| # Copyright 2015 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Functional tests for aggregate operations.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import copy |
| |
| import numpy as np |
| |
| from tensorflow.python.eager import context |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import ops |
| from tensorflow.python.framework import test_util |
| from tensorflow.python.keras.optimizer_v2 import adagrad |
| from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule |
| from tensorflow.python.ops import embedding_ops |
| from tensorflow.python.ops import math_ops |
| from tensorflow.python.ops import resource_variable_ops |
| from tensorflow.python.ops import variables |
| from tensorflow.python.platform import test |
| |
| |
| def adagrad_update_numpy(param, accum, g_t, lr=0.001, epsilon=1e-7): |
| accum_t = accum + g_t * g_t |
| param_t = param - lr * g_t / (np.sqrt(accum_t) + epsilon) |
| return param_t, accum_t |
| |
| |
| def sparse_adagrad_update_numpy(param, |
| accum, |
| gindexs, |
| gvalues, |
| lr=0.001, |
| epsilon=1e-7): |
| accum_t = copy.deepcopy(accum) |
| param_t = copy.deepcopy(param) |
| # first loop accumulates repeated indices if necessary. |
| for i in range(len(gindexs)): |
| gindex = gindexs[i] |
| gvalue = gvalues[i] |
| accum_t[gindex] = accum_t[gindex] + gvalue * gvalue |
| for i in range(len(gindexs)): |
| gindex = gindexs[i] |
| gvalue = gvalues[i] |
| param_t[gindex] = param_t[gindex] - lr * gvalue / ( |
| np.sqrt(accum_t[gindex]) + epsilon) |
| return param_t, accum_t |
| |
| |
| class AdagradOptimizerTest(test.TestCase): |
| |
| def doTestBasic(self, use_callable_params=False): |
| for dtype in [dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) |
| var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) |
| grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| var1 = resource_variable_ops.ResourceVariable(var1_np) |
| grads0 = constant_op.constant(grads0_np) |
| grads1 = constant_op.constant(grads1_np) |
| |
| learning_rate = lambda: 3.0 |
| if not use_callable_params: |
| learning_rate = learning_rate() |
| |
| ada_opt = adagrad.Adagrad(learning_rate) |
| |
| accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| |
| if not context.executing_eagerly(): |
| ada_update = ada_opt.apply_gradients( |
| zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| # Fetch params to validate initial values |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllClose([1.0, 2.0], v0_val) |
| self.assertAllClose([3.0, 4.0], v1_val) |
| |
| # Run 3 steps of adagrad |
| for _ in range(3): |
| if not context.executing_eagerly(): |
| self.evaluate(ada_update) |
| else: |
| ada_opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| var0_np, accum0_np = adagrad_update_numpy(var0_np, accum0_np, |
| grads0_np, 3.0) |
| var1_np, accum1_np = adagrad_update_numpy(var1_np, accum1_np, |
| grads1_np, 3.0) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| |
| @test_util.run_in_graph_and_eager_modes(reset_test=True) |
| def testBasic(self): |
| self.doTestBasic() |
| |
| def testBasicCallableParams(self): |
| with context.eager_mode(): |
| self.doTestBasic(use_callable_params=True) |
| |
| def testBasicWithLearningRateDecay(self): |
| for dtype in [dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) |
| var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) |
| grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| var1 = resource_variable_ops.ResourceVariable(var1_np) |
| grads0 = constant_op.constant(grads0_np) |
| grads1 = constant_op.constant(grads1_np) |
| |
| learning_rate = 3.0 |
| decay = 0.5 |
| |
| ada_opt = adagrad.Adagrad(learning_rate, decay=decay) |
| |
| accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| |
| if not context.executing_eagerly(): |
| ada_update = ada_opt.apply_gradients( |
| zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| # Fetch params to validate initial values |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllClose([1.0, 2.0], v0_val) |
| self.assertAllClose([3.0, 4.0], v1_val) |
| |
| # Run 3 steps of adagrad |
| for t in range(3): |
| if not context.executing_eagerly(): |
| self.evaluate(ada_update) |
| else: |
| ada_opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| lr_np = learning_rate / (1 + decay * t) |
| var0_np, accum0_np = adagrad_update_numpy(var0_np, accum0_np, |
| grads0_np, lr_np) |
| var1_np, accum1_np = adagrad_update_numpy(var1_np, accum1_np, |
| grads1_np, lr_np) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| |
| def testBasicWithLargeEpsilon(self): |
| with self.cached_session(): |
| var0_np = np.array([1.0, 2.0]) |
| var1_np = np.array([3.0, 4.0]) |
| grads0_np = np.array([0.1, 0.1]) |
| grads1_np = np.array([0.01, 0.01]) |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| var1 = resource_variable_ops.ResourceVariable(var1_np) |
| grads0 = constant_op.constant(grads0_np) |
| grads1 = constant_op.constant(grads1_np) |
| |
| learning_rate = 3.0 |
| |
| ada_opt = adagrad.Adagrad(learning_rate, epsilon=1.0) |
| |
| accum0_np = np.array([0.1, 0.1]) |
| accum1_np = np.array([0.1, 0.1]) |
| |
| if not context.executing_eagerly(): |
| ada_update = ada_opt.apply_gradients( |
| zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| # Fetch params to validate initial values |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllClose([1.0, 2.0], v0_val) |
| self.assertAllClose([3.0, 4.0], v1_val) |
| |
| # Run 3 steps of adagrad |
| for _ in range(3): |
| if not context.executing_eagerly(): |
| self.evaluate(ada_update) |
| else: |
| ada_opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| var0_np, accum0_np = adagrad_update_numpy(var0_np, accum0_np, grads0_np, |
| 3.0, 1.0) |
| var1_np, accum1_np = adagrad_update_numpy(var1_np, accum1_np, grads1_np, |
| 3.0, 1.0) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| |
| def testBasicWithLearningRateInverseTimeDecay(self): |
| for dtype in [dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) |
| var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) |
| grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| var1 = resource_variable_ops.ResourceVariable(var1_np) |
| grads0 = constant_op.constant(grads0_np) |
| grads1 = constant_op.constant(grads1_np) |
| |
| learning_rate = 3.0 |
| decay = 0.5 |
| lr_schedule = learning_rate_schedule.InverseTimeDecay( |
| learning_rate, decay_steps=1.0, decay_rate=decay) |
| |
| ada_opt = adagrad.Adagrad(lr_schedule) |
| |
| accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| |
| if not context.executing_eagerly(): |
| ada_update = ada_opt.apply_gradients( |
| zip([grads0, grads1], [var0, var1])) |
| self.evaluate(variables.global_variables_initializer()) |
| |
| # Fetch params to validate initial values |
| v0_val, v1_val = self.evaluate([var0, var1]) |
| self.assertAllClose([1.0, 2.0], v0_val) |
| self.assertAllClose([3.0, 4.0], v1_val) |
| |
| # Run 3 steps of adagrad |
| for t in range(3): |
| if not context.executing_eagerly(): |
| self.evaluate(ada_update) |
| else: |
| ada_opt.apply_gradients(zip([grads0, grads1], [var0, var1])) |
| lr_np = learning_rate / (1 + decay * t) |
| var0_np, accum0_np = adagrad_update_numpy(var0_np, accum0_np, |
| grads0_np, lr_np) |
| var1_np, accum1_np = adagrad_update_numpy(var1_np, accum1_np, |
| grads1_np, lr_np) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| |
| @test_util.run_deprecated_v1 |
| def testMinimizeSparseResourceVariable(self): |
| for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var0 = resource_variable_ops.ResourceVariable( |
| [[1.0, 2.0], [3.0, 4.0]], dtype=dtype) |
| x = constant_op.constant([[4.0], [5.0]], dtype=dtype) |
| |
| def loss(): |
| pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) # pylint: disable=cell-var-from-loop |
| return pred * pred |
| |
| sgd_op = adagrad.Adagrad(1.0).minimize(loss, var_list=[var0]) |
| variables.global_variables_initializer().run() |
| # Fetch params to validate initial values |
| self.assertAllCloseAccordingToType( |
| [[1.0, 2.0], [3.0, 4.0]], var0.eval()) |
| # Run 1 step of sgd |
| sgd_op.run() |
| # Validate updated params |
| self.assertAllCloseAccordingToType( |
| [[0, 1], [3, 4]], var0.eval(), atol=0.01) |
| |
| @test_util.run_deprecated_v1 |
| def testTensorLearningRate(self): |
| for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) |
| var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) |
| grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| var1 = resource_variable_ops.ResourceVariable(var1_np) |
| grads0 = constant_op.constant(grads0_np) |
| grads1 = constant_op.constant(grads1_np) |
| |
| learning_rate = constant_op.constant(3.0) |
| ada_opt = adagrad.Adagrad(learning_rate) |
| ada_update = ada_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], var0.eval()) |
| self.assertAllClose([3.0, 4.0], var1.eval()) |
| accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| # Run 3 steps of adagrad |
| for _ in range(3): |
| ada_update.run() |
| var0_np, accum0_np = adagrad_update_numpy(var0_np, accum0_np, |
| grads0_np, learning_rate) |
| var1_np, accum1_np = adagrad_update_numpy(var1_np, accum1_np, |
| grads1_np, learning_rate) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| |
| @test_util.run_deprecated_v1 |
| def testSparseBasic(self): |
| for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var0_np = np.array([1.0, 1.0, 2.0], dtype=dtype.as_numpy_dtype) |
| grads0_np = np.array([0.1, 0, 0.1], dtype=dtype.as_numpy_dtype) |
| var1_np = np.array([3.0, 3.0, 4.0], dtype=dtype.as_numpy_dtype) |
| grads1_np = np.array([0.01, 0, 0.01], dtype=dtype.as_numpy_dtype) |
| |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| var1 = resource_variable_ops.ResourceVariable(var1_np) |
| grads0_np_indices = np.array([0, 2], dtype=np.int32) |
| grads0 = ops.IndexedSlices( |
| constant_op.constant(grads0_np[grads0_np_indices]), |
| constant_op.constant(grads0_np_indices), constant_op.constant([3])) |
| grads1_np_indices = np.array([0, 2], dtype=np.int32) |
| grads1 = ops.IndexedSlices( |
| constant_op.constant(grads1_np[grads1_np_indices]), |
| constant_op.constant(grads1_np_indices), constant_op.constant([3])) |
| learning_rate = 3.0 |
| ada_opt = adagrad.Adagrad(learning_rate) |
| ada_update = ada_opt.apply_gradients( |
| zip([grads0, grads1], [var0, var1])) |
| variables.global_variables_initializer().run() |
| |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0, 1.0, 2.0], var0.eval()) |
| self.assertAllClose([3.0, 3.0, 4.0], var1.eval()) |
| |
| accum0_np = np.array([0.1, 0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| accum1_np = np.array([0.1, 0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| |
| # Run 3 step of sgd |
| for _ in range(3): |
| ada_update.run() |
| |
| var0_np, accum0_np = sparse_adagrad_update_numpy( |
| var0_np, accum0_np, grads0_np_indices, |
| grads0_np[grads0_np_indices], learning_rate) |
| var1_np, accum1_np = sparse_adagrad_update_numpy( |
| var1_np, accum1_np, grads1_np_indices, |
| grads1_np[grads1_np_indices], learning_rate) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| |
| @test_util.run_deprecated_v1 |
| def testSparseSingleVarDim(self): |
| for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var0_np = np.array([1.0], dtype=dtype.as_numpy_dtype) |
| grads0_np = np.array([0.1], dtype=dtype.as_numpy_dtype) |
| |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| grads0_np_indices = np.array([0], dtype=np.int32) |
| grads0 = ops.IndexedSlices( |
| constant_op.constant(grads0_np[grads0_np_indices]), |
| constant_op.constant(grads0_np_indices), constant_op.constant([3])) |
| learning_rate = 3.0 |
| ada_opt = adagrad.Adagrad(learning_rate, epsilon=1.) |
| ada_update = ada_opt.apply_gradients(zip([grads0], [var0])) |
| variables.global_variables_initializer().run() |
| |
| # Fetch params to validate initial values |
| self.assertAllClose([1.0], var0.eval()) |
| |
| accum0_np = np.array([0.1], dtype=dtype.as_numpy_dtype) |
| |
| # Run 3 step of sgd |
| for _ in range(3): |
| ada_update.run() |
| |
| var0_np, accum0_np = sparse_adagrad_update_numpy( |
| var0_np, |
| accum0_np, |
| grads0_np_indices, |
| grads0_np[grads0_np_indices], |
| learning_rate, |
| epsilon=1.) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| |
| @test_util.run_deprecated_v1 |
| def testSparseRepeatedIndices(self): |
| for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var_np = np.array([[1.0], [2.0]], dtype=dtype.as_numpy_dtype) |
| |
| repeated_index_update_var = resource_variable_ops.ResourceVariable( |
| var_np, dtype=dtype) |
| aggregated_update_var = resource_variable_ops.ResourceVariable( |
| var_np, dtype=dtype) |
| grad_repeated_index = ops.IndexedSlices( |
| constant_op.constant( |
| [0.1, 0.1], shape=[2, 1], dtype=dtype), |
| constant_op.constant([1, 1]), |
| constant_op.constant([2, 1])) |
| grad_aggregated = ops.IndexedSlices( |
| constant_op.constant( |
| [0.2], shape=[1, 1], dtype=dtype), |
| constant_op.constant([1]), |
| constant_op.constant([2, 1])) |
| repeated_update = adagrad.Adagrad(3.0).apply_gradients( |
| [(grad_repeated_index, repeated_index_update_var)]) |
| aggregated_update = adagrad.Adagrad(3.0).apply_gradients( |
| [(grad_aggregated, aggregated_update_var)]) |
| variables.global_variables_initializer().run() |
| self.assertAllClose(aggregated_update_var.eval(), |
| repeated_index_update_var.eval()) |
| for _ in range(3): |
| repeated_update.run() |
| aggregated_update.run() |
| self.assertAllClose(aggregated_update_var.eval(), |
| repeated_index_update_var.eval()) |
| |
| @test_util.run_deprecated_v1 |
| def testSparseRepeatedIndicesByEmbeddingLookUp(self): |
| for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var_repeated = resource_variable_ops.ResourceVariable( |
| [1.0, 2.0], dtype=dtype) |
| loss_repeated = lambda: math_ops.reduce_sum( # pylint: disable=g-long-lambda |
| embedding_ops.embedding_lookup(var_repeated, [0, 0])) # pylint: disable=cell-var-from-loop |
| var_aggregated = resource_variable_ops.ResourceVariable( |
| [1.0, 2.0], dtype=dtype) |
| loss_aggregated = lambda: 2 * math_ops.reduce_sum( # pylint: disable=g-long-lambda |
| embedding_ops.embedding_lookup(var_aggregated, [0])) # pylint: disable=cell-var-from-loop |
| update_op_repeated = adagrad.Adagrad(2.0).minimize( |
| loss_repeated, var_list=[var_repeated]) |
| update_op_aggregated = adagrad.Adagrad(2.0).minimize( |
| loss_aggregated, var_list=[var_aggregated]) |
| variables.global_variables_initializer().run() |
| self.assertAllCloseAccordingToType( |
| var_repeated.eval(), var_aggregated.eval()) |
| for _ in range(3): |
| update_op_repeated.run() |
| update_op_aggregated.run() |
| self.assertAllCloseAccordingToType( |
| var_repeated.eval(), var_aggregated.eval()) |
| |
| @test_util.run_deprecated_v1 |
| def testSparseStability(self): |
| for dtype in [dtypes.half]: |
| with self.cached_session(): |
| shape = [1, 6] |
| var0_np = np.array([[ |
| 0.00872496, -0.106952, 0.110467, 0.226505, -0.0147257, -0.0105945 |
| ]], |
| dtype=dtype.as_numpy_dtype) |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| grads0_np = np.array([[ |
| -5.91278e-05, 5.31673e-05, -2.5779e-06, 4.29153e-05, -8.4877e-05, |
| -9.48906e-05 |
| ]], |
| dtype=dtype.as_numpy_dtype) |
| grads0 = ops.IndexedSlices( |
| constant_op.constant(grads0_np), constant_op.constant([0]), |
| constant_op.constant(shape)) |
| ada_opt = adagrad.Adagrad(1.0) |
| ada_update = ada_opt.apply_gradients(zip([grads0], [var0])) |
| slot0 = ada_opt.get_slot(var0, "accumulator") |
| init = variables.global_variables_initializer() |
| for _ in range(100): |
| init.run() |
| ada_update.run() |
| self.assertAllCloseAccordingToType( |
| np.array([[0.1, 0.1, 0.1, 0.1, 0.1, 0.1]]), slot0.eval()) |
| self.assertAllCloseAccordingToType( |
| np.array([[ |
| 0.00891194, -0.10712013, 0.11047515, 0.22636929, -0.0144573, |
| -0.01029443 |
| ]]), var0.eval()) |
| |
| @test_util.run_deprecated_v1 |
| def testSharing(self): |
| for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: |
| with self.cached_session(): |
| var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) |
| grads0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) |
| grads1_np = np.array([0.01, 0.01], dtype=dtype.as_numpy_dtype) |
| |
| var0 = resource_variable_ops.ResourceVariable(var0_np) |
| var1 = resource_variable_ops.ResourceVariable(var1_np) |
| grads0 = constant_op.constant(grads0_np) |
| grads1 = constant_op.constant(grads1_np) |
| |
| learning_rate = 3.0 |
| ada_opt = adagrad.Adagrad(learning_rate) |
| # Apply the optimizer twice. Both applications will use |
| # the same accums. |
| ada_update1 = ada_opt.apply_gradients( |
| zip([grads0, grads1], [var0, var1])) |
| ada_update2 = ada_opt.apply_gradients( |
| zip([grads0, grads1], [var0, var1])) |
| slot0 = ada_opt.get_slot(var0, "accumulator") |
| self.assertEqual(slot0.shape, var0.shape) |
| slot1 = ada_opt.get_slot(var1, "accumulator") |
| self.assertEqual(slot1.shape, var1.shape) |
| variables.global_variables_initializer().run() |
| |
| # Fetch params to validate initial values. |
| self.assertAllClose([1.0, 2.0], var0.eval()) |
| self.assertAllClose([3.0, 4.0], var1.eval()) |
| # Mix the first and the second adagrad for 3 steps. |
| ada_update1.run() |
| ada_update2.run() |
| ada_update1.run() |
| |
| accum0_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| accum1_np = np.array([0.1, 0.1], dtype=dtype.as_numpy_dtype) |
| for _ in range(3): |
| var0_np, accum0_np = adagrad_update_numpy(var0_np, accum0_np, |
| grads0_np, learning_rate) |
| var1_np, accum1_np = adagrad_update_numpy(var1_np, accum1_np, |
| grads1_np, learning_rate) |
| self.assertAllCloseAccordingToType(var0_np, self.evaluate(var0)) |
| self.assertAllCloseAccordingToType(var1_np, self.evaluate(var1)) |
| |
| def testConstructAdagradWithLR(self): |
| opt = adagrad.Adagrad(lr=1.0) |
| opt_2 = adagrad.Adagrad(learning_rate=0.1, lr=1.0) |
| opt_3 = adagrad.Adagrad(learning_rate=0.1) |
| self.assertIsInstance(opt.lr, variables.Variable) |
| self.assertIsInstance(opt_2.lr, variables.Variable) |
| self.assertIsInstance(opt_3.lr, variables.Variable) |
| |
| self.evaluate(variables.global_variables_initializer()) |
| self.assertAllClose(self.evaluate(opt.lr), (1.0)) |
| self.assertAllClose(self.evaluate(opt_2.lr), (1.0)) |
| self.assertAllClose(self.evaluate(opt_3.lr), (0.1)) |
| |
| |
| if __name__ == "__main__": |
| test.main() |
