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)
#x4 = tf.layers.conv2d(relu3, 512, 3, strides=2, padding='same')
#x4 = tf.layers.batch_normalization(x4, training=True)
#relu4 = tf.maximum(0.2*x4, x4)
#flat = tf.reshape(relu3, (-1,4*4*256))
flat = tf.reshape(relu3, [-1, 1])
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
# ## 生成器（Generator）
# 部署 `generator` 函数以使用 `z` 生成图像。该函数应能够重复使用神经网络中的各种变量。
# 在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 "generator" 的变量空间名来重复使用该函数中的变量。
#
# 该函数应返回所生成的 28 x 28 x `out_channel_dim` 维度图像。
# In[7]:
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:return: Discriminator logits; the output of the neural network
"""
# define feedforward behavior
out = F.leaky_relu(self.conv1(x), 0.2)
out = F.leaky_relu(self.conv2(out), 0.2)
out = F.leaky_relu(self.conv3(out), 0.2)
out = F.leaky_relu(self.conv4(out), 0.2)
out = out.view(-1, self.conv_dim * 8 * 2 * 2)
out = self.fc(out)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(Discriminator)
class Generator(nn.Module):
def __init__(self, z_size, conv_dim):
"""
Initialize the Generator Module
:param z_size: The length of the input latent vector, z
:param conv_dim: The depth of the inputs to the *last* transpose convolutional layer
"""
super(Generator, self).__init__()
# complete init function
self.conv_dim = conv_dim
self.fc = nn.Linear(z_size, conv_dim * 8 * 2 * 2)
# reshape
#4*4*256
# Flatten it
flat = tf.reshape(relu2, (-1, 7*7*128))
#flat = tf.nn.dropout(flat,0.8)
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
# ### 生成器（Generator）
# 部署 `generator` 函数以使用 `z` 生成图像。该函数应能够重复使用神经网络中的各种变量。
# 在 [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) 中使用 "generator" 的变量空间名来重复使用该函数中的变量。
#
# 该函数应返回所生成的 28 x 28 x `out_channel_dim` 维度图像。
# In[120]:
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
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)
def test_structure(self):
test_discriminator(Discriminator)
