murphyja11
commented
3 years ago You can wrap kendalls_tau with tf.py_function:
def tf_kendalls_tau(y_true, y_pred):
kt = tf.py_function(
kendalls_tau,
(y_true, y_pred),
tf.float32
)
return kt
model.compile(optimizer="Adam", loss="mse", metrics=tf_kendalls_tau)The error is caused by calling the TensorFlow graph version of the function. When you call model.fit(), AutoGraph converts the kendall_tau function into a TensorFlow graph. We can use tf.py_function to prevent this, which allows us to represent kendall_tau in the graph using Python constructs.
While this works, I don't think this issue is resolved. Calling the AutoGraph-converted version of tfp.stats.kendalls_tau throws this error. I'm going to look into why this is the case.
Does someone with more experience already see an issue here? The AutoGraph-converted version of tfp.stats.kendalls_tau should be callable, right?
Full code that worked for me:
import tensorflow_probability as tfp
import tensorflow as tf
import numpy as np
def kendalls_tau(y_true, y_pred):
a = tf.reshape(y_true, shape=(-1,))
b = tf.reshape(y_pred, shape=(-1,))
kendall = tfp.stats.kendalls_tau(a, b)
return kendall
def tf_kendalls_tau(y_true, y_pred):
kt = tf.py_function(
kendalls_tau,
(y_true, y_pred),
tf.float32
)
return kt
inputs = tf.keras.layers.Input(shape=(3,))
outputs = tf.keras.layers.Dense(2)(inputs)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer="Adam", loss="mse", metrics=tf_kendalls_tau)
x = np.random.random((2, 3))
y = np.random.randint(0, 2, (2, 2) )
model.fit(x, y)
sorensenjs
I am trying to use TensorFlow probability as a metric in Keras. With respect to kendalls_tau, I get the following error:
How can I fix this?