def main():
import problem_unittests as tests
tests.test_model_inputs(model_inputs)
tests.test_discriminator(discriminator, tf)
tests.test_generator(generator, tf)
tests.test_model_loss(model_loss)
tests.test_model_opt(model_opt, tf)
logits=d_logits_real, labels=tf.ones_like(d_logits_real) * 0.9))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.zeros_like(d_logits_real)))
d_loss = d_loss_real + d_loss_fake
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
# ### 优化（Optimization）
# 部署 `model_opt` 函数实现对 GANs 的优化。使用 [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) 获取可训练的所有变量。通过变量空间名 `discriminator` 和 `generator` 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。
# In[9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
d_model_fake, d_logits_fake = discriminator(g_model,True)
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake,labels=tf.ones_like(d_logits_fake)))
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_real,labels=tf.ones_like(d_logits_real)*0.8))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake,labels=tf.zeros_like(d_logits_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
# ### 优化（Optimization）
# 部署 `model_opt` 函数实现对 GANs 的优化。使用 [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) 获取可训练的所有变量。通过变量空间名 `discriminator` 和 `generator` 来过滤变量。该函数应返回形如 (discriminator training operation, generator training operation) 的元组。
# In[122]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
def unit_test(self):
tests.test_model_inputs(self.model_inputs)
tests.test_discriminator(self.discriminator, tf)
tests.test_generator(self.generator, tf)
tests.test_model_loss(self.model_loss)
tests.test_model_opt(self.model_opt, tf)
