Python 3.x ValueError:未知初始值设定项：my_筛选器

我使用以下代码构建CNN：

def arbitrary_functionality(tensor):

    return tf.abs(tensor)

def my_filter(shape, dtype=None):
    f = np.array([
        [[[-1]], [[2]], [[-2]], [[2]], [[-1]]],
        [[[2]], [[-6]], [[8]], [[-6]], [[2]]],
        [[[-2]], [[8]], [[-12]], [[8]], [[-2]]],
        [[[2]], [[-6]], [[8]], [[-6]], [[2]]],
        [[[-1]], [[2]], [[-2]], [[2]], [[-1]]]])

    assert f.shape == shape
    return K.variable(f, dtype='float32')

input_layer = Input(shape=(256, 256, 1))
conv = Conv2D(1, [5, 5], kernel_initializer=my_filter, input_shape=(256, 256, 1), trainable=True, padding='same')(input_layer)
conv = Conv2D(8, (5, 5), padding='same', strides=1, use_bias=False)(conv)
lambda_layer = Lambda(arbitrary_functionality)(conv)
output_layer = Activation(activation='tanh')(lambda_layer)
output_layer = AveragePooling2D(pool_size= (5, 5), strides=2)(output_layer)

hidden = Dense(256)(output_layer)
hidden = LeakyReLU(alpha=0.2)(hidden)
output = Dense(2, activation='softmax')(hidden)
model = Model(inputs=input_layer, outputs=output)

# Callback for loss logging per epoch
class LossHistory(Callback):
    def on_train_begin(self, logs={}):
        self.losses = []
        self.val_losses = []

    def on_epoch_end(self, batch, logs={}):
        self.losses.append(logs.aget('loss'))
        self.val_losses.append(logs.get('val_loss'))


history = LossHistory()

tensorboard = TensorBoard (log_dir='E:/logs/trail' , histogram_freq=0, write_graph=True , write_images=False)
    adam =  keras.optimizers.Adam(lr= lrate, beta_1= 0.9, beta_2= 0.999, epsilon= 1e-08, decay= decay)
    model.compile(loss = 'binary_crossentropy', optimizer = adam, metrics = ['accuracy', 'mse'])

batch_si = 64

fitted_model = model.fit(X_train, y_train, batch_size= batch_si, callbacks=[tensorboard], epochs=epochs, verbose=1, validation_split= 0.2 , shuffle=True)

# Save Model
model.save('E:/models/trail.h5', overwrite = True)
model.save_weights('E:/models/weights_trail.hdf5', overwrite=True)
   
# Evaluate the model
scores = model.evaluate(X_test, y_test, batch_size=batch_si, verbose=1)
print("Model Accuracy: {:5.2f}%".format(100*scores[1]))

# Load and Evaluate the Model
new_model = tf.keras.models.load_model('E:/models/trail.h5', custom_objects={'tf': tf})
new_model.load_weights('E:/models/trail.hdf5')

new_model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy', 'mse'])

scores = new_model.evaluate(X_test, y_test, verbose=1)
print("Accuracy After Model Reloaded: {:5.2f}%".format(100*scores[1]))

现在的问题是，在保存并重新加载模型之前，我可以成功地评估输出。但是，当我重新加载经过训练的模型文件并尝试评估输出时，我得到了以下错误：

ValueError: Unknown initializer: my_filter

---

您必须注册自定义函数名（请参见此处：）：

new_model = tf.keras.models.load_model('E:/models/trail.h5', custom_objects={'my_filter': my_filter, 'tf': tf})