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)
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(
d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate,
beta1=beta1).minimize(
g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
# ## 训练神经网络（Neural Network Training）
# ### 输出显示
# 使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。
# In[10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim,
image_mode):
"""
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
m_vars = tf.trainable_variables()
d_var = [var for var in m_vars if var.name.startswith("discriminator")]
g_var = [var for var in m_vars if var.name.startswith("generator")]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(d_loss,var_list=d_var)
g_opt = tf.train.AdamOptimizer(learning_rate,beta1=beta1).minimize(g_loss,var_list=g_var)
return d_opt, g_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
# ## 训练神经网络（Neural Network Training）
# ### 输出显示
# 使用该函数可以显示生成器 (Generator) 在训练过程中的当前输出，这会帮你评估 GANs 模型的训练程度。
# In[123]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
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)
