Reputation: 129
I have loaded a Keras model (or just created it and compiled). How do I access the list of metrics objects with which the model was compiled?
I can access the loss and the optimizer using: model.loss
and model.optimizer
.
Therefore, I assumed that I will find the list of metrics in model.metrics
, but that only returns an empty list.
Upvotes: 1
Views: 3400
Reputation: 3574
You have to run the model for at least 1 epoch for the metric names to be available:
import numpy as np
import tensorflow as tf
x = np.random.uniform(0,1, (37432,512))
y = np.random.randint(0,2, (37432,1))
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(256, activation = 'relu'))
model.add(tf.keras.layers.Dense(2, activation='softmax'))
model.compile(loss="sparse_categorical_crossentropy",
optimizer='adam',
metrics=['accuracy'])
print(model.metrics_names)
_ = model.fit(x= x, y = y, validation_split=0.2, verbose = 0)
print(model.metrics_names)
Output:
[]
['loss', 'accuracy']
For metric objects:
model.metrics[1:]
Output:
[<tensorflow.python.keras.metrics.MeanMetricWrapper at 0x7fbe702aee50>]
Upvotes: 3
Reputation: 161
You can get them before model.fit()
from the model.compiled_metrics
attribute, which is a MetricGenerator object created in model.compile()
. The memory address is the same before and after fitting so I assume it is the same object. This is working with tf 2.6.0.
>>> model.compile(metrics=[tf.keras.losses.sparse_categorical_crossentropy])
>>> model.metrics
[]
>>> model.compiled_metrics
<keras.engine.compile_utils.MetricsContainer at 0x7f701c7ed4a8>
>>> model.compiled_metrics._metrics
[<keras.metrics.SparseCategoricalCrossentropy object at 0x7facf8109b00>]
>>> model.fit(x)
...
>>> model.metrics
[<keras.metrics.Mean object at 0x7facf81099e8>,
<keras.metrics.SparseCategoricalCrossentropy object at 0x7facf8109b00>]
Upvotes: 2