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233 lines
8.7 KiB
233 lines
8.7 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import contextlib
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import unittest
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import numpy as np
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import six
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import sys
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid.optimizer import SGDOptimizer
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from paddle.fluid import Conv2D, Pool2D, Linear
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from test_imperative_base import new_program_scope
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from paddle.fluid.dygraph.base import to_variable
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class Discriminator(fluid.Layer):
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def __init__(self):
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super(Discriminator, self).__init__()
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self._fc1 = Linear(1, 32, act='elu')
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self._fc2 = Linear(32, 1)
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def forward(self, inputs):
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x = self._fc1(inputs)
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x = self._fc2(x)
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return x
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class Generator(fluid.Layer):
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def __init__(self):
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super(Generator, self).__init__()
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self._fc1 = Linear(2, 64, act='elu')
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self._fc2 = Linear(64, 64, act='elu')
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self._fc3 = Linear(64, 1)
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def forward(self, inputs):
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x = self._fc1(inputs)
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x = self._fc2(x)
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x = self._fc3(x)
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return x
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class TestDygraphGAN(unittest.TestCase):
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def test_gan_float32(self):
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seed = 90
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paddle.seed(1)
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paddle.framework.random._manual_program_seed(1)
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startup = fluid.Program()
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discriminate_p = fluid.Program()
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generate_p = fluid.Program()
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scope = fluid.core.Scope()
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with new_program_scope(
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main=discriminate_p, startup=startup, scope=scope):
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discriminator = Discriminator()
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generator = Generator()
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img = fluid.layers.data(
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name="img", shape=[2, 1], append_batch_size=False)
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noise = fluid.layers.data(
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name="noise", shape=[2, 2], append_batch_size=False)
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d_real = discriminator(img)
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d_loss_real = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(
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x=d_real,
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label=fluid.layers.fill_constant(
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shape=[2, 1], dtype='float32', value=1.0)))
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d_fake = discriminator(generator(noise))
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d_loss_fake = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(
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x=d_fake,
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label=fluid.layers.fill_constant(
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shape=[2, 1], dtype='float32', value=0.0)))
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d_loss = d_loss_real + d_loss_fake
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sgd = SGDOptimizer(learning_rate=1e-3)
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sgd.minimize(d_loss)
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with new_program_scope(main=generate_p, startup=startup, scope=scope):
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discriminator = Discriminator()
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generator = Generator()
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noise = fluid.layers.data(
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name="noise", shape=[2, 2], append_batch_size=False)
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d_fake = discriminator(generator(noise))
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g_loss = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(
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x=d_fake,
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label=fluid.layers.fill_constant(
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shape=[2, 1], dtype='float32', value=1.0)))
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sgd = SGDOptimizer(learning_rate=1e-3)
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sgd.minimize(g_loss)
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exe = fluid.Executor(fluid.CPUPlace() if not core.is_compiled_with_cuda(
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) else fluid.CUDAPlace(0))
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static_params = dict()
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with fluid.scope_guard(scope):
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img = np.ones([2, 1], np.float32)
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noise = np.ones([2, 2], np.float32)
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exe.run(startup)
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static_d_loss = exe.run(discriminate_p,
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feed={'img': img,
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'noise': noise},
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fetch_list=[d_loss])[0]
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static_g_loss = exe.run(generate_p,
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feed={'noise': noise},
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fetch_list=[g_loss])[0]
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# generate_p contains all parameters needed.
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for param in generate_p.global_block().all_parameters():
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static_params[param.name] = np.array(
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scope.find_var(param.name).get_tensor())
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dy_params = dict()
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with fluid.dygraph.guard():
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paddle.seed(1)
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paddle.framework.random._manual_program_seed(1)
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discriminator = Discriminator()
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generator = Generator()
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sgd = SGDOptimizer(
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learning_rate=1e-3,
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parameter_list=(
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discriminator.parameters() + generator.parameters()))
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d_real = discriminator(to_variable(np.ones([2, 1], np.float32)))
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d_loss_real = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(
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x=d_real, label=to_variable(np.ones([2, 1], np.float32))))
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d_fake = discriminator(
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generator(to_variable(np.ones([2, 2], np.float32))))
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d_loss_fake = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(
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x=d_fake, label=to_variable(np.zeros([2, 1], np.float32))))
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d_loss = d_loss_real + d_loss_fake
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d_loss.backward()
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sgd.minimize(d_loss)
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discriminator.clear_gradients()
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generator.clear_gradients()
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d_fake = discriminator(
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generator(to_variable(np.ones([2, 2], np.float32))))
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g_loss = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(
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x=d_fake, label=to_variable(np.ones([2, 1], np.float32))))
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g_loss.backward()
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sgd.minimize(g_loss)
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for p in discriminator.parameters():
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dy_params[p.name] = p.numpy()
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for p in generator.parameters():
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dy_params[p.name] = p.numpy()
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dy_g_loss = g_loss.numpy()
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dy_d_loss = d_loss.numpy()
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dy_params2 = dict()
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with fluid.dygraph.guard():
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fluid.set_flags({'FLAGS_sort_sum_gradient': True})
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paddle.seed(1)
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paddle.framework.random._manual_program_seed(1)
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discriminator2 = Discriminator()
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generator2 = Generator()
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sgd2 = SGDOptimizer(
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learning_rate=1e-3,
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parameter_list=(
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discriminator2.parameters() + generator2.parameters()))
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d_real2 = discriminator2(to_variable(np.ones([2, 1], np.float32)))
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d_loss_real2 = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(
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x=d_real2, label=to_variable(np.ones([2, 1], np.float32))))
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d_fake2 = discriminator2(
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generator2(to_variable(np.ones([2, 2], np.float32))))
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d_loss_fake2 = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(
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x=d_fake2, label=to_variable(np.zeros([2, 1], np.float32))))
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d_loss2 = d_loss_real2 + d_loss_fake2
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d_loss2.backward()
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sgd2.minimize(d_loss2)
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discriminator2.clear_gradients()
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generator2.clear_gradients()
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d_fake2 = discriminator2(
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generator2(to_variable(np.ones([2, 2], np.float32))))
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g_loss2 = fluid.layers.reduce_mean(
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fluid.layers.sigmoid_cross_entropy_with_logits(
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x=d_fake2, label=to_variable(np.ones([2, 1], np.float32))))
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g_loss2.backward()
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sgd2.minimize(g_loss2)
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for p in discriminator2.parameters():
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dy_params2[p.name] = p.numpy()
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for p in generator.parameters():
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dy_params2[p.name] = p.numpy()
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dy_g_loss2 = g_loss2.numpy()
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dy_d_loss2 = d_loss2.numpy()
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self.assertEqual(dy_g_loss, static_g_loss)
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self.assertEqual(dy_d_loss, static_d_loss)
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for k, v in six.iteritems(dy_params):
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self.assertTrue(np.allclose(v, static_params[k]))
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self.assertEqual(dy_g_loss2, static_g_loss)
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self.assertEqual(dy_d_loss2, static_d_loss)
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for k, v in six.iteritems(dy_params2):
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self.assertTrue(np.allclose(v, static_params[k]))
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if __name__ == '__main__':
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unittest.main()
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