Keras中的自定义平均方向精度损失函数

Question

我想在Keras中将MDA定义为损失函数：

我在TensorFlow中有一个例子似乎有效：

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt


def mda(y, y_hat):
    c = tf.equal(tf.sign(y[1:] - y[:-1]), tf.sign(y_hat[1:] - y_hat[:-1]))
    return tf.reduce_mean(tf.cast(c, tf.float32))

y = np.array([0, 1, 2, 1, 0, 1])
y_hat = np.array([0, 1, 0, 1, 0, 1])
plt.plot(y)
plt.plot(y_hat, alpha=.6)
plt.show()

sess = tf.Session()
print(sess.run(mda(tf.constant(y), tf.constant(y_hat))))

相应的情节：

mda结果：0.6是3/5并且在这种情况下有意义，因为正确预测了5个方向中的3个。

但是，如果我尝试在Keras中实现此代码：

def mda(y_true, y_pred):
    """Compute Mean Directional Accuracy.

    https://en.wikipedia.org/wiki/Mean_Directional_Accuracy_(MDA)

    Parameters
    ----------
    y_true : tensor
    y_pred : tensor

    Returns
    -------
    mda : tensor
    """
    s = K.equal(K.sign(y_true[1:] - y_true[:-1]),
                 K.sign(y_pred[1:] - y_pred[:-1]))
    return K.mean(K.cast(s, K.floatx()))

我得到ValueError: None values not supported.，我无法解决。

日志：

ValueError                                Traceback (most recent call last)
<ipython-input-101-b8bdd1fcaea1> in <module>()
     67                         callbacks=[reduce_lr],
     68                         validation_data=(teX, teY),
---> 69                         verbose=1)
     70     print("Runtime:", time.time() - start)
     71 

/anaconda/lib/python3.6/site-packages/keras/models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
    861                               class_weight=class_weight,
    862                               sample_weight=sample_weight,
--> 863                               initial_epoch=initial_epoch)
    864 
    865     def evaluate(self, x, y, batch_size=32, verbose=1,

/anaconda/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
   1411         else:
   1412             ins = x + y + sample_weights
-> 1413         self._make_train_function()
   1414         f = self.train_function
   1415 

/anaconda/lib/python3.6/site-packages/keras/engine/training.py in _make_train_function(self)
    935                 self._collected_trainable_weights,
    936                 self.constraints,
--> 937                 self.total_loss)
    938             updates = self.updates + training_updates
    939             # Gets loss and metrics. Updates weights at each call.

/anaconda/lib/python3.6/site-packages/keras/optimizers.py in get_updates(self, params, constraints, loss)
    231         for p, g, a in zip(params, grads, accumulators):
    232             # update accumulator
--> 233             new_a = self.rho * a + (1. - self.rho) * K.square(g)
    234             self.updates.append(K.update(a, new_a))
    235             new_p = p - lr * g / (K.sqrt(new_a) + self.epsilon)

/anaconda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py in square(x)
   1369         A tensor.
   1370     """
-> 1371     return tf.square(x)
   1372 
   1373 

/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py in square(x, name)
    472           indices=x.indices, values=x_square, dense_shape=x.dense_shape)
    473     else:
--> 474       return gen_math_ops.square(x, name=name)
    475 
    476 

/anaconda/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py in square(x, name)
   2731     A `Tensor`. Has the same type as `x`.
   2732   """
-> 2733   result = _op_def_lib.apply_op("Square", x=x, name=name)
   2734   return result
   2735 

/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py in apply_op(self, op_type_name, name, **keywords)
    502             # What type does convert_to_tensor think it has?
    503             observed = ops.internal_convert_to_tensor(
--> 504                 values, as_ref=input_arg.is_ref).dtype.name
    505             prefix = ("Input '%s' of '%s' Op has type %s that does not match" %
    506                       (input_name, op_type_name, observed))

/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in internal_convert_to_tensor(value, dtype, name, as_ref, preferred_dtype)
    714 
    715         if ret is None:
--> 716           ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
    717 
    718         if ret is NotImplemented:

/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in _constant_tensor_conversion_function(v, dtype, name, as_ref)
    174                                          as_ref=False):
    175   _ = as_ref
--> 176   return constant(v, dtype=dtype, name=name)
    177 
    178 

/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/constant_op.py in constant(value, dtype, shape, name, verify_shape)
    163   tensor_value = attr_value_pb2.AttrValue()
    164   tensor_value.tensor.CopyFrom(
--> 165       tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
    166   dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
    167   const_tensor = g.create_op(

/anaconda/lib/python3.6/site-packages/tensorflow/python/framework/tensor_util.py in make_tensor_proto(values, dtype, shape, verify_shape)
    358   else:
    359     if values is None:
--> 360       raise ValueError("None values not supported.")
    361     # if dtype is provided, forces numpy array to be the type
    362     # provided if possible.

ValueError: None values not supported.

我也无法使用TensorFlow重现此错误，例如我尝试用y_hat替换其中一个None值但会引发不同的错误。

我不确定ValueError是否意味着在运行时期间生成了某种None值，或者我搞砸了Keras实现。我真的很感激任何帮助。

Answer 1

我尝试了你提供的代码，它在我的电脑上运行良好。

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import keras as K


def mda(y_true, y_pred):
    s = K.equal(K.sign(y_true[1:] - y_true[:-1]),
                 K.sign(y_pred[1:] - y_pred[:-1]))
    return K.mean(K.cast(s, K.floatx()))


y = np.array([0, 1, 2, 1, 0, 1])
y_hat = np.array([0, 1, 0, 1, 0, 1])
plt.plot(y)
plt.plot(y_hat, alpha=.6)
plt.show()

sess = tf.Session()
print(sess.run(mda(tf.constant(y), tf.constant(y_hat)))

你的Tensorflow / Keras设置是什么？

Answer 2

MAD不可微，您的梯度为None