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我有一个非常常见的用途，即冻结Inception的底层并仅训练前两层，之后我降低学习速率并微调整个初始模型。如何从Inception-3检查点恢复具有不同可训练变量的训练
这里是我跑第一部分
train_dir='/home/ubuntu/pynb/TF play/log-inceptionv3flowers'
with tf.Graph().as_default():
tf.logging.set_verbosity(tf.logging.INFO)
dataset = get_dataset()
images, _, labels = load_batch(dataset, batch_size=32)
# Create the model, use the default arg scope to configure the batch norm parameters.
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, _ = inception.inception_v3(images, num_classes=5, is_training=True)
# Specify the loss function:
one_hot_labels = slim.one_hot_encoding(labels, 5)
tf.losses.softmax_cross_entropy(one_hot_labels, logits)
total_loss = tf.losses.get_total_loss()
# Create some summaries to visualize the training process:
tf.summary.scalar('losses/Total Loss', total_loss)
# Specify the optimizer and create the train op:
optimizer = tf.train.RMSPropOptimizer(0.001, 0.9,
momentum=0.9, epsilon=1.0)
train_op = slim.learning.create_train_op(total_loss, optimizer, variables_to_train=get_variables_to_train())
# Run the training:
final_loss = slim.learning.train(
train_op,
logdir=train_dir,
init_fn=get_init_fn(),
number_of_steps=4500,
save_summaries_secs=30,
save_interval_secs=30,
session_config=tf.ConfigProto(gpu_options=gpu_options))
print('Finished training. Last batch loss %f' % final_loss)
其正常运行，然后我的代码运行的第二部分
train_dir='/home/ubuntu/pynb/TF play/log-inceptionv3flowers'
with tf.Graph().as_default():
tf.logging.set_verbosity(tf.logging.INFO)
dataset = get_dataset()
images, _, labels = load_batch(dataset, batch_size=32)
# Create the model, use the default arg scope to configure the batch norm parameters.
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, _ = inception.inception_v3(images, num_classes=5, is_training=True)
# Specify the loss function:
one_hot_labels = slim.one_hot_encoding(labels, 5)
tf.losses.softmax_cross_entropy(one_hot_labels, logits)
total_loss = tf.losses.get_total_loss()
# Create some summaries to visualize the training process:
tf.summary.scalar('losses/Total Loss', total_loss)
# Specify the optimizer and create the train op:
optimizer = tf.train.RMSPropOptimizer(0.0001, 0.9,
momentum=0.9, epsilon=1.0)
train_op = slim.learning.create_train_op(total_loss, optimizer)
# Run the training:
final_loss = slim.learning.train(
train_op,
logdir=train_dir,
init_fn=get_init_fn(),
number_of_steps=10000,
save_summaries_secs=30,
save_interval_secs=30,
session_config=tf.ConfigProto(gpu_options=gpu_options))
print('Finished training. Last batch loss %f' % final_loss)
注意的是，在第二部分中，我没有通过代码任何东西变成create_train_op的variables_to_train参数。那么这个错误显示
NotFoundError (see above for traceback): Key InceptionV3/Conv2d_4a_3x3/BatchNorm/beta/RMSProp not found in checkpoint
[[Node: save_1/RestoreV2_49 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_recv_save_1/Const_0, save_1/RestoreV2_49/tensor_names, save_1/RestoreV2_49/shape_and_slices)]]
[[Node: save_1/Assign_774/_1550 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_2911_save_1/Assign_774", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
我怀疑它在寻找的InceptionV3/Conv2d_4a_3x3层，这是不存在的RMSProp变量，因为我没有在以前的检查站列车层。我不知道如何实现我想要的功能，因为在文档中没有关于如何执行此操作的示例。