在Keras损失函数中重塑TensorFlow张量? [英] Reshape TensorFlow tensor inside Keras loss function?

问题描述

是否可以在自定义Keras损失函数内部重塑TF张量?我正在为卷积神经网络定义此自定义损失函数吗?

Is there a way to reshape a TF tensor inside of a custom Keras loss function? I'm defining this custom loss function for a convolutional neural network?

def custom_loss(x, x_hat):
    """
    Custom loss function for training background extraction networks (autoencoders)
    """

    #flatten x, x_hat before computing mean, median
    shape = x_hat.get_shape().as_list()
    batch_size = shape[0]
    image_size = np.prod(shape[1:])

    x = tf.reshape(x, [batch_size, image_size])
    x_hat = tf.reshape(x_hat, [batch_size, image_size]) 

    B0 = reduce_median(tf.transpose(x_hat))
    # I divide by sigma in the next step. So I add a small float32 to F0
    # so as to prevent sigma from becoming 0 or Nan.

    F0 = tf.abs(x_hat - B0) + 1e-10

    sigma = tf.reduce_mean(tf.sqrt(F0 / 0.5), axis=0)

    background_term = tf.reduce_mean(F0 / sigma, axis=-1)

    bce = binary_crossentropy(x, x_hat)

    loss = bce + background_term 

    return loss

除了计算标准binary_crossentropy之外，还将另外的background_term添加到损耗中.该术语激励网络预测图像接近批次的中位数.由于CNN的输出是2d，而reduce_median在1d阵列中的效果更好，因此我必须将图像重塑为1d阵列.当我尝试训练此网络时，出现错误

In addition to computing the standard binary_crossentropy an additional background_term is added into the loss. This term incentives the network to predict images close the median of a batch. Since the outputs of the CNN are 2d and reduce_median works better with 1d arrays I have to reshape the images into 1d arrays. When I try to train this network I get the error

Traceback (most recent call last):
  File "stackoverflow.py", line 162, in <module>
    autoencoder = build_conv_autoencoder(lambda_W, input_shape, num_filters, optimizer, custom_loss)
  File "stackoverflow.py", line 136, in build_conv_autoencoder
    autoencoder.compile(optimizer, loss, metrics=[mean_squared_error])
  File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 594, in compile
    **kwargs)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 667, in compile
    sample_weight, mask)
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/training.py", line 318, in weighted
    score_array = fn(y_true, y_pred)
  File "stackoverflow.py", line 26, in custom_loss
    x = tf.reshape(x, [batch_size, image_size])
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 2448, in reshape
    name=name)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 494, in apply_op
    raise err
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 491, in apply_op
    preferred_dtype=default_dtype)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 710, in internal_convert_to_tensor
    ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 176, in _constant_tensor_conversion_function
    return constant(v, dtype=dtype, name=name)
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 165, in constant
    tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 441, in make_tensor_proto
    tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 441, in <listcomp>
    tensor_proto.string_val.extend([compat.as_bytes(x) for x in proto_values])
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/compat.py", line 65, in as_bytes
    (bytes_or_text,))
TypeError: Expected binary or unicode string, got None

在实例化TensorFlow图之前，似乎Keras正在调用custom_loss.这将使batch_size无，而不是实际值.是否有适当的方法来重塑损失函数中的张量以避免该错误?您可以在此处查看完整代码.

It seems like Keras is calling custom_loss before the TensorFlow graph is instantiated. This makes batch_size None instead of the actual value. Is there a proper way to reshape tensors inside loss functions to this error is avoided? You can look at the full code here .

推荐答案

有没有适当的方法来重整张量...

Is there a proper way to reshape tensors...

如果使用Keras，则应使用K.reshape(x,shape)方法，该方法是tf.reshape(x,shape)的包装，如我们在

If you are using Keras you should use the K.reshape(x,shape) method, which is a wrapper for tf.reshape(x,shape) as we can see in the docs.

我还注意到您正在使用get_shape()来获取张量形状，在Keras上，您可以使用K.int_shape(x)来做到这一点，正如

I also notice you are using get_shape() to obtain your tensor shape, when on Keras you can do this with K.int_shape(x) as also mentioned in the docs, like this:

shape = K.int_shape(x_hat)

除了还有其他一些操作，您还可以直接调用Tensorflow导入，而不是Keras后端(例如tf.abs()，tf.reduce_mean()，tf.transpose()等).您应该考虑在keras后端中使用其对应的包装器以具有统一的符号并保证更常规的行为.另外，通过使用Keras后端，您可以使程序与Theano和Tensorflow兼容，因此您应该考虑这是一个很大的优点.

Besides that there are several other operations you do directly calling your Tensorflow import, instead of the Keras Backend (like tf.abs(), tf.reduce_mean(), tf.transpose(), etc.). You should consider using its corresponding wrappers in the keras backend to have uniform notation and guarantee a more regular behaviour. Also, by using the Keras backend you are giving your program compatibility with both Theano and Tensorflow, so it is a big plus you should consider.

此外，当使用未定义尺寸的张量时，可能会出现一些TypeError.请查看此问题，其中他们说明了如何重塑具有不确定尺寸的张量.另外，对于与Keras类似的内容，请检查其他问题在一个答案中，我解释了如何使用Tensorflow作为后端使用Keras来实现这一点.

Additionally, some TypeError may appear when working with tensors with undefined dimension(s). Please take a look at this question where they explain about reshaping tensors with undefined dimensions. Also, for its equivalent in Keras, check this other question, where in an answer I explain how to achieve that using Keras with Tensorflow as backend.

...现在有关您的代码.基本上，由于您有一些未定义的尺寸，因此可以传递值-1来使它推断形状，而不管尺寸如何(在第一个链接的问题中对此进行了说明，但也可以在文档).像这样:

...Now regarding your code. Basically, as you have some undefined dimensions, you can pass the value -1 to have it infer the shape no matter what size it could be (it is explained in the first linked question, but can also be seen in the docs). Something like:

x = tf.reshape(x, [-1, image_size])

或使用Keras后端:

Or using Keras backend:

x = K.reshape(x, [-1, image_size])

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