Loading Model.py

import os

os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist

physical_devices = tf.config.list_physical_devices("GPU")
tf.config.experimental.set_memory_growth(physical_devices[0], True)

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 28*28).astype("float32") / 225.0
x_test = x_test.reshape(-1, 28*28).astype("float32") / 225.0

model1 = keras.Sequential(
    [
        layers.Dense(64, activation="relu"),
        layers.Dense(10)
    ]
)

input = keras.Input(784)
x = layers.Dense(64, activation="relu")(input)
outputs = layers.Dense(10)(x)
model2 = keras.Model(inputs=input, outputs=outputs)

class MyModel(keras.Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.dense1 = layers.Dense(64, activation="relu")
        self.dense2 = layers.Dense(10)

    def call(self, input_tensor):
        x = tf.nn.relu(self.dense1, (input_tensor))
        return self.dense2(x)

model3 = MyModel()

# model = model1
# model.load_weights('saved_model/')
# model.compile(
#     loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
#     optimizer=keras.optimizers.Adam(),
#     metrics=["accuracy"],
# )

model = keras.models.load_model('complete_saved_model')

model.fit(x_train, y_train, batch_size=32, epochs=2, verbose=2)
model.evaluate(x_test, y_test, batch_size=32, verbose=2)
model.save('complete_saved_model')