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SupportNet: solving catastrophic forgetting in class incremental learning with support data
Can I use other data sets in the code that you provide? Do I need to modify the data format?
Hi,
I noticed that the fisher information is calculated by the test data. See line 345 in src_image_data/train_procedures.py
fisher_info_val=previous_network.compute_fisher_information(X_test_converted,session)
But I think we should calculate it using the training data. Did I misunderstand something or is there any reason to use the test data? Thanks.
Best!
#load Pfam dataset and encoding them into array functional_domain_encoding_list=Pfam.Pfam_from_pickle_file_encoding( '/home/liy0f/ec_project/data_and_feature/Pfam_name_list_new_data.pickle', '/home/liy0f/ec_project/data_and_feature/Pfam_model_names_list.pickle')
Hello,about the two pickles,can you tell me how to get these and what datasets they belong to in your paper, can you provide the two pickles for me ? thank you very much.
Hello, can you upload the breakhis and hela10 dataset in the src_image_data folder?
Hello,Because I don’t know how to deal with the data set when I run the experiment, can you provide the data set used in the code, thank you very much.
ok,I don't know why the last issues is closed, there is a question.
Traceback (most recent call last):
File "supportnet_ec.py", line 94, in
lambda: tf.constant(0.5, dtype=tf.float32))
File "/home/xiaolin/.local/lib/python2.7/site-packages/tensorflow/python/util/ deprecation.py", line 432, in new_func
return func(*args, **kwargs)
File "/home/xiaolin/.local/lib/python2.7/site-packages/tensorflow/python/ops/c ontrol_flow_ops.py", line 2040, in cond
orig_res_t, res_t = context_t.BuildCondBranch(true_fn)
File "/home/xiaolin/.local/lib/python2.7/site-packages/tensorflow/python/ops/c ontrol_flow_ops.py", line 1890, in BuildCondBranch
original_result = fn()
File "supportnet_ec.py", line 93, in
tf.gather(final_feature, support_ind))/tf.cast(tf.size(support_ind),dtype=tf .float32),
File "/home/xiaolin/.local/lib/python2.7/site-packages/tensorflow/python/ops/m ath_ops.py", line 874, in r_binary_op_wrapper
x = ops.convert_to_tensor(x, dtype=y.dtype.base_dtype, name="x")
File "/home/xiaolin/.local/lib/python2.7/site-packages/tensorflow/python/frame work/ops.py", line 1011, in convert_to_tensor
as_ref=False)
File "/home/xiaolin/.local/lib/python2.7/site-packages/tensorflow/python/frame work/ops.py", line 1107, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/home/xiaolin/.local/lib/python2.7/site-packages/tensorflow/python/frame work/constant_op.py", line 217, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/home/xiaolin/.local/lib/python2.7/site-packages/tensorflow/python/frame work/constant_op.py", line 196, in constant
value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "/home/xiaolin/.local/lib/python2.7/site-packages/tensorflow/python/frame work/tensor_util.py", line 424, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
Now, I am searching the solution about the problem.
When the program is running to the end, there is a state of insufficient memory. My server's memory is 128G. Is it not enough for this program?
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