我想在另一个Keras网络(B)内使用Keras网络(A)。我首先训练网络A.然后我在网络B中使用它来执行一些正则化。内部网络B我想用evaluate
或predict
来从网络A得到输出。不幸的是,我一直无法得到这个工作,因为这些函数需要一个numpy数组,而不是接收一个Tensorflow变量作为输入。keras正向传递与张量变量作为输入
这里是我如何使用自定义正则内部网络答:
class CustomRegularizer(Regularizer):
def __init__(self, model):
"""model is a keras network"""
self.model = model
def __call__(self, x):
"""Need to fix this part"""
return self.model.evaluate(x, x)
我如何计算与Keras网络与Tensorflow变量作为输入向前传球?
作为一个例子,这里就是我与numpy的:
x = np.ones((1, 64), dtype=np.float32)
model.predict(x)[:, :10]
输出:
array([[-0.0244251 , 3.31579041, 0.11801113, 0.02281714, -0.11048832,
0.13053198, 0.14661783, -0.08456061, -0.0247585 ,
0.02538805]], dtype=float32)
随着Tensorflow
x = tf.Variable(np.ones((1, 64), dtype=np.float32))
model.predict_function([x])
输出:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-92-4ed9d86cd79d> in <module>()
1 x = tf.Variable(np.ones((1, 64), dtype=np.float32))
----> 2 model.predict_function([x])
~/miniconda/envs/bolt/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
2266 updated = session.run(self.outputs + [self.updates_op],
2267 feed_dict=feed_dict,
-> 2268 **self.session_kwargs)
2269 return updated[:len(self.outputs)]
2270
~/miniconda/envs/bolt/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
776 try:
777 result = self._run(None, fetches, feed_dict, options_ptr,
--> 778 run_metadata_ptr)
779 if run_metadata:
780 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
~/miniconda/envs/bolt/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
952 np_val = subfeed_val.to_numpy_array()
953 else:
--> 954 np_val = np.asarray(subfeed_val, dtype=subfeed_dtype)
955
956 if (not is_tensor_handle_feed and
~/miniconda/envs/bolt/lib/python3.6/site-packages/numpy/core/numeric.py in asarray(a, dtype, order)
529
530 """
--> 531 return array(a, dtype, copy=False, order=order)
532
533
ValueError: setting an array element with a sequence.
我添加了上下文以了解网络如何用于我的问题。我还没有能够调整你的答案来解决我的问题。 –
对不起,但我认为还需要更多的细节来帮助你调试。我唯一能想到的是你可以尝试'cr([sess.run(x)])'和'cr = CustomRegularizer(model)'。 –