實施新TFGAN模塊

Question

我工作在tensorflow實施新TFGAN模塊TFGAN Module

有沒有人能夠真正得到它的工作？我運行到經過tf.random.noise成一個簡單的發電機的問題：

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 

然後tfgan電話：

# 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線。

（作爲一個方面說明，我已經幾乎完成了我所有的神經網絡工作在keras層次的抽象，所以我知道這是一個簡單的問題）

---

編輯PER評論來自kvorobiev（謝謝非常多）

排除數據生成器（基本相同，在GitHub上的帖子）的代碼，

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點：

1）我相信你的錯誤是從你的鑑別器的第二個參數來。如果您使用庫調用，TFGAN期望第二個參數成爲您想要的任何條件（可以是無條件情況下的輸入噪聲，條件情況下的類，InfoGAN中的結構化噪聲等）。您的定義是使用noise作爲n_units，這很可能導致類型不匹配。要解決這個問題，只需要使用第二個鑑別符參數而不是n_dims。 2）我正在開源採購許多有用/說明性示例（無條件/有條件/關於MNIST的InfoGAN，關於CIFAR的分佈式培訓，對圖像壓縮的對抗性損失，圖像到圖像翻譯，等等）。他們很快就會出現在這裏：https://github.com/tensorflow/models/tree/master/research。