• test3


    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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  • 原文地址:https://www.cnblogs.com/herd/p/10811131.html
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