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| # Copyright 2017 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. | |
| # ============================================================================== | |
| """Tests for sequence_layers.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import numpy as np | |
| import tensorflow as tf | |
| from tensorflow.contrib import slim | |
| import model | |
| import sequence_layers | |
| def fake_net(batch_size, num_features, feature_size): | |
| return tf.convert_to_tensor( | |
| np.random.uniform(size=(batch_size, num_features, feature_size)), | |
| dtype=tf.float32) | |
| def fake_labels(batch_size, seq_length, num_char_classes): | |
| labels_np = tf.convert_to_tensor( | |
| np.random.randint( | |
| low=0, high=num_char_classes, size=(batch_size, seq_length))) | |
| return slim.one_hot_encoding(labels_np, num_classes=num_char_classes) | |
| def create_layer(layer_class, batch_size, seq_length, num_char_classes): | |
| model_params = model.ModelParams( | |
| num_char_classes=num_char_classes, | |
| seq_length=seq_length, | |
| num_views=1, | |
| null_code=num_char_classes) | |
| net = fake_net( | |
| batch_size=batch_size, num_features=seq_length * 5, feature_size=6) | |
| labels_one_hot = fake_labels(batch_size, seq_length, num_char_classes) | |
| layer_params = sequence_layers.SequenceLayerParams( | |
| num_lstm_units=10, weight_decay=0.00004, lstm_state_clip_value=10.0) | |
| return layer_class(net, labels_one_hot, model_params, layer_params) | |
| class SequenceLayersTest(tf.test.TestCase): | |
| def test_net_slice_char_logits_with_correct_shape(self): | |
| batch_size = 2 | |
| seq_length = 4 | |
| num_char_classes = 3 | |
| layer = create_layer(sequence_layers.NetSlice, batch_size, seq_length, | |
| num_char_classes) | |
| char_logits = layer.create_logits() | |
| self.assertEqual( | |
| tf.TensorShape([batch_size, seq_length, num_char_classes]), | |
| char_logits.get_shape()) | |
| def test_net_slice_with_autoregression_char_logits_with_correct_shape(self): | |
| batch_size = 2 | |
| seq_length = 4 | |
| num_char_classes = 3 | |
| layer = create_layer(sequence_layers.NetSliceWithAutoregression, | |
| batch_size, seq_length, num_char_classes) | |
| char_logits = layer.create_logits() | |
| self.assertEqual( | |
| tf.TensorShape([batch_size, seq_length, num_char_classes]), | |
| char_logits.get_shape()) | |
| def test_attention_char_logits_with_correct_shape(self): | |
| batch_size = 2 | |
| seq_length = 4 | |
| num_char_classes = 3 | |
| layer = create_layer(sequence_layers.Attention, batch_size, seq_length, | |
| num_char_classes) | |
| char_logits = layer.create_logits() | |
| self.assertEqual( | |
| tf.TensorShape([batch_size, seq_length, num_char_classes]), | |
| char_logits.get_shape()) | |
| def test_attention_with_autoregression_char_logits_with_correct_shape(self): | |
| batch_size = 2 | |
| seq_length = 4 | |
| num_char_classes = 3 | |
| layer = create_layer(sequence_layers.AttentionWithAutoregression, | |
| batch_size, seq_length, num_char_classes) | |
| char_logits = layer.create_logits() | |
| self.assertEqual( | |
| tf.TensorShape([batch_size, seq_length, num_char_classes]), | |
| char_logits.get_shape()) | |
| if __name__ == '__main__': | |
| tf.test.main() | |