forked from p32761584/tensorlayer3
91 lines
3.0 KiB
Python
91 lines
3.0 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 tensorflow as tf
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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 Test_Leaky_ReLUs(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.ni = tl.layers.Input(shape=[16, 10])
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cls.w_shape = (10, 5)
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cls.eps = 0.0
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@classmethod
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def tearDownClass(cls):
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pass
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def init_dense(self, w_init):
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return tl.layers.Dense(n_units=self.w_shape[1], in_channels=self.w_shape[0], W_init=w_init)
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def test_zeros(self):
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dense = self.init_dense(tl.initializers.zeros())
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self.assertEqual(np.sum(dense.all_weights[0].numpy() - np.zeros(shape=self.w_shape)), self.eps)
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nn = dense(self.ni)
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def test_ones(self):
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dense = self.init_dense(tl.initializers.ones())
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self.assertEqual(np.sum(dense.all_weights[0].numpy() - np.ones(shape=self.w_shape)), self.eps)
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nn = dense(self.ni)
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def test_constant(self):
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dense = self.init_dense(tl.initializers.constant(value=5.0))
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self.assertEqual(np.sum(dense.all_weights[0].numpy() - np.ones(shape=self.w_shape) * 5.0), self.eps)
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nn = dense(self.ni)
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# test with numpy arr
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arr = np.random.uniform(size=self.w_shape).astype(np.float32)
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dense = self.init_dense(tl.initializers.constant(value=arr))
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self.assertEqual(np.sum(dense.all_weights[0].numpy() - arr), self.eps)
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nn = dense(self.ni)
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def test_RandomUniform(self):
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dense = self.init_dense(tl.initializers.random_uniform(minval=-0.1, maxval=0.1, seed=1234))
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print(dense.all_weights[0].numpy())
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nn = dense(self.ni)
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def test_RandomNormal(self):
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dense = self.init_dense(tl.initializers.random_normal(mean=0.0, stddev=0.1))
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print(dense.all_weights[0].numpy())
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nn = dense(self.ni)
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def test_TruncatedNormal(self):
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dense = self.init_dense(tl.initializers.truncated_normal(mean=0.0, stddev=0.1))
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print(dense.all_weights[0].numpy())
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nn = dense(self.ni)
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def test_deconv2d_bilinear_upsampling_initializer(self):
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rescale_factor = 2
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imsize = 128
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num_channels = 3
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num_in_channels = 3
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num_out_channels = 3
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filter_shape = (5, 5, num_out_channels, num_in_channels)
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ni = tl.layers.Input(shape=(1, imsize, imsize, num_channels))
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bilinear_init = tl.initializers.deconv2d_bilinear_upsampling_initializer(shape=filter_shape)
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deconv_layer = tl.layers.DeConv2dLayer(
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shape=filter_shape, outputs_shape=(1, imsize * rescale_factor, imsize * rescale_factor, num_out_channels),
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strides=(1, rescale_factor, rescale_factor, 1), W_init=bilinear_init, padding='SAME', act=None,
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name='g/h1/decon2d'
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)
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nn = deconv_layer(ni)
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def test_config(self):
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init = tl.initializers.constant(value=5.0)
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new_init = tl.initializers.Constant.from_config(init.get_config())
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if __name__ == '__main__':
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unittest.main()
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