python - Tensorflow - 我是否正确恢复模型？

Question

我有以下正在运行的代码(没有错误)。我的问题是我是否正确恢复模型？特别是我看不到语句 print(v_) 的任何输出。

所以，我想知道我是否正确执行了以下操作:

1. 恢复模型

2. 使用恢复的模型

   将 tensorflow 导入为 tf

   data, labels = cifar_tools.read_data('C:\\Users\\abc\\Desktop\\Testing')
   
   x = tf.placeholder(tf.float32, [None, 150 * 150])
   y = tf.placeholder(tf.float32, [None, 2])
   
   w1 = tf.Variable(tf.random_normal([5, 5, 1, 64]))
   b1 = tf.Variable(tf.random_normal([64]))
   
   w2 = tf.Variable(tf.random_normal([5, 5, 64, 64]))
   b2 = tf.Variable(tf.random_normal([64]))
   
   w3 = tf.Variable(tf.random_normal([38*38*64, 1024]))
   b3 = tf.Variable(tf.random_normal([1024]))
   
   w_out = tf.Variable(tf.random_normal([1024, 2]))
   b_out = tf.Variable(tf.random_normal([2]))
   
   def conv_layer(x,w,b):
       conv = tf.nn.conv2d(x,w,strides=[1,1,1,1], padding = 'SAME')
       conv_with_b = tf.nn.bias_add(conv,b)
       conv_out = tf.nn.relu(conv_with_b)
       return conv_out
   
   def maxpool_layer(conv,k=2):
       return tf.nn.max_pool(conv, ksize=[1,k,k,1], strides=[1,k,k,1], padding='SAME')
   
   def model():
       x_reshaped = tf.reshape(x, shape=[-1, 150, 150, 1])
   
       conv_out1 = conv_layer(x_reshaped, w1, b1)
       maxpool_out1 = maxpool_layer(conv_out1)
       norm1 = tf.nn.lrn(maxpool_out1, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
       conv_out2 = conv_layer(norm1, w2, b2)
       norm2 = tf.nn.lrn(conv_out2, 4, bias=1.0, alpha=0.001 / 9.0, beta=0.75)
       maxpool_out2 = maxpool_layer(norm2)
   
       maxpool_reshaped = tf.reshape(maxpool_out2, [-1, w3.get_shape().as_list()[0]])
       local = tf.add(tf.matmul(maxpool_reshaped, w3), b3)
       local_out = tf.nn.relu(local)
   
       out = tf.add(tf.matmul(local_out, w_out), b_out)
       return out
   
   model_op = model()
   
   cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(model_op, y))
   train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
   
   correct_pred = tf.equal(tf.argmax(model_op, 1), tf.argmax(y,1))
   accuracy = tf.reduce_mean(tf.cast(correct_pred,tf.float32))
   
   with tf.Session() as sess:
       sess.run(tf.global_variables_initializer())
       onehot_labels = tf.one_hot(labels, 2, on_value=1.,off_value=0.,axis=-1)
       onehot_vals = sess.run(onehot_labels)
       batch_size = len(data)
       # Restore model
       saver = tf.train.import_meta_graph('C:\\Users\\abc\\Desktop\\\Testing\\mymodel.meta')
       saver.restore(sess, tf.train.latest_checkpoint('./'))
       all_vars = tf.get_collection('vars')
       for v in all_vars:
           v_ = sess.run(v)
           print(v_)
   
   for j in range(0, 5):
       print('EPOCH', j)
       for i in range(0, len(data), batch_size):
           batch_data = data[i:i+batch_size, :]
           batch_onehot_vals = onehot_vals[i:i+batch_size, :]
           _, accuracy_val = sess.run([train_op, accuracy], feed_dict={x: batch_data, y: batch_onehot_vals})
           print(i, accuracy_val)
   
       print('DONE WITH EPOCH')
   

编辑 1

这样恢复有用吗？

saver = tf.train.Saver()
saver = tf.train.import_meta_graph('C:\\Users\\Abder-Rahman\\Desktop\\\Testing\\mymodel.meta')
saver.restore(sess, tf.train.latest_checkpoint('./'))
print('model restored'

编辑2

这就是我保存模型的方式:

#Save model
saver = tf.train.Saver()
saved_path = saver.save(sess, 'C:\\Users\\abc\\Desktop\\\Testing\\mymodel')
print("The model is in this file: ", saved_path)

谢谢。

Answer 1

您的保护码是正确的。而变量必须在检索集合之前添加到集合中。
tf.add_to_collection("vars", w1)
tf.add_to_collection("vars", b1)
...
然后 all_vars = tf.get_collection('vars')