Spaces:
Running
Running
File size: 3,789 Bytes
0b8359d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 |
# 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()
|