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@ -14,8 +14,12 @@
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from __future__ import print_function
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import unittest
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import numpy as np
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import unittest
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import paddle.fluid as fluid
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import paddle.fluid.initializer as initializer
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from op_test import OpTest
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@ -59,7 +63,7 @@ def nce(input, weight, bias, sample_weight, labels, num_classes,
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class TestNCE(OpTest):
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def generate_data(self, dim, batch_size, num_classes, num_true_class,
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num_neg_samples):
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num_neg_samples, is_sparse):
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input = np.random.randn(batch_size, dim).astype(np.float32)
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weight = np.random.randn(num_classes, dim).astype(np.float32)
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bias = np.random.randn(num_classes).astype(np.float32)
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@ -70,7 +74,8 @@ class TestNCE(OpTest):
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'num_neg_samples': num_neg_samples,
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'custom_neg_classes': list(range(num_neg_samples)),
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'seed': 0,
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'sampler': 0
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'sampler': 0,
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'is_sparse': is_sparse
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}
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self.inputs = {
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'Input': input,
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@ -81,7 +86,7 @@ class TestNCE(OpTest):
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}
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def set_data(self):
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self.generate_data(5, 5, 4, 1, 2)
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self.generate_data(5, 5, 4, 1, 2, False)
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def compute(self):
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out = nce(self.inputs['Input'], self.inputs['Weight'],
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@ -107,9 +112,110 @@ class TestNCE(OpTest):
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["Input", "Weight", "Bias"], "Cost", max_relative_error=0.02)
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class TestNCECase1(TestNCE):
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class TestNCECase1Tensor(TestNCE):
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def set_data(self):
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self.generate_data(10, 20, 10, 2, 5)
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self.generate_data(10, 20, 10, 2, 5, False)
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class TestNCECase1SelectedRows(unittest.TestCase):
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def setUp(self):
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self.base_lr = 0.0001
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self.batch_size = 8
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@staticmethod
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def get_place():
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place = fluid.core.CPUPlace()
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return place
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@staticmethod
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def get_train_data(batch_size):
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batchs = []
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for i in range(batch_size):
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input = np.random.randn(batch_size, 10).astype(np.float32)
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labels = np.random.randint(0, 20, (batch_size, 1))
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batchs.append([input, labels])
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return batchs
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def get_optimizer(self):
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# SGD optimizer
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optimizer = fluid.optimizer.SGD(learning_rate=self.base_lr)
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return optimizer
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def train_network(self, num_total_classes, num_neg_samples, sampler,
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custom_dist, is_sparse):
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input = fluid.layers.data(name="input", shape=[10], dtype="float32")
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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w_param = fluid.default_main_program().global_block().create_parameter(
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shape=[num_total_classes, 10],
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dtype='float32',
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name='nce_w',
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initializer=initializer.ConstantInitializer())
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b_param = fluid.default_main_program().global_block().create_parameter(
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shape=[num_total_classes, 1],
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dtype='float32',
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name='nce_b',
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initializer=initializer.ConstantInitializer())
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cost = fluid.layers.nce(input=input,
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label=label,
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num_total_classes=num_total_classes,
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sampler=sampler,
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custom_dist=custom_dist,
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sample_weight=None,
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param_attr='nce_w',
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bias_attr='nce_b',
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seed=1,
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num_neg_samples=num_neg_samples,
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is_sparse=is_sparse)
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avg_cost = fluid.layers.mean(cost)
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# optimizer
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optimizer = self.get_optimizer()
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optimizer.minimize(avg_cost)
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return [avg_cost, [input, label]]
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def test_input_is_selected_rows(self):
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place = self.get_place()
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exe = fluid.Executor(place)
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data = self.get_train_data(self.batch_size)
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nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32')
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rets = []
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# for dense
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dense_scope = fluid.core.Scope()
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dense_startup_program = fluid.framework.Program()
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dense_train_program = fluid.framework.Program()
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with fluid.scope_guard(dense_scope):
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with fluid.program_guard(dense_train_program,
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dense_startup_program):
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cost, feeds = self.train_network(20, 5, "custom_dist",
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nid_freq_arr.tolist(), False)
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feeder = fluid.DataFeeder(feed_list=feeds, place=place)
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exe.run(dense_startup_program)
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loss_val = exe.run(dense_train_program,
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feed=feeder.feed(data),
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fetch_list=[cost.name])
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rets.append(np.mean(loss_val))
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# for sparse
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sparse_scope = fluid.core.Scope()
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sparse_startup_program = fluid.framework.Program()
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sparse_train_program = fluid.framework.Program()
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with fluid.scope_guard(sparse_scope):
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with fluid.program_guard(sparse_train_program,
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sparse_startup_program):
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cost, feeds = self.train_network(20, 5, "custom_dist",
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nid_freq_arr.tolist(), True)
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feeder = fluid.DataFeeder(feed_list=feeds, place=place)
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exe.run(sparse_startup_program)
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loss_val = exe.run(sparse_train_program,
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feed=feeder.feed(data),
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fetch_list=[cost.name])
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rets.append(np.mean(loss_val))
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self.assertEqual(rets[0], rets[1])
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if __name__ == '__main__':
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tangwei12
6 years ago
committed by
GitHub