The problem of adding a custom loss function in the Keras model

$$KER\; \&\; ltAS-modetypein\; theself-givensenselossLossfunctionnumberisaddedplusaskedquestions$$

Combined with the focal loss function to explain

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
import numpy as np

print(tf.__version__)
print(np.__version__)

mnist = np.load("mnist.npz")
x_train, y_train, x_test, y_test = mnist['x_train'],mnist['y_train'],mnist['x_test'],mnist['y_test']

x_train, x_test = x_train / 255.0, x_test / 255.0
y_train = np.int32(y_train)
y_test = np.int32(y_test)
# Add a channels dimension
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]
y_train = tf.one_hot(y_train,depth=10)
y_test = tf.one_hot(y_test,depth=10)
train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).shuffle(100).batch(32)

x_test.shape

def MyModel():
    inputs = tf.keras.Input(shape=(28,28,1), name='digits')
    x = tf.keras.layers.Conv2D(32, 3, activation='relu')(inputs)
    x = tf.keras.layers.Flatten()(x)
    x = tf.keras.layers.Dense(128, activation='relu')(x)
    outputs = tf.keras.layers.Dense(10,activation='softmax', name='predictions')(x)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

# #多分类的focal loss 损失函数
# class FocalLoss(tf.keras.losses.Loss):

#     def __init__(self,gamma=2.0,alpha=0.25):
#         self.gamma = gamma
#         self.alpha = alpha
#         super(FocalLoss, self).__init__()

#     def call(self,y_true,y_pred):
#         y_pred = tf.nn.softmax(y_pred,axis=-1)
#         epsilon = tf.keras.backend.epsilon()
#         y_pred = tf.clip_by_value(y_pred, epsilon, 1.0)
        
       
#         y_true = tf.cast(y_true,tf.float32)
        
#         loss = -  y_true * tf.math.pow(1 - y_pred, self.gamma) * tf.math.log(y_pred)
        
#         loss = tf.math.reduce_sum(loss,axis=1)
#         return loss
    

def FocalLoss(gamma=2.0,alpha=0.25):
    def focal_loss_fixed(y_true, y_pred):
        y_pred = tf.nn.softmax(y_pred,axis=-1)
        epsilon = tf.keras.backend.epsilon()
        y_pred = tf.clip_by_value(y_pred, epsilon, 1.0)

        y_true = tf.cast(y_true,tf.float32)

        loss = -  y_true * tf.math.pow(1 - y_pred, gamma) * tf.math.log(y_pred)

        loss = tf.math.reduce_sum(loss,axis=1)
        return  loss
    return focal_loss_fixed

model = MyModel()
model.compile(optimizer = tf.keras.optimizers.Adam(0.001), #优化器
              loss =  FocalLoss(gamma=2.0,alpha=0.25), #损失函数
              metrics = [tf.keras.metrics.CategoricalAccuracy()]
             ) #评估函数

model.fit(train_ds, epochs=5,validation_data=test_ds)