(self, use_resource=False)
| 55 | class AdamOptimizerTest(test.TestCase): |
| 56 | |
| 57 | def doTestSparse(self, use_resource=False): |
| 58 | for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: |
| 59 | with self.cached_session(): |
| 60 | # Initialize variables for numpy implementation. |
| 61 | m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 |
| 62 | var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) |
| 63 | grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| 64 | var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) |
| 65 | grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) |
| 66 | |
| 67 | if use_resource: |
| 68 | var0 = resource_variable_ops.ResourceVariable(var0_np) |
| 69 | var1 = resource_variable_ops.ResourceVariable(var1_np) |
| 70 | else: |
| 71 | var0 = variables.RefVariable(var0_np) |
| 72 | var1 = variables.RefVariable(var1_np) |
| 73 | grads0_np_indices = np.array([0, 1], dtype=np.int32) |
| 74 | grads0 = ops.IndexedSlices( |
| 75 | constant_op.constant(grads0_np), |
| 76 | constant_op.constant(grads0_np_indices), constant_op.constant([2])) |
| 77 | grads1_np_indices = np.array([0, 1], dtype=np.int32) |
| 78 | grads1 = ops.IndexedSlices( |
| 79 | constant_op.constant(grads1_np), |
| 80 | constant_op.constant(grads1_np_indices), constant_op.constant([2])) |
| 81 | opt = adam.AdamOptimizer() |
| 82 | update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| 83 | variables.global_variables_initializer().run() |
| 84 | |
| 85 | # Fetch params to validate initial values |
| 86 | self.assertAllClose([1.0, 2.0], self.evaluate(var0)) |
| 87 | self.assertAllClose([3.0, 4.0], self.evaluate(var1)) |
| 88 | |
| 89 | beta1_power, beta2_power = opt._get_beta_accumulators() |
| 90 | |
| 91 | # Run 3 steps of Adam |
| 92 | for t in range(1, 4): |
| 93 | self.assertAllCloseAccordingToType(0.9**t, self.evaluate(beta1_power)) |
| 94 | self.assertAllCloseAccordingToType(0.999**t, |
| 95 | self.evaluate(beta2_power)) |
| 96 | update.run() |
| 97 | |
| 98 | var0_np, m0, v0 = adam_update_numpy(var0_np, grads0_np, t, m0, v0) |
| 99 | var1_np, m1, v1 = adam_update_numpy(var1_np, grads1_np, t, m1, v1) |
| 100 | |
| 101 | # Validate updated params |
| 102 | self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| 103 | self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| 104 | |
| 105 | @test_util.run_deprecated_v1 |
| 106 | def testSparse(self): |
no test coverage detected