(change_3)):
        changestep3.append(tf.unstack(change_3[i]))
        for j in range(len(changestep3[i])):
          changestep1[i].append(changestep3[i][j])

        changestep1[i] = tf.stack(changestep1[i])
       final1 = tf.stack(changestep1)
       resize_var_w = tf.assign(w_b_not['weight_4'], final1, validate_shape=False)
       final2 = tf.concat([w_b_not['bias_4'] ,  w_b_new['bias_4']], axis=0)
       resize_var = tf.assign(w_b_not['bias_4'], final2, validate_shape=False)

       optimizer_new = tf.train.GradientDescentOptimizer(0.01)
       new_op_w = optimizer_new.minimize(loss, var_list = resize_var_w)
       new_op_b = optimizer_new.minimize(loss, var_list = resize_var)       
       for step in range(num_steps,num_steps + num_train_steps):
          offset = (step * batch_size) % (train_labels.shape[0] - batch_size)       
          batch_data = train_dataset[offset:(offset + batch_size), :, :, :]      
          batch_labels = train_labels[offset:(offset + batch_size), :]
          feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels , keep_prob:0.5}        
          _,_, l, predictions = sess.run([new_op_w,new_op_b, loss, train_prediction ], feed_dict=feed_dict)
          if (step % 50 == 0):
            print('%d\t%f\t%.1f%%\t%.1f%%' % (step, l, accuracy(predictions, batch_labels), accuracy(valid_prediction.eval(), valid_labels)))    
       print('Test accuracy: %.1f%%' % accuracy(test_prediction.eval() , test_labels))

       save_path_w_b = saver.save(sess, "path_checkpoint.ckpt")
       print("Model saved in file: %s" % save_path_w_b)

Answer 1

根据GradientDescentOptimizer的minimal 方法上的TensorFlow文档，“ var_list”必须是Variable对象的列表。 根据您的代码， resize_var_w是一个张量。

编辑要更具体：

如果您给优化器提供var_list ，顾名思义，它必须是变量列表。 在反向传播期间，优化器将循环var_list并仅更新列表中的变量，而不是图中的所有可训练变量。 单个变量不可迭代。

如果您只想更新一个张量，则可以尝试：

resize_var_w = [tf.assign(w_b_not['weight_4'], final1, validate_shape=False)]

我没有测试，但是应该可以工作。