6a455db8f33ceefa0f8c544dc427ba6402f85588,gpflow/conditionals/mo_conditionals.py,,inducing_point_conditional,#Any#Any#Any#Any#,144

Before Change

    Kmn = covariances.Kuf(inducing_variable, kernel, Xnew)  // [M, L, N, P]
    Knn = kernel(Xnew, full=full_cov, full_output_cov=full_output_cov)  // [N, P](x N)x P  or  [N, P](x P)

    M, L, N, K = [Kmn.shape[i] for i in range(Kmn.shape.ndims)]
    Kmm = tf.reshape(Kmm, (M * L, M * L))

    if full_cov == full_output_cov:
        Kmn = tf.reshape(Kmn, (M * L, N * K))

After Change

    Kmn = covariances.Kuf(inducing_variable, kernel, Xnew)  // [M, L, N, P]
    Knn = kernel(Xnew, full=full_cov, full_output_cov=full_output_cov)  // [N, P](x N)x P  or  [N, P](x P)

    M, L, N, K = tf.unstack(tf.shape(Kmn), num=Kmn.shape.ndims, axis=0)
    Kmm = tf.reshape(Kmm, (M * L, M * L))

    if full_cov == full_output_cov:
        Kmn = tf.reshape(Kmn, (M * L, N * K))

Instances