[Python] Getting ValueError: Unexpected object from deserialization, expected a layer or...

here is the code from the Distance layer part because site is not allowing me to upload full code.

embedding = make_embedding()


distance layer

class L1Dist(Layer):

def __init__(self, **kwargs):
super().__init__()

def call(self, input_embedding, validation_embedding):
return tf.math.abs(input_embedding - validation_embedding)


def make_siamese_model():

input_image = Input(name='input_img', shape=(100,100,3))

validation_image = Input(name='validation_img', shape=(100,100,3))

siamese_layer = L1Dist()
siamese_layer._name = 'distance'
distances = siamese_layer(embedding(input_image), embedding(validation_image))

classifier = Dense(1, activation='sigmoid')(distances)

return Model(inputs=[input_image, validation_image], outputs=classifier, name='SiameseNetwork')


siamese_model = make_siamese_model()


binary_cross_loss = tf.losses.BinaryCrossentropy()


opt = tf.keras.optimizers.Adam(1e-4) # 0.0001


checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')
checkpoint = tf.train.Checkpoint(opt=opt, siamese_model=siamese_model)


@tf.function
def train_step(batch):

with tf.GradientTape() as tape:
X = batch[:2]
y = batch[2]

yhat = siamese_model(X, training=True)
loss = binary_cross_loss(y, yhat)
print(loss)

grad = tape.gradient(loss, siamese_model.trainable_variables)

opt.apply_gradients(zip(grad, siamese_model.trainable_variables))

return loss


from tensorflow.python.keras.metrics import Precision, Recall


def train(data, EPOCHS):
for epoch in range(1, EPOCHS+1):
print('\n Epoch {}/{}'.format(epoch, EPOCHS))
progbar = tf.keras.utils.Progbar(len(data))

r = Recall()
p = Precision()

for idx, batch in enumerate(data):
# Run train step here
loss = train_step(batch)
yhat = siamese_model(batch[:2], training=False)
r.update_state(batch[2], yhat)
p.update_state(batch[2], yhat)
progbar.update(idx+1)
print(loss.numpy(), r.result().numpy(), p.result().numpy())

if epoch % 1 == 0:
checkpoint.save(file_prefix=checkpoint_prefix)


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


EPOCHS = 2
train(train_data, EPOCHS)


siamese_model.save('siamese_model.h5')


new_model = tf.keras.models.load_model('siamese_model.h5',custom_objects={'L1Dist':L1Dist, 'binary_crossentropy':tf.losses.binary_crossentropy})


while reloading the model it is showing an ValueError: Unexpected object from deserialization, expected a layer or operation, got a <class 'main.L1Dist'>
please suggest changes in the code so that i could reload the siamsese model.

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