forked from TensorLayer/tensorlayer3
98 lines
3.3 KiB
Python
98 lines
3.3 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import unittest
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import tensorlayer as tl
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import numpy as np
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from tests.utils import CustomTestCase
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class Layer_Embed_Test(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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pass
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@classmethod
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def tearDownClass(cls):
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pass
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def test_onehot(self):
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input = tl.layers.Input([32], dtype=tl.int32)
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onehot = tl.layers.OneHot(depth=8, on_value=1, off_value=0, axis=-1)
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print(onehot)
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tensor = tl.layers.OneHot(depth=8)(input)
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self.assertEqual(tensor.get_shape().as_list(), [32, 8])
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def test_embed(self):
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input = tl.layers.Input([8, 100], dtype=tl.int32)
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embed = tl.layers.Embedding(vocabulary_size=1000, embedding_size=50, name='embed')
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print(embed)
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tensor = embed(input)
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self.assertEqual(tensor.get_shape().as_list(), [8, 100, 50])
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def test_avg_embed(self):
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batch_size = 8
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length = 5
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input = tl.layers.Input([batch_size, length], dtype=tl.int32)
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avgembed = tl.layers.AverageEmbedding(vocabulary_size=1000, embedding_size=50, name='avg')
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print(avgembed)
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tensor = avgembed(input)
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# print(tensor)
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self.assertEqual(tensor.get_shape().as_list(), [batch_size, 50])
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def test_word2vec_nce(self):
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batch_size = 8
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embedding_size = 50
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inputs = tl.layers.Input([batch_size], dtype=tl.int32)
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labels = tl.layers.Input([batch_size, 1], dtype=tl.int32)
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emb_net = tl.layers.Word2vecEmbedding(
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vocabulary_size=10000,
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embedding_size=embedding_size,
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num_sampled=100,
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activate_nce_loss=True, # the nce loss is activated
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nce_loss_args={},
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E_init=tl.initializers.random_uniform(minval=-1.0, maxval=1.0),
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nce_W_init=tl.initializers.truncated_normal(stddev=float(1.0 / np.sqrt(embedding_size))),
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nce_b_init=tl.initializers.constant(value=0.0),
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)
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print(emb_net)
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embed_tensor = emb_net([inputs, labels], use_nce_loss=False)
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embed_tensor, embed_nce_loss = emb_net([inputs, labels], use_nce_loss=True)
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embed_tensor, embed_nce_loss = emb_net([inputs, labels])
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self.assertEqual(embed_tensor.get_shape().as_list(), [batch_size, embedding_size])
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def test_word2vec_no_nce(self):
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batch_size = 8
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embedding_size = 50
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inputs = tl.layers.Input([batch_size], dtype=tl.int32)
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emb_net = tl.layers.Word2vecEmbedding(
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vocabulary_size=10000,
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embedding_size=embedding_size,
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num_sampled=100,
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activate_nce_loss=False,
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nce_loss_args={},
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E_init=tl.initializers.random_uniform(minval=-1.0, maxval=1.0),
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nce_W_init=tl.initializers.truncated_normal(stddev=float(1.0 / np.sqrt(embedding_size))),
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nce_b_init=tl.initializers.constant(value=0.0),
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)
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print(emb_net)
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embed_tensor = emb_net(inputs)
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embed_tensor = emb_net(inputs, use_nce_loss=False)
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try:
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embed_tensor = emb_net(inputs, use_nce_loss=True)
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except AttributeError as e:
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print(e)
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self.assertEqual(embed_tensor.get_shape().as_list(), [batch_size, embedding_size])
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if __name__ == '__main__':
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unittest.main()
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