Method testBasic

tensorflow/compiler/tests/adam_test.py:54–95

(self)

class AdamOptimizerTest(xla_test.XLATestCase):

  def testBasic(self):
    for dtype in self.float_types:
      # TODO: test fails for float16 due to excessive precision requirements.
      if dtype in [np.float16, dtypes.bfloat16.as_numpy_dtype]:
        continue
      with self.session(), self.test_scope():
        variable_scope.get_variable_scope().set_use_resource(True)

        # Initialize variables for numpy implementation.
        m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0
        var0_np = np.array([1.0, 2.0], dtype=dtype)
        grads0_np = np.array([0.1, 0.1], dtype=dtype)
        var1_np = np.array([3.0, 4.0], dtype=dtype)
        grads1_np = np.array([0.01, 0.01], dtype=dtype)

        var0 = resource_variable_ops.ResourceVariable(var0_np)
        var1 = resource_variable_ops.ResourceVariable(var1_np)
        grads0 = array_ops.placeholder(dtype)
        grads1 = array_ops.placeholder(dtype)
        opt = adam.AdamOptimizer()
        update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
        variables.global_variables_initializer().run()

        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], self.evaluate(var0))
        self.assertAllClose([3.0, 4.0], self.evaluate(var1))

        beta1_power, beta2_power = opt._get_beta_accumulators()

        # Run 3 steps of Adam
        for t in range(1, 4):
          self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power))
          self.assertAllCloseAccordingToType(0.999**t,
                                             self.evaluate(beta2_power))
          update.run(feed_dict={grads0: grads0_np, grads1: grads1_np})

          var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0)
          var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1)

          # Validate updated params
          self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0))
          self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1))

  def testTensorLearningRate(self):
    for dtype in self.float_types: