通过使用 Gan 神经网络的判别器使用 Keras 自定义 Keras 损失函数

Question

GAN鉴别器

我使用下面的这段代码得到了 GAN 神经网络的鉴别器：

import tensorflow as tf
import numpy as np
from IPython.display import display, Audio

tf.reset_default_graph()
saver = tf.train.import_meta_graph('./infer/infer.meta')
graph = tf.get_default_graph()
sess = tf.InteractiveSession()
saver.restore(sess, tf.train.latest_checkpoint('model/'))

# here is z with underline, it doesn't showing ceractly in stack.
# I use random data to test this function.
_z = np.random.uniform(-1., 1., size=[5, 257])
x = graph.get_tensor_by_name('x:0')
D_z = graph.get_tensor_by_name('D_z:0')
D_z = sess.run(D_z, {x: _z})
print(D_z)

自定义 Keras 损失函数

我想创建一个函数来自定义 keras 损失函数：

# Load the graph
tf.reset_default_graph()
saver = tf.train.import_meta_graph('./infer/infer.meta')
graph = tf.get_default_graph()
sess = tf.InteractiveSession()
saver.restore(sess, tf.train.latest_checkpoint('model/'))

def gan_loss(y_true, y_pred):
    _z = y_pred
    x = graph.get_tensor_by_name('x:0')
    D_z = graph.get_tensor_by_name('D_z:0')
    D_z = sess.run(D_z, {x: _z})

    return D_z

我遇到的问题

我遇到了向我展示的问题：无法提供 tesor，您必须提供 numpy 或其他类型的数据。

类型错误：提要的值不能是 tf.Tensor 对象。 可接受的提要值包括 Python 标量、字符串、列表或 numpy ndarray。

我喜欢 Stak 中的相关问题： 在 Keras 中使用 K.eval() 将 Tensor 转换为 np.array 返回 InvalidArgumentError

Tensorflow：如何用张量提供占位符变量？

GAN 神经网络，我如何得到鉴别器

X = tf.placeholder(tf.float32, [None, 257], name='x')
D_z, h3 = discriminator(X)
D_z = tf.identity(D_z, name='D_z')

D_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='GAN/Discriminator')
# global_step = tf.train.get_or_create_global_step()
saver = tf.train.Saver(D_vars)
infer_dir = './infer/'
tf.train.write_graph(tf.get_default_graph(), infer_dir, 'infer.pbtxt')
infer_metagraph_fp = os.path.join(infer_dir, 'infer.meta')
tf.train.export_meta_graph(
    filename=infer_metagraph_fp,
    clear_devices=True,
    saver_def=saver.as_saver_def())
tf.reset_default_graph()

Answer 1

我能够使用下面的简单代码重现您的错误，我正在向feed_dict提供张量 -

重现错误的代码 -

%tensorflow_version 1.x
import tensorflow as tf
print(tf.__version__)
import numpy as np

x = tf.placeholder(tf.float32)
y = x * 42

with tf.Session() as sess:
  a = tf.constant(2)
  train_accuracy = y.eval(session=sess,feed_dict={x: a})
  print(train_accuracy)

输出 -

1.15.2
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-18-44556cb4551b> in <module>()
     10 with tf.Session() as sess:
     11   a = tf.constant(2)
---> 12   train_accuracy = y.eval(session=sess,feed_dict={x: a})
     13   print(train_accuracy)

3 frames
/tensorflow-1.15.2/python3.6/tensorflow_core/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
   1129                             'For reference, the tensor object was ' +
   1130                             str(feed_val) + ' which was passed to the '
-> 1131                             'feed with key ' + str(feed) + '.')
   1132 
   1133           subfeed_dtype = subfeed_t.dtype.as_numpy_dtype

TypeError: The value of a feed cannot be a tf.Tensor object. Acceptable feed values include Python scalars, strings, lists, numpy ndarrays, or TensorHandles. For reference, the tensor object was Tensor("Const_6:0", shape=(), dtype=int32) which was passed to the feed with key Tensor("Placeholder_9:0", dtype=float32).

当我将tensor转换为feed_dict numpy类型时，我能够修复它。 所以在你的情况下，将y_pred转换为numpy类型。

固定代码 -

%tensorflow_version 1.x
import tensorflow as tf
print(tf.__version__)
import numpy as np

x = tf.placeholder(tf.float32)
y = x * 42  

with tf.Session() as sess:
  a = tf.constant(2)
  a = np.array(a.eval())
  train_accuracy = y.eval(session=sess,feed_dict={x: b})
  print(train_accuracy)

输出 -

1.15.2
84.0

希望这能回答你的问题。 快乐学习。