常連

### 前提・実現したいこと

自作の損失関数でkerasによる機械学習を行いたいです。
まず行いたい機械学習について、22次元の数値から、2次元の数値を予測する回帰モデルです。
そして損失関数の内容については、出力の一つ目をA,二つ目をBとしたとき、A+B/nという式を考え、ｎの範囲243から600までの和
Σ(A+B/n)〔243..600〕
について、正解ラベルと予測結果の平均二乗誤差を出すというものです。
### 発生している問題・エラーメッセージ
損失関数を作成し、C:\Users(ユーザー名)\Anaconda3\Lib\site-packages\kerasに存在する、losses.pyのファイルに、自作の損失関数originallossを入力し、動くかどうかを確認しようとしたのですが、originallossという損失関数は存在しないというエラーが返ってきました。どのようにすればoriginallossが認識されるようになるのか知りたいです。

```
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-62-e841daab48c1> in <module>
     64 model.add(Dense(2))
     65 model.compile(loss='oriloss',
---> 66               optimizer='adam')
     67 
     68 hist1 = model.fit(X_data_std, y_data_std, batch_size=69, epochs=8249, verbose=1)

~\Anaconda3\lib\site-packages\keras\engine\training.py in compile(self, optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors, **kwargs)
    137             loss_functions = [losses.get(l) for l in loss]
    138         else:
--> 139             loss_function = losses.get(loss)
    140             loss_functions = [loss_function for _ in range(len(self.outputs))]
    141         self.loss_functions = loss_functions

~\Anaconda3\lib\site-packages\keras\losses.py in get(identifier)
    131     # Arguments
    132         identifier: None or str, name of the function.
--> 133 
    134     # Returns
    135         The loss function or None if `identifier` is None.

~\Anaconda3\lib\site-packages\keras\losses.py in deserialize(name, custom_objects)
    112 msle = MSLE = mean_squared_logarithmic_error
    113 kld = KLD = kullback_leibler_divergence
--> 114 cosine = cosine_proximity
    115 
    116 

~\Anaconda3\lib\site-packages\keras\utils\generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    163             if fn is None:
    164                 raise ValueError('Unknown ' + printable_module_name +
--> 165                                  ':' + function_name)
    166         return fn
    167     else:

ValueError: Unknown loss function:oriloss

```

### 該当のソースコード
学習用のコード
```python
# -*- coding: utf_8 -*-
import os
import time
import numpy as np
np.set_printoptions(threshold=np.inf) 
from sklearn.preprocessing import StandardScaler # 標準化の計算
from keras.models import Sequential
from keras.layers import Dense, Activation




# ---------標準化準備-----------------------
x_stdsc = StandardScaler()
y_stdsc = StandardScaler()


# ---------重み保存--------------------------
result_dir = 'result'
if not os.path.exists(result_dir):
    os.mkdir(result_dir)


# ------------CSV読み込み--------------------
data = np.loadtxt("train0.csv",delimiter=",")
# test1 = np.loadtxt("train100.csv",delimiter=",")



# ---------------xとyを割り当て-----------------
X_data = data[:,:22]
# y_data = data[:,[21,22]]
y_data = data[:,22:]
# X_test1 = test1[:,:6]
# y_test1 = test1[:,[10, 11, 16]]


# -----------------標準化-------------------
# print(y_test)
X_data_std = x_stdsc.fit_transform(X_data)
# X_test_std1 = x_stdsc.transform(X_test1)


y_data_std = y_stdsc.fit_transform(y_data)
# y_test_std1 = y_stdsc.transform(y_test1)


# ---------------モデルkeras-------------------

model = Sequential()

model.add(Dense(32, input_dim=22, init='uniform'))
model.add(Activation('sigmoid'))
model.add(Dense(213))
model.add(Activation('sigmoid'))
model.add(Dense(2))
model.compile(loss='originalloss',
              optimizer='adam')

hist1 = model.fit(X_data_std, y_data_std, batch_size=69, epochs=8249, verbose=1)

# -------------------save model-------------------
json_string = model.to_json()
open(os.path.join(result_dir, 'model.json'), 'w').write(json_string)
# -------------------save weights----------------------
model.save_weights(os.path.join(result_dir, 'weights.h5'))
```
実装しようとしている損失関数
```python
def originalloss(y_true,y_pred):
    def f(n):
        return y_pred[:,0]+y_pred[:,1]/n
    def g(n):
        return y_true[:,0]+y_true[:,1]/n
    def sigma(func, frm, to):
        result = 0; #答えの受け皿
        for i in range(frm, to):
            result += func(i)
        return result
    return K.mean(K.square(sigma(f, 273,601 )-sigma(g, 273,601), axis=-1) #22

```
現在のlosses.pyの中身
```python
"""Built-in loss functions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import six
from . import backend as K
from .utils.generic_utils import deserialize_keras_object
from .utils.generic_utils import serialize_keras_object

def originalloss(y_true,y_pred):
    def f(n):
        return y_pred[:,0]+y_pred[:,1]/n
    def g(n):
        return y_true[:,0]+y_true[:,1]/n
    def sigma(func, frm, to):
        result = 0; #答えの受け皿
        for i in range(frm, to):
            result += func(i)
        return result
    return K.mean(K.square(sigma(f, 273,601 )-sigma(g, 273,601), axis=-1) #22

def mean_squared_error(y_true, y_pred):
    return K.mean(K.square(y_pred - y_true), axis=-1)


def mean_absolute_error(y_true, y_pred):
    return K.mean(K.abs(y_pred - y_true), axis=-1)


def mean_absolute_percentage_error(y_true, y_pred):
    diff = K.abs((y_true - y_pred) / K.clip(K.abs(y_true),
                                            K.epsilon(),
                                            None))
    return 100. * K.mean(diff, axis=-1)


def mean_squared_logarithmic_error(y_true, y_pred):
    first_log = K.log(K.clip(y_pred, K.epsilon(), None) + 1.)
    second_log = K.log(K.clip(y_true, K.epsilon(), None) + 1.)
    return K.mean(K.square(first_log - second_log), axis=-1)


def squared_hinge(y_true, y_pred):
    return K.mean(K.square(K.maximum(1. - y_true * y_pred, 0.)), axis=-1)


def hinge(y_true, y_pred):
    return K.mean(K.maximum(1. - y_true * y_pred, 0.), axis=-1)


def categorical_hinge(y_true, y_pred):
    pos = K.sum(y_true * y_pred, axis=-1)
    neg = K.max((1. - y_true) * y_pred, axis=-1)
    return K.maximum(0., neg - pos + 1.)


def logcosh(y_true, y_pred):
    """Logarithm of the hyperbolic cosine of the prediction error.

    `log(cosh(x))` is approximately equal to `(x ** 2) / 2` for small `x` and
    to `abs(x) - log(2)` for large `x`. This means that 'logcosh' works mostly
    like the mean squared error, but will not be so strongly affected by the
    occasional wildly incorrect prediction.

    # Arguments
        y_true: tensor of true targets.
        y_pred: tensor of predicted targets.

    # Returns
        Tensor with one scalar loss entry per sample.
    """
    def _logcosh(x):
        return x + K.softplus(-2. * x) - K.log(2.)
    return K.mean(_logcosh(y_pred - y_true), axis=-1)


def categorical_crossentropy(y_true, y_pred):
    return K.categorical_crossentropy(y_true, y_pred)


def sparse_categorical_crossentropy(y_true, y_pred):
    return K.sparse_categorical_crossentropy(y_true, y_pred)


def binary_crossentropy(y_true, y_pred):
    return K.mean(K.binary_crossentropy(y_true, y_pred), axis=-1)


def kullback_leibler_divergence(y_true, y_pred):
    y_true = K.clip(y_true, K.epsilon(), 1)
    y_pred = K.clip(y_pred, K.epsilon(), 1)
    return K.sum(y_true * K.log(y_true / y_pred), axis=-1)


def poisson(y_true, y_pred):
    return K.mean(y_pred - y_true * K.log(y_pred + K.epsilon()), axis=-1)


def cosine_proximity(y_true, y_pred):
    y_true = K.l2_normalize(y_true, axis=-1)
    y_pred = K.l2_normalize(y_pred, axis=-1)
    return -K.sum(y_true * y_pred, axis=-1)


# Aliases.
oriloss = originalloss
mse = MSE = mean_squared_error
mae = MAE = mean_absolute_error
mape = MAPE = mean_absolute_percentage_error
msle = MSLE = mean_squared_logarithmic_error
kld = KLD = kullback_leibler_divergence
cosine = cosine_proximity


def serialize(loss):
    return serialize_keras_object(loss)


def deserialize(name, custom_objects=None):
    return deserialize_keras_object(name,
                                    module_objects=globals(),
                                    custom_objects=custom_objects,
                                    printable_module_name='loss function')


def get(identifier):
    """Get the `identifier` loss function.

    # Arguments
        identifier: None or str, name of the function.

    # Returns
        The loss function or None if `identifier` is None.

    # Raises
        ValueError if unknown identifier.
    """
    if identifier is None:
        return None
    if isinstance(identifier, six.string_types):
        identifier = str(identifier)
        return deserialize(identifier)
    if isinstance(identifier, dict):
        return deserialize(identifier)
    elif callable(identifier):
        return identifier
    else:
        raise ValueError('Could not interpret '
                         'loss function identifier:', identifier)

```
### 試したこと

mse = MSE = mean_squared_errorみたいな名前の定義が必要なのかと思いoriloss = originallossとも書いてloss=orilossともしたのですが、うまくいきませんでした。

	@@ -1 +1 @@
1	- kerasの自作損失関数を実装~~する方法につ~~いて
1	+ kerasで自作損失関数を実装したい

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