Running on Python 2.7

I am trying to run this code on Python 2.7.  Is this a big mistake?
Here's some issues I encountered:

tf.mul() should now be tf.multiply()
tf_sub() should now be tf.subtract()
sigmoid_cross_entropy_with_logits has been renamed to: tf.nn.sigmoid_cross_entropy_with_logits,
scalar_summary has been renamed to: tf.summary.scalar(),
merge_all has been renamed to:  tf.summary.merge_all

https://stackoverflow.com/questions/41066244/tensorflow-module-object-has-no-attribute-scalar-summary
How should I deal with os.makedirs(settings.STATIC_ROOT, exist_ok=True) in Python 2.7?
try:
os.makedirs(settings.STATIC_ROOT)
except OSError as e:
if e.errno != errno.EEXIST:
raise

Hi!
Yes, unfortunately code was created with python 3.4+ and tensorflow 0.10.
First 5 issues points to tensorflow version, not python - at tensorflow 1.0 a lot of API were changed. I've not updated the code, because of not a lot of users. You may send a pull request if you want. Default tensorflow version mentioned in requirements.txt file.
Regarding os.makedirs(dir_name, exist_ok=True) - yes, such api was provided at Python 3 only. It can be replaced with few lines, as:
if not os.path.exists(dir_name):
    os.makedirs(dir_name)

Understood about tensorflow version.
I made the required changes and ran the script.  Any ideas about this error?
===== Evaluate embeddings though SVM =====
Traceback (most recent call last):
File "results_validation/visualize_distribution.py", line 34, in
notes = [get_notes_from_filepath(path) for path in handled_pathes]
File "results_validation/visualize_distribution.py", line 28, in get_notes_from_filepath
log_name = [l for l in all_logs if l.startswith(idx + '_')][0]
IndexError: list index out of range
btw  - tflearn should probably be added to requirments.txt

On Jun 28, 2017, at 1:33 AM, Illarion<EMAIL_ADDRESS>wrote:
Hi!
Yes, unfortunately code was created with python 3.4+ and tensorflow 0.10.
First 5 issues points to tensorflow version, not python - at tensorflow 1.0 a lot of API were changed. I've not updated the code, because of not a lot of users. You may send a pull request if you want. Default tensorflow version mentioned in requirements.txt file.
Regarding os.makedirs(dir_name, exist_ok=True) - yes, such api was provided at Python 3 only. It can be replaced with few lines, as:
if not os.path.exists(dir_name):
os.makedirs(dir_name)
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub https://github.com/ikhlestov/rbm_based_autoencoders_with_tensorflow/issues/1#issuecomment-311593844, or mute the thread https://github.com/notifications/unsubscribe-auth/AC9i27FwQiM_RpA_ZjEE7naJT3z6Zy41ks5sIg_MgaJpZM4OHGnt.

Does this look wrong?
…
0 [07:08<00:00, 20.63it/s]
rbm: train_layers_by_pairs__last_layer_binarized
Hamming: accuracy - 0, time consumption<PHONE_NUMBER>51 seconds
Dot product: accuracy - 0, time consumption 0 seconds
...
autoencoder: new_initialized_model__without_Gaussian_noise
Hamming: accuracy - 0, time consumption<PHONE_NUMBER>81 seconds
Dot product: accuracy - 0, time consumption 0 seconds
Results were saved to /tmp/find_similar_accuracy_results.csv

On Jun 28, 2017, at 11:40 AM, David Laxer<EMAIL_ADDRESS>wrote:
Understood about tensorflow version.
I made the required changes and ran the script.  Any ideas about this error?
===== Evaluate embeddings though SVM =====
Traceback (most recent call last):
File "results_validation/visualize_distribution.py", line 34, in
notes = [get_notes_from_filepath(path) for path in handled_pathes]
File "results_validation/visualize_distribution.py", line 28, in get_notes_from_filepath
log_name = [l for l in all_logs if l.startswith(idx + '_')][0]
IndexError: list index out of range
btw  - tflearn should probably be added to requirments.txt

On Jun 28, 2017, at 1:33 AM, Illarion<EMAIL_ADDRESS><EMAIL_ADDRESS>wrote:
Hi!
Yes, unfortunately code was created with python 3.4+ and tensorflow 0.10.
First 5 issues points to tensorflow version, not python - at tensorflow 1.0 a lot of API were changed. I've not updated the code, because of not a lot of users. You may send a pull request if you want. Default tensorflow version mentioned in requirements.txt file.
Regarding os.makedirs(dir_name, exist_ok=True) - yes, such api was provided at Python 3 only. It can be replaced with few lines, as:
if not os.path.exists(dir_name):
os.makedirs(dir_name)
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub https://github.com/ikhlestov/rbm_based_autoencoders_with_tensorflow/issues/1#issuecomment-311593844, or mute the thread https://github.com/notifications/unsubscribe-auth/AC9i27FwQiM_RpA_ZjEE7naJT3z6Zy41ks5sIg_MgaJpZM4OHGnt.

Yes, this is incorrect results.
Now I try make a version update at least for tensorflow 0.12 but I don't know how long may it take

Here’s the full output.
Does anything else look wrong?
David-Laxers-MacBook-Pro:rbm_based_autoencoders_with_tensorflow davidlaxer$ ./run_all.sh
===== Train RBM =====
Train model by pair layers
Only last layer is binarized
Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz
Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz
Train layers pair 0 and 1 for 2 epochs
Training model no: 0
Initialize new variables
WARNING:tensorflow:From /Users/davidlaxer/anaconda/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py:170: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use tf.global_variables_initializer instead.
Epoch: 0, time consumption: 0:00:34.575373
Epoch: 1, time consumption: 0:00:33.535722
Train layers pair 1 and 2 for 2 epochs
Training model no: 0
Restore variables from previous run:
bias_0
bias_1
W_0_1
Initialize not restored variables:
W_1_2:0
bias_2:0
WARNING:tensorflow:From /Users/davidlaxer/anaconda/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py:170: initialize_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use tf.variables_initializer instead.
Epoch: 0, time consumption: 0:00:22.907593
Epoch: 1, time consumption: 0:00:22.378566
Train layers pair 2 and 3 for 2 epochs
Training model no: 0
Restore variables from previous run:
bias_0
bias_1
bias_2
W_0_1
W_1_2
Initialize not restored variables:
W_2_3:0
bias_3:0
WARNING:tensorflow:From /Users/davidlaxer/anaconda/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py:170: initialize_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use tf.variables_initializer instead.
Epoch: 0, time consumption: 0:00:19.697597
Epoch: 1, time consumption: 0:00:20.018516
Get embeddings for test set
Get embeddings for train set
===== Train AutoEncoder initialized from RBM =====
Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz
Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz
Such variables were created:
W_0_1:0: (784, 484)
bias_1:0: (484,)
W_1_2:0: (484, 196)
bias_2:0: (196,)
W_2_3:0: (196, 100)
bias_3:0: (100,)
W_3_4:0: (196, 100)
bias_4:0: (196,)
W_4_5:0: (484, 196)
bias_5:0: (484,)
W_5_6:0: (784, 484)
bias_6:0: (784,)
Training model no: 0
!!!RBM model save file .ckpt with run_no: 0 not exists!!!
===== Train AutoEncoder initialized with new variables =====
Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz
Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz
Such variables were created:
W_0_1:0: (784, 484)
bias_1:0: (484,)
W_1_2:0: (484, 196)
bias_2:0: (196,)
W_2_3:0: (196, 100)
bias_3:0: (100,)
W_3_4:0: (196, 100)
bias_4:0: (196,)
W_4_5:0: (484, 196)
bias_5:0: (484,)
W_5_6:0: (784, 484)
bias_6:0: (784,)
Training model no: 0
WARNING:tensorflow:From /Users/davidlaxer/anaconda/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py:170: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use tf.global_variables_initializer instead.
Epoch: 0, time consumption: 0:00:38.965459
Epoch: 1, time consumption: 0:00:39.121245
Epoch: 2, time consumption: 0:00:40.023313
Epoch: 3, time consumption: 0:00:39.448177
Epoch: 4, time consumption: 0:00:38.664667
Epoch: 5, time consumption: 0:00:39.490704
Such variables were created:
W_0_1:0: (784, 484)
bias_1:0: (484,)
W_1_2:0: (484, 196)
bias_2:0: (196,)
W_2_3:0: (196, 100)
bias_3:0: (100,)
W_3_4:0: (196, 100)
bias_4:0: (196,)
W_4_5:0: (484, 196)
bias_5:0: (484,)
W_5_6:0: (784, 484)
bias_6:0: (784,)
Get embeddings for test set
Get embeddings for train set
===== Train AutoEncoder initialized from RBM without Gaussian noise=====
Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz
Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz
Such variables were created:
W_0_1:0: (784, 484)
bias_1:0: (484,)
W_1_2:0: (484, 196)
bias_2:0: (196,)
W_2_3:0: (196, 100)
bias_3:0: (100,)
W_3_4:0: (196, 100)
bias_4:0: (196,)
W_4_5:0: (484, 196)
bias_5:0: (484,)
W_5_6:0: (784, 484)
bias_6:0: (784,)
Training model no: 1
!!!RBM model save file .ckpt with run_no: 0 not exists!!!
===== Train AutoEncoder initialized with new variables without Gaussian noise =====
Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz
Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz
Such variables were created:
W_0_1:0: (784, 484)
bias_1:0: (484,)
W_1_2:0: (484, 196)
bias_2:0: (196,)
W_2_3:0: (196, 100)
bias_3:0: (100,)
W_3_4:0: (196, 100)
bias_4:0: (196,)
W_4_5:0: (484, 196)
bias_5:0: (484,)
W_5_6:0: (784, 484)
bias_6:0: (784,)
Training model no: 1
WARNING:tensorflow:From /Users/davidlaxer/anaconda/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py:170: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use tf.global_variables_initializer instead.
Epoch: 0, time consumption: 0:00:39.791340
Epoch: 1, time consumption: 0:00:41.863832
Epoch: 2, time consumption: 0:00:39.197791
Epoch: 3, time consumption: 0:00:38.405157
Epoch: 4, time consumption: 0:00:38.756205
Epoch: 5, time consumption: 0:00:39.403081
Such variables were created:
W_0_1:0: (784, 484)
bias_1:0: (484,)
W_1_2:0: (484, 196)
bias_2:0: (196,)
W_2_3:0: (196, 100)
bias_3:0: (100,)
W_3_4:0: (196, 100)
bias_4:0: (196,)
W_4_5:0: (484, 196)
bias_5:0: (484,)
W_5_6:0: (784, 484)
bias_6:0: (784,)
Get embeddings for test set
Get embeddings for train set
===== Display embeddings distribution from autoencoders(as background process) =====
===== Evaluate embeddings though SVM =====
Traceback (most recent call last):
File "results_validation/visualize_distribution.py", line 34, in
notes = [get_notes_from_filepath(path) for path in handled_pathes]
File "results_validation/visualize_distribution.py", line 28, in get_notes_from_filepath
log_name = [l for l in all_logs if l.startswith(idx + '_')][0]
IndexError: list index out of range
Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz
Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz
Time cons: 206.56s, type: default mnist dataset
Accuracy: 0.44660
precision    recall  f1-score   support
      0       0.96      0.69      0.81       980
      1       0.62      0.01      0.03      1135
      2       0.73      0.30      0.42      1032
      3       0.78      0.30      0.43      1010
      4       0.78      0.41      0.54       982
      5       0.50      0.38      0.43       892
      6       0.76      0.75      0.76       958
      7       0.74      0.33      0.45      1028
      8       0.39      0.47      0.42       974
      9       0.19      0.91      0.32      1009

avg / total       0.65      0.45      0.45     10000

Time cons: 41.23s, type: rbm: train_layers_by_pairs__last_layer_binarized
Accuracy: 0.42620
precision    recall  f1-score   support
      0       0.08      0.06      0.07       980
      1       0.72      0.47      0.57      1135
      2       0.46      0.40      0.42      1032
      3       0.41      0.38      0.40      1010
      4       0.40      0.31      0.35       982
      5       0.28      0.32      0.30       892
      6       0.59      0.85      0.70       958
      7       0.72      0.63      0.67      1028
      8       0.33      0.33      0.33       974
      9       0.30      0.50      0.38      1009

avg / total       0.43      0.43      0.42     10000

Time cons: 42.56s, type: autoencoder: new_initialized_model__with_Gaussian_noise
Accuracy: 0.09780
precision    recall  f1-score   support
      0       0.10      0.15      0.12       980
      1       0.11      0.13      0.12      1135
      2       0.10      0.10      0.10      1032
      3       0.10      0.08      0.09      1010
      4       0.08      0.05      0.06       982
      5       0.08      0.09      0.08       892
      6       0.12      0.08      0.10       958
      7       0.09      0.08      0.08      1028
      8       0.11      0.10      0.10       974
      9       0.09      0.11      0.10      1009

avg / total       0.10      0.10      0.10     10000

Time cons: 45.38s, type: autoencoder: new_initialized_model__without_Gaussian_noise
Accuracy: 0.08760
precision    recall  f1-score   support
      0       0.00      0.00      0.00       980
      1       0.01      0.00      0.01      1135
      2       0.00      0.00      0.00      1032
      3       0.00      0.00      0.00      1010
      4       0.00      0.00      0.00       982
      5       0.06      0.29      0.10       892
      6       0.03      0.01      0.01       958
      7       0.00      0.00      0.00      1028
      8       0.12      0.62      0.21       974
      9       0.00      0.00      0.00      1009

avg / total       0.02      0.09      0.03     10000

Results were saved to /tmp/svm_clusterizationn_test_results.csv
===== Evaluate embeddings though most similar accuracy
Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz
Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz
hamming_dist/default mnist dataset: 100%|█| 10000/10000 [16:57<00:00, 15.02it/s]
dot_product/default mnist dataset: 100%|█| 10000/10000 [1:08:26<00:00,  5.00it/s]
default mnist dataset
Hamming: accuracy - 0, time consumption 1017.47319913 seconds
Dot product: accuracy - 0, time consumption 0 seconds
hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   0%| | 0/10000 [hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   0%| | 10/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   0%| | 19/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   0%| | 30/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   0%| | 42/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   0%| | 50/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 58/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 65/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 72/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 79/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 86/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 92/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 98/10000 hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 106/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 112/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 118/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 124/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 130/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 135/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 140/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 144/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   1%| | 148/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   2%| | 152/10000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:   2%| | 159/10000hamming_dist/rbm: 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train_layers_by_pairs__last_layer_binarized:  59%|▌| 5902/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  59%|▌| 5911/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  59%|▌| 5919/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  59%|▌| 5926/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  59%|▌| 5935/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  59%|▌| 5943/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 5950/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 5959/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 5967/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 5974/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 5982/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 5989/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 5996/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 6002/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 6009/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 6017/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 6025/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 6032/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 6038/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  60%|▌| 6050/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6057/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6064/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6071/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6077/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6084/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6093/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6101/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6108/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6117/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6124/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6135/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  61%|▌| 6147/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  62%|▌| 6158/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  62%|▌| 6169/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  62%|▌| 6179/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  62%|▌| 6193/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_binarized:  62%|▌| 6204/1000hamming_dist/rbm: train_layers_by_pairs__last_layer_b

It seems that first RBM training pass. After autoencoders try to initialize from saved RBM, but tensorflow have another API/names for saves and two training of the autoencoders are failed.
This caused by line #412 at the autoencoder.py

And after all there are some troubles with names of stored tensor/variables

So, this error means the RBM training phase didn’t save the model properly?

!!!RBM model save file .ckpt with run_no: 0 not exists!!!

On Jun 28, 2017, at 1:01 PM, Illarion<EMAIL_ADDRESS>wrote:
And after all there are some troubles with names of stored tensor/variables
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No, this means that RBM was trained/saved as expected. Now it saved as model.ckpt.index file. So you can just add 'index' at the end of this line.
But later this lines in the autoencoder will fail, because tensorflow change once again naming convetions for saved layers inside checkpoint.

Is this the issue you alluded to with the contents/format of the checkpoint file?
https://github.com/tensorflow/tensorflow/issues/3967

On Jun 28, 2017, at 1:24 PM, Illarion<EMAIL_ADDRESS>wrote:
No, this means that RBM was trained/saved as expected. Now it saved as model.ckpt.index file. So you can just add 'index' at the end of this line.
But later this lines in the autoencoder will fail, because tensorflow change once again naming convetions for saved layers inside checkpoint.
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And this one ...
https://github.com/tensorflow/tensorflow/blob/e0344f1e7ceff9c192369000d244c4ca47d7a1cc/tensorflow/python/tools/inspect_checkpoint.py

On Jun 28, 2017, at 1:24 PM, Illarion<EMAIL_ADDRESS>wrote:
No, this means that RBM was trained/saved as expected. Now it saved as model.ckpt.index file. So you can just add 'index' at the end of this line.
But later this lines in the autoencoder will fail, because tensorflow change once again naming convetions for saved layers inside checkpoint.
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Reply to this email directly, view it on GitHub, or mute the thread.

Yes, the second link may help in the debugging

I installed a python 3.4 virtual environment, installed tdqm, installed tensorflow 1.1.0
and replaced a few old tf functions mul, sub, merge_summaries, ...
Does this look correct?

notes,accuracy,prec,f_score,recall
default mnist dataset,0.4466,0.647146339198,0.460551796425,0.454023693039
rbm: train_layers_by_pairs__last_layer_binarized,0.413,0.43410044028,0.413626653203,0.411131796451
autoencoder: new_initialized_model__with_Gaussian_noise,0.0968,0.0957067341317,0.0791412829712,0.0962315867979
autoencoder: new_initialized_model__without_Gaussian_noise,0.1009,0.01009,0.0183304568989,0.1
autoencoder: new_initialized_model__without_Gaussian_noise
Hamming: accuracy - 0.1012, time consumption 68.1845691204071 seconds
Dot product: accuracy - 0.1012, time consumption 0.1012 seconds
Results were saved to /tmp/find_similar_accuracy_results.csv

No, at least you should not have negative loss


JSDocs

One of the most powerful tools to help other developers (internal and external) use our functions is to proper document them with JSDocs. We should document all important functions properly with JSDocs annotation. This seems like a tedious task but here is a simple guideline how to gradually improve our documentation coverage:
Every time one doesn't understand what a function does and goes into code to read about, they should just add some JSDocs to that function and write down the findings.
Here is a screenshot illustrating how to write JSDocs and how useful they are:

A more deep dive into JSDocs can be found here:
http://usejsdoc.org/index.html#block-tags
Note: It is superfluous to annotate type information in JSDocs because TypeScript is already doing it.

@fubhy close if you don't want this. I'm just reviewing some issues and thought it might apply

We have this now. Using typescript docs actually so even more powerful. Check here: https://melonproject.github.io/melonjs/


execute_with_backoff should only catch API rate errors

Currently execute_with_backoff is catching all HttpErrors. We need to be more specific.

Commenting out backoff code until this is fixed.


fix(circle): publish dir

:tada: This PR is included in version 1.2.2 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


Add/enable mypy

Signed-off-by: Ben Alkov<EMAIL_ADDRESS>Maintainers will complete the following section

[ ] Commit messages are descriptive enough
[ ] Code coverage from testing does not decrease and new code is covered
[ ] JSON/YAML configuration changes are updated in the relevant schema
[ ] Changes to metadata also update the documentation for the metadata
[ ] Pull request has a link to an osbs-docs PR for user documentation updates
[ ] New feature can be disabled from a configuration file

Tested in my fork (fails due to expected mypy errors)

Can you also silence other failing modules too? We cannot merge failing CI. Errors should be fixed incrementally and checks enabled for modules. This is helpful for new code and to prevent regressions.

Can you also silence other failing modules too? We cannot merge failing CI. Errors should be fixed incrementally and checks enabled for modules. This is helpful for new code and to prevent regressions.

Sure.
I think, given that we only have hits in 2 files, I'll ignore each file separately; that way, as they are fixed one-by-one, they can be remove from  the config.

atomic_reactor/inner.py is failing in CI

@MartinBasti, @chmeliik; CI passing, please re-review. smh it's ugly, but it works...


Efficient lookup by zero-terminated C string

In some use cases (like returning a pointer by a symbol in FFI scenario) it is needed to look up a value by a given *const c_char, without assuming anything about the encoding except that a single NUL byte always ends the sequence. In this regard it could be similar to a binary lookup, but without having the length of the input sequence as an immediate value beforehand.
Is it reasonable to provide a specialized routine for incremental comparison to save cycles by not performing the length count in a separate pass?

It seems like you can have the PhfHash implementation take care of looking for the null terminator as it's hashing?

We could implement PhfHash and FmtConst for CStr now.

Thinking about this more, I'm not sure if hiding the unsafety of looking for a \0 behind a raw pointer is worth saving an extra scan of the string since the initial length finding will bring it into cache anyway.

@abonander The unsafety can be dealt with in strncmp-esque manner, by having a wrapper type with both a c_char pointer and a maximum length (not actual length). And I think the caching might not always work as intended in low-end embedded environments such as Cortex-M0. Individual cycles can be pretty expensive there, especially in real-time tasks.


Fix bug in getting function name

What type of PR is this：

[x] bug
[ ] feature
[ ] enhancement
[ ] others

Which issues of this PR fixes ：

Fixes #
Problem Summary(Required) ：

before
ERROR 1064 (HY000): Expr evaluate meet error:binary column exceed 4G in functionfunc
after
ERROR 1064 (HY000): Expr evaluate meet error: binary column exceed 4G in function "repeat"

backport branch-2.2

@Mergifyio backport branch-2.2

@mergify backport branch-2.2


Serious bug somewhere in communication between nanos and GUI

With the display connected and operating, PMax and VMAX cannot be changed on the GUI - they bounce back to the value on the display panel. Other parameters seem to work properly, including changing the display to match the GUI.
Also when changing On/Off on the GUI, the display follows properly. But when changing On/Off on the Display, theGUI does not follow.

This has been fixed in devel.


DNM: toolchain build

Please test with following PRs:
https://github.com/apple/swift-installer-scripts/pull/258
@swift-ci please build toolchain Windows platform

Please test with following PRs:
https://github.com/apple/swift-installer-scripts/pull/259
@swift-ci please build toolchain Windows platform

Please test with following PRs:
https://github.com/apple/swift-installer-scripts/pull/242
@swift-ci please build toolchain Windows platform

Please test with following PRs:
https://github.com/apple/swift-installer-scripts/pull/265
@swift-ci please build toolchain Windows platform

Please test with following PRs:
https://github.com/apple/swift-installer-scripts/pull/267
@swift-ci please build toolchain Windows platform

Please test with following PRs:
https://github.com/apple/swift-installer-scripts/pull/267
@swift-ci please build toolchain Windows platform

Please test with following PRs:
https://github.com/apple/swift-installer-scripts/pull/270
@swift-ci please build toolchain Windows platform

Is it ok if we close this PR?


Move _containerLeft init before transform

Fixes issue in https://github.com/hyperknot/Leaflet/issues/3 that was reproduced with https://github.com/hyperknot/Leaflet/issues/3#issuecomment-229182323

Great, we just need to fix the zoomAnimation: false case, as that one is skipped now.

True! But I just tried leaflet master and zoomAnimation is broken, even with the most basic example. Probably a regression after the recent changes. Will log an issue in leaflet

Bah it didn't work in RC1 either, I guess this option is not really enabled that much ahah

You mean zoomAnimation: false is broken? I cannot see it. Can you describe it? Also I'm on gitter.


perlPackages.Text-MultiMarkdown: init at 1.000035

Added package for multimarkdown file converter.

After some searching, I found that this is an older (perl) version of multimarkdown. The newest multimarkdown (v4) is written in C. In a moment I will provide a package for multimarkdown-4.7.1 instead. Apologies for the noise.


Discord

Do you have an discord server for this project?

Coming up this week🤫

Looking forward to it :)

There you go guys! Feel free to join: https://discord.gg/y24DBzCh


Animated: useNativeDriver was not specified. This is a required option and must be explicitly set to true or false

ModalBox
Showing YellowBox Warning Should use useNativeDriver in my current react-native
Using react-native release - "16.13.1"

Duplicate of #312

Estou com o mesmo problema

Me too have this problem!

same for me

fixed this problem
https://github.com/maxs15/react-native-modalbox/pull/315/commits/50979244a4cab2488c38f5bae43ce20d7a915171

When I integrate this solution in my code, I get an error when I try to implement swipeToClose modals.

.

Same here

same for me

Please merge it in master.

Fix deployed in 2.0.1

Edit: this can be resolved by adjusting it to:
 const animEvt = Animated.event([null, {customY: position}], {useNativeDriver: false});

I tried this, the warning is gone, but now I get a different error:
TypeError: animEvt is not a function. (In 'animEvt(evt, state)', 'animEvt' is an instance of AnimatedEvent)


Use include_str! in test

I use Rust skeptic for managing my snippets for SOME. I was creating a snippet like this:
fn main() {
    // The contents of file.txt is "Hello world!"
    let str_from_file = include_str!("file.txt");
    assert_eq!(str_from_file, "Hello world!");
}

And I Couldn't get it to work because file.txt wasn't available at compile time.
How would I add external files that are supposed to be compiled into the tests? This is super useful for Internationalization like Fluent and Template libraries like Tera.

See #140.


In my opinion, launch instance opens wrong page

Environment (please complete the following information):

OS: macOS 12.1
Extension version: v1.4.5

Describe the bug:

I clicked on launch project in the extension view, and a page that has nothing to do with my project is opened.
Repro steps:

created a new project with a basic html file.  body contained only an h1 tag.
with the html file active, clicked on launch project in the dev tools view.
two tabs are opened.  one with the dev tools and one with dev tools screen cast
the screen cast tab shows contents of file:///Users/chris/.vscode/extensions/ms-edgedevtools.vscode-edge-devtools-1.4.5/out/startpage/index.html

Expected behavior:

I expected the screen cast tab to show a page related to my project.  (e.g. my only html file titled index.html)
Additional context:

I closed the  instance listed under targets in the edge view and clicked on launch instance again after selecting the index.html for a second time.  This time the screen cast tab opened for "about:blank".  Also unexpected.

After reading more documentation, it looks like this is the way it is supposed to work.  Personally, I'd rather it loaded-up the html page I'm currently editing rather than having to create the launch.json file and then edit the url/file entry.  So I guess this would be a feature request related to a better initial use experience.

you want to edit a single file rather than run a local server. As any file is most likely to have other dependencies (styles, scripts and so on)

We'll, I expected to see my file in the browser is the key thing. That's not unreasonable.
Besides, while I'd normally use a web-server, it isn't strictly speaking necessary.
If I open a file directly off the file system the browser will happily pull in referenced resources even without the support of a (web)server.

I tend to agree with @chris-miner here. Very often when I start a new web project, I start with a simple index.html file and no web server. I put in my content, and quickly after I need devtools to start styling the page.
When using the extension, the first-run experience in that workflow could be a bit better. I think it would be fair for the extension to detect the main index.html file and load it in the browser using the file:// protocol.


Add a Gitter chat badge to Readme.md

felixge/node-formidable now has a Chat Room on Gitter
@xarguments has just created a chat room. You can visit it here: https://gitter.im/Formidable-Lib-node-formidable/Lobby.
This pull-request adds this badge to your Readme.md:

If my aim is a little off, please let me know.
Happy chatting.
PS: Click here if you would prefer not to receive automatic pull-requests from Gitter in future.

Note: This pull request was created automatically, by Gitter.

Actually... Formidable-Lib-node-formidable/Lobby looks... nah. :D

@olstenlarck, Agree. It was generated automatically by Gitter.
What about changing to this?
https://gitter.im/node-formidable
Note that https://gitter.im/formidable is unavailable

Make sense yea. I'm good with node-formidable - lets be consistent and 1:1. :)


Implement course filters as SQL

Apply course filters via SQL instead of in state. It is more efficient to filter courses at the database level. Allows us to utilize Flask-SQLAlchemy's paginate function for #36.
Replaces #74

@MikeVarchola I went a different route then SQL views. I think this should easily allow you to implement the paginate feature. Do you mind giving it a quick review to see if it's compatible?

@jonbiemond  but yes, this solution will work with paginate() from SQLAlchemy


Break long method definitions when auto-correcting

Add autocorrect capabilities to Layout/LineLength, by wrapping method definitions on multiple lines.
# bad
def foo(abc: "100000", def: "100000", ghi: "100000", jkl: "100000", mno: "100000")
end

# good (autocorrect)
def foo(abc: "100000", def: "100000",
  ghi: "100000", jkl: "100000", mno: "100000")
end

Combined with https://github.com/rubocop/rubocop/pull/10691 it allows for some nice autocorrect.
This was split from the larger WIP pull request here: https://github.com/rubocop/rubocop/pull/10681

Before submitting the PR make sure the following are checked:

[x] The PR relates to only one subject with a clear title and description in grammatically correct, complete sentences.
[x] Wrote good commit messages.
[x] Commit message starts with [Fix #issue-number] (if the related issue exists).
[x] Feature branch is up-to-date with master (if not - rebase it).
[x] Squashed related commits together.
[x] Added tests.
[x] Ran bundle exec rake default. It executes all tests and runs RuboCop on its own code.
[x] Added an entry (file) to the changelog folder named {change_type}_{change_description}.md if the new code introduces user-observable changes. See changelog entry format for details.

I've run this feature locally and infinite loop is detected on 98981457abc38c3edd1f6a2a738f144314fedd0a.
# .rubocop.yml
AllCops:
  NewCops: enable
  TargetRubyVersion: 3.1

# example.rb
def foo(abc: '100000', def: '100000', ghi: '100000', jkl: '100000', mno: '100000'); end

$ bundle exec rubocop -a example.rb
Inspecting 1 file
W

Offenses:

example.rb:1:1: C: [Corrected] Style/EmptyMethod: Put empty method definitions on a single line.
def foo(abc: '100000', def: '100000', ghi: '100000', jkl: '100000', mno: '100000'); ...
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
example.rb:1:38: C: [Corrected] Layout/TrailingWhitespace: Trailing whitespace detected.
def foo(abc: '100000', def: '100000',
                                     ^
example.rb:1:41: C: [Corrected] Layout/LineLength: Line is too long. [83/40]
def foo(abc: '100000', def: '100000', ghi: '100000', jkl: '100000', mno: '100000');
                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
example.rb:1:41: C: [Corrected] Layout/LineLength: Line is too long. [87/40]
def foo(abc: '100000', def: '100000', ghi: '100000', jkl: '100000', mno: '100000'); end
                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
example.rb:1:83: C: [Corrected] Style/Semicolon: Do not use semicolons to terminate expressions.
def foo(abc: '100000', def: '100000', ghi: '100000', jkl: '100000', mno: '100000');
                                                                                ^
example.rb:2:1: C: [Corrected] Layout/ParameterAlignment: Align the parameters of a method definition if they span more than one line.
ghi: '100000', jkl: '100000', mno: '100000')
^^^^^^^^^^^^^
example.rb:2:2: W: [Corrected] Layout/DefEndAlignment: end at 2, 1 is not aligned with def at 1, 0.
 end
 ^^^
example.rb:2:41: C: Layout/LineLength: Line is too long. [52/40]
        ghi: '100000', jkl: '100000', mno: '100000')
                                        ^^^^^^^^^^^^

0 files inspected, 8 offenses detected, 7 offenses corrected
Infinite loop detected in /Users/nobuyo/dev/rubocop-test/example.rb and caused by Layout/LineLength -> Layout/DefEndAlignment, Layout/LineLength, Style/EmptyMethod, Style/Semicolon -> Layout/ParameterAlignment, Layout/TrailingWhitespace, Style/EmptyMethod -> Layout/LineLength, Style/EmptyMethod
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:296:in `check_for_infinite_loop'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:279:in `block in iterate_until_no_changes'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:278:in `loop'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:278:in `iterate_until_no_changes'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:247:in `do_inspection_loop'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:130:in `block in file_offenses'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:155:in `file_offense_cache'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:129:in `file_offenses'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:120:in `process_file'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:101:in `block in each_inspected_file'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:100:in `each'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:100:in `reduce'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:100:in `each_inspected_file'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:86:in `inspect_files'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/runner.rb:47:in `run'
/Users/nobuyo/dev/rubocop-korri/lib/rubocop/cli/command/execute_runner.rb:26:in `block in execute_runner'

# ...

I've run this feature locally and infinite loop is detected on 9898145.

Looks like you example is already an infinite loop on the master branch, Style/EmptyMethod (with EnforcedStyle: compact) doesn't play well with Layout/LineLength.
Using the expanded style does work:
# .rubocop.yml
AllCops:
  NewCops: enable
  TargetRubyVersion: 3.1

Layout/LineLength:
  Max: 40
Style/EmptyMethod:
  EnforcedStyle: expanded

# example.rb (after autocorrect)
def foo(abc: '100000', def: '100000',
        ghi: '100000', jkl: '100000', mno: '100000')
end

Sorry if not relevant, this PR is pretty short and laconic, just like a warning:
I like configuration like this:
Layout/FirstArgumentIndentation:
  EnforcedStyle: consistent
Layout/FirstParameterIndentation:
  EnforcedStyle: consistent
Layout/FirstArrayElementIndentation:
  EnforcedStyle: consistent
Layout/FirstHashElementIndentation:
  EnforcedStyle: consistent
Layout/MultilineMethodCallIndentation:
  EnforcedStyle: indented
Layout/MultilineOperationIndentation:
  EnforcedStyle: indented
Layout/MultilineArrayBraceLayout:
  EnforcedStyle: new_line
Layout/MultilineHashBraceLayout:
  EnforcedStyle: new_line
Layout/ArgumentAlignment:
  EnforcedStyle: with_fixed_indentation
Layout/ParameterAlignment:
  EnforcedStyle: with_fixed_indentation

And I'd like to see correct… style of breaking, I mean new lines instead of alignment.
I hope this is already works correctly, maybe just we should add more specs for it.

Not sure what you are concerned about (as in not sure I understand)? The change here is so that LineLength can wrap the method definition on it's own.

I mean you have specs like this:
expect_correction(<<~RUBY)
  def foo(abc: "100000", def: "100000",#{trailing_whitespace}
  ghi: "100000", jkl: "100000", mno: "100000")
  end
RUBY

And probably there should be additional context with another style of FirstParameterIndentation and different expectation. Just to be sure that it works. align_parentheses, as I see, so it'd be (I believe):
expect_correction(<<~RUBY)
  def foo(
          abc: "100000", def: "100000",#{trailing_whitespace}
ghi: "100000", jkl: "100000", mno: "100000")
  end
RUBY

Once the method definition is wrapped it becomes "multiline" and other cops can take over, including the new Layout/MultilineMethodParameterLineBreaks I'm proposing to introduce here: #10691

I'm discussing this small PR, and description and specs what I've seen. I'm not sure about (draft and large) 10691, maybe there it's resolved, but, again, I'm talking just about this PR and maybe I'm wrong. So, that's why I wrote from the start: "may be non-relevant and it's just a warning".
I hope it's more understandable now.

@AlexWayfer I think what you are describing is Layout/FirstMethodParameterLineBreak here is an example of how those rule combine to nicelly autocorrect:
# .rubocop.yml
AllCops:
  NewCops: enable
  TargetRubyVersion: 3.1

Layout/LineLength:
  Max: 40
Style/EmptyMethod:
  EnforcedStyle: expanded
Layout/FirstParameterIndentation:
  EnforcedStyle: consistent
Layout/ParameterAlignment:
  EnforcedStyle: with_fixed_indentation
Layout/FirstMethodParameterLineBreak:
  Enabled: true

# source (bad)
# frozen_string_literal: true

def foo(abc: '100000', def: '100000', ghi: '100000', jkl: '100000', mno: '100000')
end

# result (good)
# frozen_string_literal: true

def foo(
  abc: '100000', def: '100000',
  ghi: '100000', jkl: '100000', mno: '100000'
)
end

(If you run the same example on the master branch, nothing will get autocorrected)
The goal of the change here, is just for LineLength to wrap the method definition on multiple lines, so that other Cop can take over and format it the way you like.
As for specs, as far as I have seen, the codebase does not have specs for combined rules!

@bbatsov for context Layout/MultilineMethodParameterLineBreaks won't be auto-correctable for long lines until this PR is merged too!

Looks good. Sorry for the slow turnaround here!


AsyncEx.Tasks referenced version of disposables could use an update

When updating to Nito.AsyncEx.Tasks 5.0.0 that dll has a reference to Nito.Disposables 2.0.0. If you upgrade to Nito.Disposable 2.0.1 this dll wont be copied into any project where Nito is implicit used.

Hello,
i confirm the same bug there. It works OK with Nito.Disposables 2.0.0.
If we update nuget package for DIsposables to be 2.1.0 like nuget package manager says it is an update, then it doesnt work anymore.

Still running into the same issue. Is this project dead or is someone still there to fix this issue? If it's dead, what alternatives are there to be considered?

@StephenCleary Thanks for clarification of this matter. As we're still stuck on .net Framework, I didn't really know whether this is still a problem on newer .net or not. Now I got one more point to convince my ceo of an upgrade :)

I hear that; we finally got the go-ahead to do a .NET Core conversion this year, and I'm looking forward to getting that done.
But we transitioned to SDK-style csproj with PackageReference a few years ago and it has been a lifesaver. Before that we were running into binding redirect issues constantly. So that may be something to explore if you can't go all the way to Core yet.

Thanks again, will try to go that step to PackageReference already asap :)

what alternatives are there to be considered?

@alex3683 You could check out my library as an alternative. https://github.com/timcassell/ProtoPromise I implemented all the async sync primitives (not the collections).


PyCon UK 2014

PyCon UK 2014

Web
http://2014.pyconuk.org/

Playlist
Part of https://www.youtube.com/channel/UChA9XP_feY1-1oSy2L7acog/videos

http://web.archive.org/web/20150111124900/http://pyconuk.net/Schedule
http://web.archive.org/web/20160126082717/http://pyconuk.net/Talks


[Suggestion:] Implement reporting system on Discord/Twitter

I would like to know if it is possible for you to add that when a player is banned, that you can put the api of a bot in discord or the twitter account, so that it publishes the message of "the player has been banned for x, x days "
Like this
https://www.youtube.com/watch?v=b7gt3HsbBv4

Good Idea. I'll add Discord Webhook Support.

Done in V2.1.0!