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)
def run_tests():
import problem_unittests as t
t.test_decoding_layer(decoding_layer)
t.test_decoding_layer_infer(decoding_layer_infer)
t.test_decoding_layer_train(decoding_layer_train)
t.test_encoding_layer(encoding_layer)
t.test_model_inputs(model_inputs)
t.test_process_encoding_input(process_decoder_input)
t.test_sentence_to_seq(sentence_to_seq)
t.test_seq2seq_model(seq2seq_model)
t.test_text_to_ids(text_to_ids)
def run_all_tests():
tests.test_text_to_ids(text_to_ids)
check_tensorflow_gpu()
tests.test_model_inputs(model_inputs)
tests.test_process_encoding_input(process_decoder_input)
from imp import reload
reload(tests)
tests.test_encoding_layer(encoding_layer)
tests.test_decoding_layer_train(decoding_layer_train)
tests.test_decoding_layer_infer(decoding_layer_infer)
tests.test_decoding_layer(decoding_layer)
tests.test_seq2seq_model(seq2seq_model)
tests.test_sentence_to_seq(sentence_to_seq)
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
input_images = tf.placeholder(
tf.float32, (None, image_width, image_height, image_channels))
z_data = tf.placeholder(tf.float32, (None, z_dim))
learning_rate = tf.placeholder(tf.float32)
return input_images, z_data, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
# ### 辨别器（Discriminator）
# 部署 `discriminator` 函数创建辨别器神经网络以辨别 `images`。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 "discriminator" 的变量空间名来重复使用该函数中的变量。
#
# 该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。
# In[6]:
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
real_inputs = tf.placeholder(tf.float32,(None,image_width,image_height,image_channels),name="inputs_real")
inputs_z = tf.placeholder(tf.float32,(None,z_dim),name="inputs_z")
learning_rate = tf.placeholder(tf.float32,name="learning_rate")
return real_inputs, inputs_z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
# ### 辨别器（Discriminator）
# 部署 `discriminator` 函数创建辨别器神经网络以辨别 `images`。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 "discriminator" 的变量空间名来重复使用该函数中的变量。
#
# 该函数应返回形如 (tensor output of the discriminator, tensor logits of the discriminator) 的元组。
# In[119]:
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param image: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
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)