实施新TFGAN模块

Question

我工作在tensorflow实施新TFGAN模块TFGAN Module

有没有人能够真正得到它的工作？我运行到经过tf.random.noise成一个简单的发电机的问题：

tfgan = tf.contrib.gan 
noise = tf.random_normal([BATCH_SIZE, 28,28]) 

def my_generator(z, out_dim=28*28, n_units=128, reuse=False, alpha=0.01):  
    with tf.variable_scope('generator', reuse=reuse): 
     # Hidden layer 
     h1 = tf.layers.dense(z, n_units, activation=None) 

     # Leaky ReLU 
     h1 = tf.maximum(h1, alpha*h1) 

     # Logits and tanh output 
     logits = tf.layers.dense(h1, out_dim, activation=None) 
     out = tf.nn.tanh(logits) 

    return out, logits 

然后tfgan电话：

# Build the generator and discriminator. 
gan_model = tfgan.gan_model(
    generator_fn=my_generator, 
    discriminator_fn=my_discriminator, 
    real_data=images, 
    generator_inputs=noise) 

Error: "tuple' object has no attribute 'dtype'"，指着我generator_inputs线。

（作为一个方面说明，我已经几乎完成了我所有的神经网络工作在keras层次的抽象，所以我知道这是一个简单的问题）

---

编辑PER评论来自kvorobiev（谢谢非常多）

排除数据生成器（基本相同，在GitHub上的帖子）的代码，

tfgan = tf.contrib.gan 
noise = tf.random_normal([28,28]) 


def unconditional_generator(z, out_dim=28*28, n_units=128, reuse=False, alpha=0.01):  
    with tf.variable_scope('generator', reuse=reuse): 
     # Hidden layer 
     h1 = tf.layers.dense(z, n_units, activation=None) 
     # Leaky ReLU 
     h1 = tf.maximum(h1, alpha*h1) 

     # Logits and tanh output 
     logits = tf.layers.dense(h1, out_dim, activation=None) 
     out = tf.nn.tanh(logits) 

     return out, logits 

def unconditional_discriminator(x, n_units=128, reuse=False, alpha=0.01): 
    with tf.variable_scope('discriminator', reuse=reuse): 
     # Hidden layer 
     h1 = tf.layers.dense(x, n_units, activation=None) 

     # Leaky ReLU 
     h1 = tf.maximum(h1, alpha*h1) 

     logits = tf.layers.dense(h1, 1, activation=None) 
     out = tf.nn.sigmoid(logits) 

     return out, logits 

# Build the generator and discriminator. 
gan_model = tfgan.gan_model(
    generator_fn= unconditional_generator, # you define 
    discriminator_fn = unconditional_discriminator, # you define 
    real_data=img_generator, 
    generator_inputs=noise) 

# Build the GAN loss. 
gan_loss = tfgan.gan_loss(
    gan_model, 
    generator_loss_fn=tfgan_losses.wasserstein_generator_loss, 
    discriminator_loss_fn=tfgan_losses.wasserstein_discriminator_loss) 

# Create the train ops, which calculate gradients and apply updates to weights. 
train_ops = tfgan.gan_train_ops(
    gan_model, 
    gan_loss, 
    generator_optimizer=tf.train.AdamOptimizer(gen_lr, 0.5), 
    discriminator_optimizer=tf.train.AdamOptimizer(dis_lr, 0.5)) 

# Run the train ops in the alternating training scheme. 
tfgan.gan_train(
    train_ops, 
    hooks=[tf.train.StopAtStepHook(num_steps=100)], 
    logdir=FLAGS.train_log_dir) 

回溯：

-------------------------------------------------------------------------- AttributeError       Traceback (most recent call last) <ipython-input-3-2c570c5257d0> in <module>() 
    37  discriminator_fn = unconditional_discriminator, # you define 
    38  real_data=img_generator, 
---> 39  generator_inputs=noise) 
    40 
    41 # Build the GAN loss. 

~/tf_1.4/lib/python3.5/site-packages/tensorflow/contrib/gan/python/train.py in gan_model(generator_fn, discriminator_fn, real_data, generator_inputs, generator_scope, discriminator_scope, check_shapes) 
    105 with variable_scope.variable_scope(discriminator_scope) as dis_scope: 
    106  discriminator_gen_outputs = discriminator_fn(generated_data, 
--> 107             generator_inputs) 
    108 with variable_scope.variable_scope(dis_scope, reuse=True): 
    109  real_data = ops.convert_to_tensor(real_data) 

<ipython-input-3-2c570c5257d0> in unconditional_discriminator(x, n_units, reuse, alpha) 
    19  with tf.variable_scope('discriminator', reuse=reuse): 
    20   # Hidden layer 
---> 21   h1 = tf.layers.dense(x, n_units, activation=None) 
    22 
    23   # Leaky ReLU 

~/tf_1.4/lib/python3.5/site-packages/tensorflow/python/layers/core.py in dense(inputs, units, activation, use_bias, kernel_initializer, bias_initializer, kernel_regularizer, bias_regularizer, activity_regularizer, kernel_constraint, bias_constraint, trainable, name, reuse) 
    245     trainable=trainable, 
    246     name=name, 
--> 247     dtype=inputs.dtype.base_dtype, 
    248     _scope=name, 
    249     _reuse=reuse) 

AttributeError: 'tuple' object has no attribute 'dtype' 

Answer 1

2点：

1）我相信你的错误是从你的鉴别器的第二个参数来。如果您使用库调用，TFGAN期望第二个参数成为您想要的任何条件（可以是无条件情况下的输入噪声，条件情况下的类，InfoGAN中的结构化噪声等）。您的定义是使用noise作为n_units，这很可能导致类型不匹配。要解决这个问题，只需要使用第二个鉴别符参数而不是n_dims。 2）我正在开源采购许多有用/说明性示例（无条件/有条件/关于MNIST的InfoGAN，关于CIFAR的分布式培训，对图像压缩的对抗性损失，图像到图像翻译，等等）。他们很快就会出现在这里：https://github.com/tensorflow/models/tree/master/research。