ValueError: Unknown metric function when using cus

Question

Keras 2.x killed off a bunch of useful metrics that I need to use, so I copied the functions from the old metrics.py file into my code, then included them as follows.

def precision(y_true, y_pred): #taken from old keras source code
     true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
     predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
     precision = true_positives / (predicted_positives + K.epsilon())
     return precision
def recall(y_true, y_pred): #taken from old keras source code
     true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
     possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
     recall = true_positives / (possible_positives + K.epsilon())
     return recall

...

model.compile(loss='categorical_crossentropy', optimizer='adam', 
metrics=['accuracy', precision, recall])

and this results in

ValueError: Unknown metric function:precision

What am I doing wrong? I can't see anything I'm doing wrong according to Keras documentation.

edit:

Here is the full Traceback:

 Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/Library/Python/2.7/site-packages/keras/models.py", line 274, in 
load_model
    sample_weight_mode=sample_weight_mode)
  File "/Library/Python/2.7/site-packages/keras/models.py", line 824, in 
compile
     **kwargs)
  File "/Library/Python/2.7/site-packages/keras/engine/training.py", line 
934, in compile
     handle_metrics(output_metrics)
   File "/Library/Python/2.7/site-packages/keras/engine/training.py", line 
901, in handle_metrics
    metric_fn = metrics_module.get(metric)
  File "/Library/Python/2.7/site-packages/keras/metrics.py", line 75, in get
     return deserialize(str(identifier))
  File "/Library/Python/2.7/site-packages/keras/metrics.py", line 67, in 
deserialize
    printable_module_name='metric function')
  File "/Library/Python/2.7/site-packages/keras/utils/generic_utils.py", 
line 164, in deserialize_keras_object
    ':' + function_name)
ValueError: Unknown metric function:precision
<FATAL>                         : Failed to load Keras model from file: 
model.h5
***> abort program execution
Traceback (most recent call last):
  File "classification.py", line 84, in <module>
    'H:!V:FilenameModel=model.h5:NumEpochs=20:BatchSize=32') 
#:VarTransform=D,G
TypeError: none of the 3 overloaded methods succeeded. Full details:
  TMVA::MethodBase* TMVA::Factory::BookMethod(TMVA::DataLoader* loader, 
TString theMethodName, TString methodTitle, TString theOption = "") =>
    could not convert argument 2
  TMVA::MethodBase* TMVA::Factory::BookMethod(TMVA::DataLoader* loader, 
TMVA::Types::EMVA theMethod, TString methodTitle, TString theOption = "") =>
    FATAL error (C++ exception of type runtime_error)
  TMVA::MethodBase* TMVA::Factory::BookMethod(TMVA::DataLoader*, 
TMVA::Types::EMVA, TString, TString, TMVA::Types::EMVA, TString) =>
    takes at least 6 arguments (4 given)

Answer 1

My suggestion would be implementing your metrics in Keras callback.

Because:

1. It can achieve the same thing as metrics does.

2. It can also provide you model saving strategy.

   class Checkpoint(keras.callbacks.Callback):

   def __init__(self, test_data, filename):
       self.test_data = test_data
       self.filename = filename
   
   def on_train_begin(self, logs=None):
       self.pre = [0.]
       self.rec = [0.]
       print('Test on %s begins' % self.filename)
   
   def on_train_end(self, logs={}):
       print('Best Precison: %s' % max(self.pre))
       print('Best Recall: %s' % max(self.rec))
       return
   
   def on_epoch_end(self, epoch, logs={}):
       x, y = self.test_data
       self.pre.append(precision(x, y))
       self.rec.append(recall(x, y))
   
       # print your precision or recall as you want
       print(...)
   
       # Save your model when a better trained model was found
       if pre > max(self.pre):
           self.model.save(self.filename, overwrite=True)
           print('Higher precision found. Save as %s' % self.filename)
       return
   

after that, you can add your callback to your:

checkpoint = Checkpoint((x_test, y_test), 'precison.h5')
model.compile(loss='categorical_crossentropy', optimizer='adam', callbacks=[checkpoint])

Answer 2

From the traceback it seems that the problem occurs when you try to load the saved model:

 Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "/Library/Python/2.7/site-packages/keras/models.py", line 274, in 
load_model
    sample_weight_mode=sample_weight_mode)
...
ValueError: Unknown metric function:precision
<FATAL>                         : Failed to load Keras model from file: 
model.h5

Take a look at this issue: https://github.com/keras-team/keras/issues/10104

You need to add your custom objects when loading the model. For example:

dependencies = {
    'auc_roc': auc_roc
}

model = keras.models.load_model(self.output_directory + 'best_model.hdf5', custom_objects=dependencies)

Answer 3

I tested your code in Python 3.6.5, TensorFlow==1.9 and Keras==2.2.2 and it worked. I think the error could be due to Python 2 usage.

import numpy as np
import tensorflow as tf
import keras
import keras.backend as K
from keras.layers import Dense
from keras.models import Sequential, Input, Model
from sklearn import datasets
print(f"TF version: {tf.__version__}, Keras version: {keras.__version__}\n")
# dummy dataset
iris = datasets.load_iris()
x, y_ = iris.data, iris.target
def one_hot(v): return np.eye(len(np.unique(v)))[v]
y = one_hot(y_)
# model
inp = Input(shape=(4,))
dense = Dense(8, activation='relu')(inp)
dense = Dense(16, activation='relu')(dense)
dense = Dense(3, activation='softmax')(dense)
model = Model(inputs=inp, outputs=dense)
# custom metrics
def precision(y_true, y_pred): #taken from old keras source code
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
    precision = true_positives / (predicted_positives + K.epsilon())
    return precision
def recall(y_true, y_pred): #taken from old keras source code
    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
    recall = true_positives / (possible_positives + K.epsilon())
    return recall
# training
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy', precision, recall])
model.fit(x=x, y=y, batch_size=8, epochs=15)

Output:

TF version: 1.9.0, Keras version: 2.2.2

Epoch 1/15
150/150 [==============================] - 0s 2ms/step - loss: 1.2098 - acc: 0.2600 - precision: 0.0000e+00 - recall: 0.0000e+00
Epoch 2/15
150/150 [==============================] - 0s 135us/step - loss: 1.1036 - acc: 0.4267 - precision: 0.0000e+00 - recall: 0.0000e+00
Epoch 3/15
150/150 [==============================] - 0s 132us/step - loss: 1.0391 - acc: 0.5733 - precision: 0.0000e+00 - recall: 0.0000e+00
Epoch 4/15
150/150 [==============================] - 0s 133us/step - loss: 0.9924 - acc: 0.6533 - precision: 0.0000e+00 - recall: 0.0000e+00
Epoch 5/15
150/150 [==============================] - 0s 108us/step - loss: 0.9379 - acc: 0.6667 - precision: 0.0000e+00 - recall: 0.0000e+00
Epoch 6/15
150/150 [==============================] - 0s 134us/step - loss: 0.8802 - acc: 0.6667 - precision: 0.0533 - recall: 0.0067  
Epoch 7/15
150/150 [==============================] - 0s 167us/step - loss: 0.8297 - acc: 0.7867 - precision: 0.4133 - recall: 0.0800  
Epoch 8/15
150/150 [==============================] - 0s 138us/step - loss: 0.7743 - acc: 0.8200 - precision: 0.9467 - recall: 0.3667
Epoch 9/15
150/150 [==============================] - 0s 161us/step - loss: 0.7232 - acc: 0.7467 - precision: 1.0000 - recall: 0.5667
Epoch 10/15
150/150 [==============================] - 0s 134us/step - loss: 0.6751 - acc: 0.8000 - precision: 0.9733 - recall: 0.6333
Epoch 11/15
150/150 [==============================] - 0s 134us/step - loss: 0.6310 - acc: 0.8867 - precision: 0.9924 - recall: 0.6400
Epoch 12/15
150/150 [==============================] - 0s 131us/step - loss: 0.5844 - acc: 0.8867 - precision: 0.9759 - recall: 0.6600
Epoch 13/15
150/150 [==============================] - 0s 111us/step - loss: 0.5511 - acc: 0.9133 - precision: 0.9759 - recall: 0.6533
Epoch 14/15
150/150 [==============================] - 0s 134us/step - loss: 0.5176 - acc: 0.9000 - precision: 0.9403 - recall: 0.6733
Epoch 15/15
150/150 [==============================] - 0s 134us/step - loss: 0.4899 - acc: 0.8667 - precision: 0.8877 - recall: 0.6733