Python hessian Examples

Example #1

File: test_hessian.py Project: LiuFang816/SALSTM_py_data

 def test_hessian_2d(self):
     with self.test_session():
         x1 = tf.Variable(tf.random_normal([3, 2], dtype=tf.float32))
         x2 = tf.Variable(tf.random_normal([2], dtype=tf.float32))
         y = tf.reduce_sum(tf.pow(x1, tf.constant(2.0))) + tf.reduce_sum(x2)
         tf.global_variables_initializer().run()
         self.assertAllEqual(hessian(y, [x1]).eval(), np.diag([2.0] * 6))
         self.assertAllEqual(
             hessian(y, [x1, x2]).eval(), np.diag([2.0] * 6 + [0.0] * 2))

Example #2

File: test_hessian.py Project: blei-lab/edward

 def test_hessian_2d(self):
   with self.test_session():
     x1 = tf.Variable(tf.random_normal([3, 2], dtype=tf.float32))
     x2 = tf.Variable(tf.random_normal([2], dtype=tf.float32))
     y = tf.reduce_sum(tf.pow(x1, tf.constant(2.0))) + tf.reduce_sum(x2)
     tf.initialize_all_variables().run()
     self.assertAllEqual(hessian(y, [x1]).eval(),
                         np.diag([2.0] * 6))
     self.assertAllEqual(hessian(y, [x1, x2]).eval(),
                         np.diag([2.0] * 6 + [0.0] * 2))

Example #3

File: test_hessian.py Project: LiuFang816/SALSTM_py_data

 def test_hessian_0d(self):
     with self.test_session():
         x1 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
         x2 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
         y = tf.pow(x1, tf.constant(2.0)) + tf.constant(2.0) * x1 * x2 + \
             tf.constant(3.0) * tf.pow(x2, tf.constant(2.0)) + \
             tf.constant(4.0) * x1 + tf.constant(5.0) * x2 + tf.constant(6.0)
         tf.global_variables_initializer().run()
         self.assertAllEqual(hessian(y, [x1]).eval(), np.array([[2.0]]))
         self.assertAllEqual(hessian(y, [x2]).eval(), np.array([[6.0]]))

Example #4

File: test_hessian.py Project: LiuFang816/SALSTM_py_data

 def test_all_finite_raises(self):
     with self.test_session():
         x1 = tf.Variable(np.nan * tf.random_normal([1], dtype=tf.float32))
         x2 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
         y = tf.pow(x1, tf.constant(2.0)) + tf.constant(2.0) * x1 * x2 + \
             tf.constant(3.0) * tf.pow(x2, tf.constant(2.0)) + \
             tf.constant(4.0) * x1 + tf.constant(5.0) * x2 + tf.constant(6.0)
         tf.global_variables_initializer().run()
         with self.assertRaisesOpError('NaN'):
             hessian(y, [x1]).eval()
         with self.assertRaisesOpError('NaN'):
             hessian(y, [x1, x2]).eval()

Example #5

File: test_hessian.py Project: blei-lab/edward

 def test_all_finite_raises(self):
   with self.test_session():
     x1 = tf.Variable(np.nan * tf.random_normal([1], dtype=tf.float32))
     x2 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
     y = tf.pow(x1, tf.constant(2.0)) + tf.constant(2.0) * x1 * x2 + \
         tf.constant(3.0) * tf.pow(x2, tf.constant(2.0)) + \
         tf.constant(4.0) * x1 + tf.constant(5.0) * x2 + tf.constant(6.0)
     tf.initialize_all_variables().run()
     with self.assertRaisesOpError('NaN'):
       hessian(y, [x1]).eval()
     with self.assertRaisesOpError('NaN'):
       hessian(y, [x1, x2]).eval()

Example #6

File: test_hessian.py Project: blei-lab/edward

 def test_hessian_0d(self):
   with self.test_session():
     x1 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
     x2 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
     y = tf.pow(x1, tf.constant(2.0)) + tf.constant(2.0) * x1 * x2 + \
         tf.constant(3.0) * tf.pow(x2, tf.constant(2.0)) + \
         tf.constant(4.0) * x1 + tf.constant(5.0) * x2 + tf.constant(6.0)
     tf.initialize_all_variables().run()
     self.assertAllEqual(hessian(y, [x1]).eval(),
                         np.array([[2.0]]))
     self.assertAllEqual(hessian(y, [x2]).eval(),
                         np.array([[6.0]]))

Example #7

File: test_hessian.py Project: LiuFang816/SALSTM_py_data

 def test_hessian_1d(self):
     with self.test_session():
         x1 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
         x2 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
         y = tf.pow(x1, tf.constant(2.0)) + tf.constant(2.0) * x1 * x2 + \
             tf.constant(3.0) * tf.pow(x2, tf.constant(2.0)) + \
             tf.constant(4.0) * x1 + tf.constant(5.0) * x2 + tf.constant(6.0)
         x3 = tf.Variable(tf.random_normal([3], dtype=tf.float32))
         z = tf.pow(x2, tf.constant(2.0)) + tf.reduce_sum(x3)
         tf.global_variables_initializer().run()
         self.assertAllEqual(
             hessian(y, [x1, x2]).eval(), np.array([[2.0, 2.0], [2.0,
                                                                 6.0]]))
         self.assertAllEqual(hessian(z, [x3]).eval(), np.zeros([3, 3]))
         self.assertAllEqual(
             hessian(z, [x2, x3]).eval(), np.diag([2.0, 0.0, 0.0, 0.0]))

Example #8

File: test_hessian.py Project: blei-lab/edward

 def test_hessian_1d(self):
   with self.test_session():
     x1 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
     x2 = tf.Variable(tf.random_normal([1], dtype=tf.float32))
     y = tf.pow(x1, tf.constant(2.0)) + tf.constant(2.0) * x1 * x2 + \
         tf.constant(3.0) * tf.pow(x2, tf.constant(2.0)) + \
         tf.constant(4.0) * x1 + tf.constant(5.0) * x2 + tf.constant(6.0)
     x3 = tf.Variable(tf.random_normal([3], dtype=tf.float32))
     z = tf.pow(x2, tf.constant(2.0)) + tf.reduce_sum(x3)
     tf.initialize_all_variables().run()
     self.assertAllEqual(hessian(y, [x1, x2]).eval(),
                         np.array([[2.0, 2.0], [2.0, 6.0]]))
     self.assertAllEqual(hessian(z, [x3]).eval(),
                         np.zeros([3, 3]))
     self.assertAllEqual(hessian(z, [x2, x3]).eval(),
                         np.diag([2.0, 0.0, 0.0, 0.0]))

Example #9

File: inferences.py Project: crack521/edward

 def finalize(self):
     get_session()
     x = self.data.sample(self.n_data) # uses mini-batch
     z, _ = self.variational.sample()
     var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                                  scope='variational')
     inv_cov = hessian(self.model.log_prob(x, z), var_list)
     print("Precision matrix:")
     print(inv_cov.eval())

Example #10

File: inferences.py Project: bradleyhb/edward

    def finalize(self):
        """Function to call after convergence.

        Computes the Hessian at the mode.
        """
        get_session()
        x = self.data.sample(self.n_data) # uses mini-batch
        z = self.variational.sample()
        var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                                     scope='variational')
        inv_cov = hessian(self.model.log_prob(x, z), var_list)
        print("Precision matrix:")
        print(inv_cov.eval())

Example #11

File: map.py Project: parulsethi/edward

  def finalize(self):
    """Function to call after convergence.

    Computes the Hessian at the mode.
    """
    # use only a batch of data to estimate hessian
    x = self.data
    z = {z: qz.value() for z, qz in six.iteritems(self.latent_vars)}
    var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                                 scope='posterior')
    inv_cov = hessian(self.model_wrapper.log_prob(x, z), var_list)
    print("Precision matrix:")
    print(inv_cov.eval())
    super(Laplace, self).finalize()

Example #12

    def finalize(self):
        """Function to call after convergence.

        Computes the Hessian at the mode.
        """
        # use only a batch of data to estimate hessian
        x = self.data
        z = self.variational.sample()
        var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                                     scope='variational')
        inv_cov = hessian(self.model.log_prob(x, z), var_list)
        print("Precision matrix:")
        print(inv_cov.eval())
        super(Laplace, self).finalize()

Example #13

File: map.py Project: blei-lab/edward

  def finalize(self):
    """Function to call after convergence.

    Computes the Hessian at the mode.
    """
    # use only a batch of data to estimate hessian
    x = self.data
    z = {z: qz.value() for z, qz in six.iteritems(self.latent_vars)}
    var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES,
                                 scope='posterior')
    inv_cov = hessian(self.model_wrapper.log_prob(x, z), var_list)
    print("Precision matrix:")
    print(inv_cov.eval())
    super(Laplace, self).finalize()

Example #14

File: test_hessian.py Project: wangxiao5791509/edward

def _test(y, xs, val_true):
    with sess.as_default():
        init = tf.initialize_all_variables()
        sess.run(init)
        val_est = hessian(y, xs).eval()
        assert np.all(val_est == val_true)

Example #15

File: test_hessian.py Project: appcoreopc/edward

def _test(y, xs, val_true):
    with sess.as_default():
        init = tf.initialize_all_variables()
        sess.run(init)
        val_est = hessian(y, xs).eval()
        assert np.all(val_est == val_true)