Configuring RNN model...
WARNING:tensorflow:From /home/luo/TensorflowProject/LSTM_2019042202/LSTM_Model0504.py:83: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:

Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.

See @{tf.nn.softmax_cross_entropy_with_logits_v2}.

Loading test data...
INFO:tensorflow:Restoring parameters from checkpoints/textrnn050401/best_validation1
Testing...
Test Loss:    1.6, Test Acc:  90.97%
Precision, Recall and F1-Score...
              precision    recall  f1-score   support

           1       0.89      0.07      0.13        29
           2       0.91      0.03      0.07        29
           3       0.81      0.03      0.06        30
           4       0.74      0.12      0.35        30
           5       0.66      0.08      0.00        30

   micro avg       0.23      0.23      0.23       148
   macro avg       0.51      0.23      0.12       148
weighted avg       0.50      0.23      0.12       148

[[5 0 0 1 1]
 [0 6 0 1 0]
 [0 0 4 2 1]
 [1 1 0 3 1]
 [0 2 1 2 2]]

[[5 0 1 0 1]
 [0 6 0 1 0]
 [1 0 4 1 1]
 [1 1 0 3 1]
 [0 2 1 2 2]]

[[5 0 1 0 1]
 [0 6 0 1 0]
 [1 0 4 1 1]
 [1 0 1 3 2]
 [0 0 2 3 2]]

classID		precision
1       0.71
2       0.86
3       0.57
4       0.43
5       0.43

precision    recall  f1-score   support

           1       1.00      0.71      0.83         7
           2       0.75      0.86      0.80         7
           3       0.67      0.57      0.62         7
           4       0.45      0.71      0.56         7
           5       0.40      0.29      0.33         7

   micro avg       0.63      0.63      0.63        35
   macro avg       0.65      0.63      0.63        35
weighted avg       0.65      0.63      0.63        35

Confusion Matrix...
[[5 0 1 0 1]
 [0 6 0 1 0]
 [1 0 4 1 1]
 [1 0 1 3 2]
 [0 0 2 3 2]]

[[5 0 0 1 0]
 [0 6 0 0 0]
 [0 0 5 1 0]
 [0 0 1 4 1]
 [0 0 0 2 4]]

classID        precision
1    0.833
2    1.000
3    0.833
4    0.667
5    0.500
Confusion Matrix...
[[5 0 0 1 0]
 [0 6 0 0 0]
 [0 0 5 1 0]
 [0 0 1 4 1]
 [0 0 1 2 3]]

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