我尝试使用相同的数据集（MNIST手写数字数据集）重新训练预训练模型的最后一层，但重新训练的模型精度远低于初始模型。我的初始模型精度约为98%，而重新训练的模型精度在40-80%之间波动，具体取决于运行情况。当我不训练前两层时，得到的结果也相似。
以下是我尝试做的可视化。
以及代码:
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_dataepochs1 = 150epochs2 = 300batch_size = 11000learning_rate1 = 1e-3learning_rate2 = 1e-4# Base modeldef base_model(input, reuse=False): with tf.variable_scope('base_model', reuse=reuse): layer1 = tf.contrib.layers.fully_connected(input, 300) features = tf.contrib.layers.fully_connected(layer1, 300) return featuresmnist = input_data.read_data_sets('./mnist/', one_hot=True)image = tf.placeholder(tf.float32, [None, 784])label = tf.placeholder(tf.float32, [None, 10])features1 = base_model(image, reuse=False)features2 = base_model(image, reuse=True)# Logits1 trained with the base modelwith tf.variable_scope('logits1', reuse=False): logits1 = tf.contrib.layers.fully_connected(features1, 10, tf.nn.relu)# Logits2 trained while the base model is frozenwith tf.variable_scope('logits2', reuse=False): logits2 = tf.contrib.layers.fully_connected(features2, 10, tf.nn.relu)# Var Listsvar_list_partial1 = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='logits1')var_list_partial2 = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='base_model')var_list1 = var_list_partial1 + var_list_partial2var_list2 = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='logits2')# Sanity checkprint("var_list1:", var_list1)print("var_list2:", var_list2)# Cross Entropy Lossesloss1 = tf.nn.softmax_cross_entropy_with_logits(logits=logits1, labels=label)loss2 = tf.nn.softmax_cross_entropy_with_logits(logits=logits2, labels=label)# Train the final logits layertrain1 = tf.train.AdamOptimizer(learning_rate1).minimize(loss1, var_list=var_list1)train2 = tf.train.AdamOptimizer(learning_rate2).minimize(loss2, var_list=var_list2)# Accuracy operationscorrect_prediction1 = tf.equal(tf.argmax(logits1, 1), tf.argmax(label, 1))correct_prediction2 = tf.equal(tf.argmax(logits2, 1), tf.argmax(label, 1))accuracy1 = tf.reduce_mean(tf.cast(correct_prediction1, "float"))accuracy2 = tf.reduce_mean(tf.cast(correct_prediction2, "float"))with tf.Session() as sess: sess.run(tf.initialize_all_variables()) batches = int(len(mnist.train.images) / batch_size) # Train base model and logits1 for epoch in range(epochs1): for batch in range(batches): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train1, feed_dict={image: batch_xs, label: batch_ys}) # Train logits2 keeping the base model frozen for epoch in range(epochs2): for batch in range(batches): batch_xs, batch_ys = mnist.train.next_batch(batch_size) sess.run(train2, feed_dict={image: batch_xs, label: batch_ys}) # Print the both models after training accuracy = sess.run(accuracy1, feed_dict={image: mnist.test.images, label: mnist.test.labels}) print("Initial Model Accuracy After training final model:", accuracy) accuracy = sess.run(accuracy2, feed_dict={image: mnist.test.images, label: mnist.test.labels}) print("Final Model Accuracy After Training:", accuracy)提前感谢！
回答：
尝试从”logits1″和”logits2″中移除非线性操作。
我修改了您的代码如下:
# Logits1 trained with the base modelwith tf.variable_scope('logits1', reuse=False): #logits1 = tf.contrib.layers.fully_connected(features1, 10, tf.nn.relu) logits1 = tf.contrib.layers.fully_connected(features1, 10, None)# Logits2 trained while the base model is frozenwith tf.variable_scope('logits2', reuse=False): #logits2 = tf.contrib.layers.fully_connected(features2, 10, tf.nn.relu) logits2 = tf.contrib.layers.fully_connected(features2, 10, None)结果变为:
Initial Model Accuracy After training final model: 0.9805Final Model Accuracy After Training: 0.9658P.S. 对于MNIST分类器来说，300 + 300个神经元太多了，但我想您的重点不是分类MNIST 🙂
