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)