I am new to TensorFlow and I am just trying to see if my idea is even possible.
I have trained a model with multi class classifier. Now I can classify a sentence in input, but I would like to change the result of CNN, for example, to improve the score of classification or change the classification.
I want to try to train just a single sentence with its class on a trained model, is this possible?
If I understand your question correctly, you are trying to reload a previously trained model either to run it through further iterations, test it on a new sentence, or fine tune the model a bit. If this is the case, yes you can do this. Look into saving and restoring models (https://www.tensorflow.org/api_guides/python/state_ops#Saving_and_Restoring_Variables).
To give you a rough outline, when you initially train your model, after setting up the network architecture, set up a saver:
trainable_var = tf.trainable_variables()
sess = tf.Session()
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer
# Run/train your model until some completion criteria is reached
#....
#....
saver.save(sess, 'model.ckpt')
Now, to reload your model:
saver = tf.train.import_meta_graph('model.ckpt.meta')
saver.restore('model.ckpt')
#Note: if you have already defined all variables before restoring the model, import_meta_graph is not necessary
This will give you access to all the trained variables and you can now feed in whatever new sentence you have. Hope this helps.
Related
Everyone!
I have a question releate in trained model reusing( tensorflow ).
I have train model
I want predict new data used trained model.
I use DNNClassifier.
I have a model.ckpt-200000.meta, model.ckpt-200000.index, checkpoint, and eval folder.
but I don't know reuse this model..
plz help me.
First, you need to import your graph,
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
new_saver = tf.train.import_meta_graph('model.ckpt-200000.meta')
new_saver.restore(sess, tf.train.latest_checkpoint('./'))
Then you can give input to the graph and get the output.
graph = tf.get_default_graph()
input = graph.get_tensor_by_name("input:0")#input tensor by name
feed_dict ={input:} #input to the model
#Now, access theoutput operation.
op_to_restore = graph.get_tensor_by_name("y_:0") #output tensor
print sess.run(op_to_restore,feed_dict) #get output here
Few things to note,
You can replace the above code with your training part of the graph
(i.e you can get the output without training).
However, you still have to construct your graph as previously and
only replace the training part.
Above method only loading the weights for the constructed graph. Therefore, you have to construct the graph first.
A good tutorial on this can be found here, http://cv-tricks.com/tensorflow-tutorial/save-restore-tensorflow-models-quick-complete-tutorial/
If you don't want to construct the graph again you can follow this tutorial, https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc
How does one save the weights of a single neural network in a tensorflow graph so that it can be loaded in a different program into a network with the same architecture?
My training code requires 3 other neural networks for the training process alone. If I were to use saver.save(sess, 'my-model)', wouldn't it save all the variables in the tensorflow graph? This doesn't seem correct for my use case.
Maybe this comes from my misunderstanding of how tensorflow should work. Am I approaching this problem correctly?
The best approach would be to use tensorflow variables scope. Say you have model_1, model_2, and model_3 and you only want to save model_1:
First, define the models in your training code:
with tf.variable_scope('model_1'):
model one declaration here
...
with tf.variable_scope('model_2'):
model one declaration here
...
with tf.variable_scope('model_3'):
model one declaration here
...
Next, define saver over the variables of model_1:
model_1_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="model_1")
saver = tf.train.Saver(model_1_variables)
While training you can save a checkpoint just like you mentioned:
saver.save(sess, 'my-model')
After your training is done and you want to restore the weights in your evaluation code, make sure you define model_1 and saver the same way:
with tf.variable_scope('model_1'):
model one declaration here
...
model_1_variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="model_1")
saver = tf.train.Saver(model_1_variables)
sess = tf.Session()
saver.restore(sess, 'my-model')`
How can I finetune a pretrained model in tensorflow on a new dataset? In Caffe I can simply rename the last layer and set some parameters for random initialization. Is something similar possible in tensorflow?
Say I have a checkpoint file (deeplab_resnet.ckpt) and some code that sets up the computational graph in which I can modify the last layer such that it has the same number of ouputs as the new dataset has classes.
Then I try to start the session like this:
sess = tf.Session(config=config)
init = tf.initialize_all_variables()
sess.run(init)
trainable = tf.trainable_variables()
saver = tf.train.Saver(var_list=trainable, max_to_keep=40)
saver.restore(sess, 'ckpt_path/deeplab_resnet.ckpt')
However this gives me an error when calling the saver.restore function since it expects the exact same graph structure as the the one it was saved from.
How can I only load all weights except for the last layer from the 'ckpt_path/deeplab_resnet.ckpt' file?
I also tried changing the Classification layer name but no luck there either...
I'm using the tensorflow-deeplab-resnet model
You can specify the names of the variables that you want to restore.
So, you can get a list of all of the variables in the model and filter out the variables of the last layer:
all_vars = tf.all_variables()
var_to_restore = [v for v in all_vars if not v.name.startswith('xxx')]
saver = tf.train.Saver(var_to_restore)
See the documentation for the details.
Alternatively, you can try to load the whole model an create a new "branch" out of the layer before the last and use it in the cost function during the training.
In classify_image.py, the input image is fed with a loaded image in
predictions = sess.run(softmax_tensor,{'DecodeJpeg/contents:0': image_data})
What if I want to add new layers to the inception model and train the whole model again? Are the variables loaded from classify_image_graph_def.pb trainable? I saw that freeze_graph.py used convert_variables_to_constants to produce freezed graph. So can those loaded weights be trained again, are they constants? And how can I connect the input('shuffle_batch:0') to the inception model to the output of tf.train.shuffle_batch?
The model used in classify_image.py has its variables frozen into constants, and doesn't have any gradient ops, so it's not easy to turn it back into something trainable. You can see how we remove one layer and replace it with something trainable here:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py
It's hard to generalize though. You'd be better off looking at some examples of fine-tuning here:
https://github.com/tensorflow/models/tree/master/inception#how-to-fine-tune-a-pre-trained-model-on-a-new-task
I am a beginner in TensorFlow, currently training a CNN.
I am using Saver in order to save the parameters used by the model, but I am having concerns whether this would itself store all the Variables used by the model, and is sufficient to restore the values to re-run the program for performing classification/testing on the trained network.
Let us look at the famous example MNIST given by TensorFlow.
In the example, we have bunch of Convolutional blocks, all of which have weight, and bias variables that gets initialised when the program is run.
W_conv1 = init_weight([5,5,1,32])
b_conv1 = init_bias([32])
After having processed several layers, we create a session, and initialise all the variables added to the graph.
sess = tf.Session()
sess.run(tf.initialize_all_variables())
saver = tf.train.Saver()
Here, is it possible to comment the saver.save code, and replace it by saver.restore(sess,file_path) after the training, in order to restore the weight, bias, etc., parameters back to the graph? Is this how it should be ?
for i in range(1000):
...
if i%500 == 0:
saver.save(sess,"model%d.cpkt"%(i))
I am currently training on large dataset, so terminating, and restarting the training is a waste of time, and resources so I request someone to please clarify before the I start the training.
If you want to save the final result only once, you can do this:
with tf.Session() as sess:
for i in range(1000):
...
path = saver.save(sess, "model.ckpt") # out of the loop
print "Saved:", path
In other programs, you can load the model using the path returned from saver.save for prediction or something. You can see some examples at https://github.com/sugyan/tensorflow-mnist.
Based on the explanation in here and Sung Kim solution I wrote a very simple model exactly for this problem. Basically in this way you need to create an object from the same class and restore its variables from the saver. You can find an example of this solution here.