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- #!/usr/bin/env python
- # -*- coding: utf-8 -*-
-
- import os
- import unittest
-
- os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
- import numpy as np
- import tensorlayer as tl
-
- from tests.utils import CustomTestCase
-
-
- class Layer_Merge_Test(CustomTestCase):
-
- @classmethod
- def setUpClass(cls):
- pass
-
- @classmethod
- def tearDownClass(cls):
- pass
-
- def test_concat(self):
-
- class CustomModel(tl.layers.Module):
-
- def __init__(self):
- super(CustomModel, self).__init__()
- self.dense1 = tl.layers.Dense(in_channels=20, n_units=10, act=tl.ReLU, name='relu1_1')
- self.dense2 = tl.layers.Dense(in_channels=20, n_units=10, act=tl.ReLU, name='relu2_1')
- self.concat = tl.layers.Concat(concat_dim=1, name='concat_layer')
-
- def forward(self, inputs):
- d1 = self.dense1(inputs)
- d2 = self.dense2(inputs)
- outputs = self.concat([d1, d2])
- return outputs
-
- model = CustomModel()
- model.set_train()
- inputs = tl.ops.convert_to_tensor(np.random.random([4, 20]).astype(np.float32))
- outputs = model(inputs)
- print(model)
-
- self.assertEqual(outputs.get_shape().as_list(), [4, 20])
-
- def test_elementwise(self):
-
- class CustomModel(tl.layers.Module):
-
- def __init__(self):
- super(CustomModel, self).__init__()
- self.dense1 = tl.layers.Dense(in_channels=20, n_units=10, act=tl.ReLU, name='relu1_1')
- self.dense2 = tl.layers.Dense(in_channels=20, n_units=10, act=tl.ReLU, name='relu2_1')
- self.element = tl.layers.Elementwise(combine_fn=tl.minimum, name='minimum', act=None)
-
- def forward(self, inputs):
- d1 = self.dense1(inputs)
- d2 = self.dense2(inputs)
- outputs = self.element([d1, d2])
- return outputs, d1, d2
-
- model = CustomModel()
- model.set_train()
- inputs = tl.ops.convert_to_tensor(np.random.random([4, 20]).astype(np.float32))
- outputs, d1, d2 = model(inputs)
- print(model)
-
- min = tl.ops.minimum(d1, d2)
- self.assertEqual(outputs.get_shape().as_list(), [4, 10])
- self.assertTrue(np.array_equal(min.numpy(), outputs.numpy()))
-
-
- if __name__ == '__main__':
-
- unittest.main()
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