cc5e7d00a6696a8cf6e40686455e7e034fbf26ff,testing/test_kerns.py,TestArcCosine,test_nan_in_gradient,#TestArcCosine#,114

Before Change

            X_data = rng.rand(N, D)
            kernel = gpflow.kernels.ArcCosine(D)

            x_free = tf.placeholder("float64")
            kernel.make_tf_array(x_free)
            X = tf.placeholder("float64")

            with kernel.tf_mode():

After Change

            X = tf.placeholder(tf.float64)
            kernel.compile()
            grads = tf.gradients(kernel.K(X), X)
            gradients = kernel.session.run(grads, feed_dict={X: X_data})
            self.assertFalse(np.any(np.isnan(gradients)))

Instances