Tensorflow保存和恢复模型的问题

Question

I am trying to use the Transfer Learning approach. Here is a snapshot for the code where my code is learning over the Training data :

max_accuracy = 0.0
    saver = tf.train.Saver()
    for epoch in range(epocs):
        shuffledRange = np.random.permutation(n_train)
        y_one_hot_train = encode_one_hot(len(classes), Y_input)
        y_one_hot_validation = encode_one_hot(len(classes), Y_validation)
        shuffledX = X_input[shuffledRange,:]
        shuffledY = y_one_hot_train[shuffledRange]
        for Xi, Yi in iterate_mini_batches(shuffledX, shuffledY, mini_batch_size):
            sess.run(train_step,
                     feed_dict={bottleneck_tensor: Xi,
                                ground_truth_tensor: Yi})
            # Every so often, print out how well the graph is training.
            is_last_step = (i + 1 == FLAGS.how_many_training_steps)
            if (i % FLAGS.eval_step_interval) == 0 or is_last_step:
                train_accuracy, cross_entropy_value = sess.run(
                  [evaluation_step, cross_entropy],
                  feed_dict={bottleneck_tensor: Xi,
                             ground_truth_tensor: Yi})
                validation_accuracy = sess.run(
                  evaluation_step,
                  feed_dict={bottleneck_tensor: X_validation,
                             ground_truth_tensor: y_one_hot_validation})
                print('%s: Step %d: Train accuracy = %.1f%%, Cross entropy = %f, Validation accuracy = %.1f%%' %
                    (datetime.now(), i, train_accuracy * 100, cross_entropy_value, validation_accuracy * 100))
                result_tensor = sess.graph.get_tensor_by_name(ensure_name_has_port(FLAGS.final_tensor_name))
                probs = sess.run(result_tensor,feed_dict={'pool_3/_reshape:0': Xi[0].reshape(1,2048)})
                if validation_accuracy > max_accuracy :
                   saver.save(sess, 'models/superheroes_model')
                   max_accuracy = validation_accuracy
                   print(probs)
            i+=1  

Here is where my code, where I am loading the model :

def load_model () :
    sess=tf.Session()    
    #First let's load meta graph and restore weights
    saver = tf.train.import_meta_graph('models/superheroes_model.meta')
    saver.restore(sess,tf.train.latest_checkpoint('models/'))
    sess.run(tf.global_variables_initializer())
    result_tensor = sess.graph.get_tensor_by_name(ensure_name_has_port(FLAGS.final_tensor_name))  
    X_feature = features[0].reshape(1,2048)        
    probs = sess.run(result_tensor,
                         feed_dict={'pool_3/_reshape:0': X_feature})
    print probs
    return sess  

So now for the same data point I am getting totally different results while training and testing. Its not even close. During testing, my probabilities are near to 25% as I have 4 classes. But during training highest class probability is 90%.
Is there any issue while saving or restoring the model?

Answer 1

Be careful -- you are calling

sess.run(tf.global_variables_initializer())

after calling

saver.restore(sess,tf.train.latest_checkpoint('models/'))

I've done similar before, and I think that resets all your trained weights/biases/etc. in the restored model.

If you must, call the initializer prior to restoring the model, and if you need to initialize something specific from the restored model, do it individually.

Answer 2

删除sess.run(tf.global_variables_initializer())在函数load_model ，如果你这样做，你的所有训练的参数将与该会为每个班级1/4概率的初始值被替换