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When calling a model's compile method, we can pass in metrics.
Why is tf.keras.metrics.Accuracy different than 'acc'?
For example, the following 2 calls give different results:
model.compile(optimizer=RMSprop(learning_rate=0.001),loss=tf.keras.losses.BinaryCrossentropy(),metrics=[tf.keras.metrics.Accuracy()])
vs.
model.compile(optimizer=RMSprop(learning_rate=0.001),loss=tf.keras.losses.BinaryCrossentropy(),metrics=['acc'])
I noticed that when using the callback on_epoch_end, the keys for logs dict changes for the 2 cases above. Using tf.keras.metrics.Accuracy() will result in logs with a key accuracy, but it's always 0. However, using 'acc' will result in a logs with a key acc that has values as expected.
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metrics. Accuracy(name="accuracy", dtype=None) Calculates how often predictions equal labels. This metric creates two local variables, total and count that are used to compute the frequency with which y_pred matches y_true .
The accuracy function tf. metrics. accuracy calculates how often predictions matches labels based on two local variables it creates: total and count , that are used to compute the frequency with which logits matches labels .
Class Accuracy Defined in tensorflow/python/keras/metrics.py. Calculates how often predictions matches labels. For example, if y_true is [1, 2, 3, 4] and y_pred is [0, 2, 3, 4] then the accuracy is 3/4 or . 75.
A metric is a function that is used to judge the performance of your model. Metric functions are similar to loss functions, except that the results from evaluating a metric are not used when training the model. Note that you may use any loss function as a metric.
Keras is a deep learning application programming interface for Python. It offers five different accuracy metrics for evaluating classifiers. This article attempts to explain these metrics at a fundamental level by exploring their components and calculations with experimentation. Logically define and calculate Accuracy — Hypothesis.
Using tf.keras.metrics.Accuracy () will result in logs with a key accuracy, but it's always 0. However, using 'acc' will result in a logs with a key acc that has values as expected. Show activity on this post. tf.keras.metrics.Accuracy used class Accuracy (MeanMetricWrapper) in metrics.py.
sometimes you want to monitor model performance by looking at charts like ROC curve or Confusion Matrix after every epoch. In Keras, metrics are passed during the compile stage as shown below. You can pass several metrics by comma separating them.
The accuracy metric computes the accuracy rate across all predictions. y_true represents the true labels while y_pred represents the predicted ones. The confusion_matrix displays a table showing the true positives, true negatives, false positives, and false negatives.
Took some digging, but I believe the difference is:
acc used def binary_accuracy(y_true, y_pred, threshold=0.5) in metrics.py under the hood
while
tf.keras.metrics.Accuracy used class Accuracy(MeanMetricWrapper) in metrics.py.
I came to this conclusion by testing & inspecting the source code for tensorflow's keras metrics.py file
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