You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
113 lines
3.9 KiB
113 lines
3.9 KiB
# Copyright (c) 2018 PaddlePaddle 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.
|
|
|
|
import unittest
|
|
import numpy as np
|
|
from op_test import OpTest
|
|
|
|
|
|
def nce(input, weight, bias, sample_weight, labels, num_classes,
|
|
num_sample_class):
|
|
samples = []
|
|
sample_labels = []
|
|
batch_size = input.shape[0]
|
|
num_true_class = labels.shape[1]
|
|
for i in range(batch_size):
|
|
w = 1 if sample_weight is None else sample_weight[i]
|
|
for label in labels[i]:
|
|
samples.append((i, label, True, w))
|
|
sample_labels.append(label)
|
|
for num in range(num_sample_class):
|
|
samples.append((i, num, False, w))
|
|
sample_labels.append(num)
|
|
# forward bias
|
|
sample_out = np.zeros(len(samples)).astype(np.float32)
|
|
if bias is not None:
|
|
for i in range(len(samples)):
|
|
sample_out[i] = bias[samples[i][1]]
|
|
# forward weight
|
|
for i in range(len(samples)):
|
|
sample_out[i] += np.dot(input[samples[i][0]], weight[samples[i][1]])
|
|
|
|
# forward activation
|
|
sample_out = 1.0 / (1.0 + np.exp(-sample_out))
|
|
# forward cost
|
|
out = np.zeros(batch_size).astype(np.float32)
|
|
b = 1.0 / num_classes * num_sample_class
|
|
for i in range(len(samples)):
|
|
o = sample_out[i]
|
|
cost = -np.log(o / (o + b)) if samples[i][2] else -np.log(b / (o + b))
|
|
out[samples[i][0]] += cost * samples[i][3]
|
|
return (out[:, np.newaxis], np.array(sample_out).reshape(
|
|
batch_size, num_sample_class + num_true_class),
|
|
np.array(sample_labels).reshape(batch_size,
|
|
num_sample_class + num_true_class))
|
|
|
|
|
|
class TestNCE(OpTest):
|
|
def generate_data(self, dim, batch_size, num_classes, num_true_class,
|
|
num_neg_samples):
|
|
input = np.random.randn(batch_size, dim).astype(np.float32)
|
|
weight = np.random.randn(num_classes, dim).astype(np.float32)
|
|
bias = np.random.randn(num_classes).astype(np.float32)
|
|
sample_weight = np.random.randn(batch_size).astype(np.float32)
|
|
labels = np.random.randint(0, num_classes, (batch_size, num_true_class))
|
|
self.attrs = {
|
|
'num_total_classes': num_classes,
|
|
'num_neg_samples': num_neg_samples,
|
|
'custom_neg_classes': range(num_neg_samples)
|
|
}
|
|
self.inputs = {
|
|
'Input': input,
|
|
'Label': labels,
|
|
'Weight': weight,
|
|
'Bias': bias,
|
|
'SampleWeight': sample_weight
|
|
}
|
|
|
|
def set_data(self):
|
|
self.generate_data(5, 5, 4, 1, 2)
|
|
|
|
def compute(self):
|
|
out = nce(self.inputs['Input'], self.inputs['Weight'],
|
|
self.inputs['Bias'], self.inputs['SampleWeight'],
|
|
self.inputs['Label'], self.attrs['num_total_classes'],
|
|
self.attrs['num_neg_samples'])
|
|
self.outputs = {
|
|
'Cost': out[0],
|
|
'SampleLogits': out[1],
|
|
'SampleLabels': out[2]
|
|
}
|
|
|
|
def setUp(self):
|
|
self.op_type = 'nce'
|
|
self.set_data()
|
|
self.compute()
|
|
|
|
def test_check_output(self):
|
|
self.check_output()
|
|
|
|
def test_check_grad(self):
|
|
self.check_grad(
|
|
["Input", "Weight", "Bias"], "Cost", max_relative_error=0.02)
|
|
|
|
|
|
class TestNCECase1(TestNCE):
|
|
def set_data(self):
|
|
self.generate_data(10, 20, 10, 2, 5)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|