for testing and validation purposes, a keras based networks that returns the input image as is

identityNetwork.py

from tensorflow import keras
from tensorflow.keras import layers
import numpy as np

IMAGE_SIDE = 256

input = layers.Input(shape=(IMAGE_SIDE, IMAGE_SIDE, 3))
x = keras.layers.Dense(3,"linear" )(input)
outputs = keras.layers.Dense(3, activation="linear")(x)

model = keras.Model(input, outputs)

optimizer = keras.optimizers.Adam()
# optimizer = tf.keras.optimizers.Adagrad(learning_rate=learning_rate, initial_accumulator_value=0.1,epsilon=1e-07,name="Adagrad",)

model.compile(
     optimizer=optimizer,
     loss=keras.losses.BinaryCrossentropy(),  # metrics=[keras.metrics.BinaryAccuracy(), keras.metrics.BinaryCrossentropy(), keras.metrics.MeanSquaredError()]
)
model.summary()
og_weights = model.get_weights()
#print(f"{og_weights}")
for layer in model.layers:
    #print(f"Layer: {layer.name} {layer.get_weights()} ")
    if "input" not in layer.name:
        layer.set_weights((np.ones((3,3)) / 3.0, np.zeros((3,))))
print("------------")
print(f"{model.get_weights()}")


i = np.ones((1, 256,256,3)) * .276
print(f"i: {i}")
p = model.predict(i)

print(f"P: {p}")


print("------------")