Finding precision and recall for MNIST dataset using TensorFlow

Question

I'm using this tutorial to learn how to train a model on the MNIST dataset here: https://www.tensorflow.org/tutorials/quickstart/beginner

Currently, the model only trains on the accuracy, but I want to figure out the F1-score of the model (starting with precision and recall first).

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10)
])
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam',
              loss=loss_fn,
              metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
Epoch 1/5
1875/1875 [==============================] - 4s 2ms/step - loss: 0.2895 - accuracy: 0.9151
Epoch 2/5
1875/1875 [==============================] - 3s 2ms/step - loss: 0.1393 - accuracy: 0.9586
...

Apparently the model also uses log-odd scores which are converted into probabilities by a softmax as well.

This is my problem, though. I tried changing the metrics in model.compile to metrics=[tf.keras.metrics.Precision()] , but I got the error ValueError: Shapes (32, 10) and (32, 1) are incompatible .

I also tried calculating the precisiona and recall through scikit-learn, but my predictions aren't lined up with the true labels.

y_pred = model.predict(x_test)
print(y_pred)
precision_score(y_test, y_pred)

Output:

[[ -4.7507367   -7.4252934   -2.8428416  ...   8.855136    -5.937388
   -2.1762638 ]
 [ -5.0433793    5.554433    12.963128   ... -18.583       -1.6025407
  -18.721622  ]
 [ -7.623428     6.3951      -1.8510209  ...   0.37932196  -1.2399373
   -6.59459   ]
 ...
---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-44-a82c4d76f544> in <module>()
      1 y_pred = model.predict(x_test)
      2 print(y_pred)
----> 3 precision_score(y_test, y_pred)

ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets

I'm thinking I might need to transform y_pred, but I'm not sure how. Or if there is a way to add precision and recall to the metrics that would be even better. How can I get the precision and recall of this model?

Answer 1

suppose you predicted using code:

predicted_result=model.predict(x_test)

the output layer has prob for digit 0 to 9, ie 10. so from the predicted result need to identify the class.

import numpy as np
class_preds = np.argmax(predicted_result, axis=-1)

now, y_test and class_preds are in classes, so can run precision_score.

from sklearn.metrics import precision_score
precision_score(y_test, class_preds,average='macro')

or

from sklearn.metrics import recall_score
recall_score(y_test, class_preds,average='macro')

even can feed this custom function to metrics:

from sklearn.metrics import precision_score
def custom_prec_score(y_true, y_pred):
    y_true=y_true.numpy()
    y_pred=y_pred.numpy()
    y_pred=np.argmax(y_pred, axis=-1)
    return precision_score(y_true, y_pred,average='macro')

model.compile(optimizer='adam',
              loss=loss_fn,run_eagerly=True,
              metrics=["accuracy",custom_prec_score])
model.fit(x_train, y_train, epochs=5)