Tensorflow: The channel dimension of the inputs should be defined

I am new to Tensorflow, and am trying to train a specific deep learning neural network. I am using Tensorflow (2.11.0) to get a deep neural network model which is described below. The data which I use is also given below:

Data:

Here is some example data. For sake of ease we can consider 10 samples in data. Here, each sample has shape: (128,128).

One can consider the below code as example training data.

x_train = np.random.rand(10, 128, 128, 1)

Normalization layer:

normalizer = tf.keras.layers.Normalization(axis=-1)
normalizer.adapt(x_train)

Build model:

def build_and_compile_model(norm):
    model = tf.keras.Sequential([
      norm,
      layers.Conv2D(128, 128, activation='relu'),
      layers.Conv2D(3, 3, activation='relu'),
      layers.Flatten(),
      layers.Dense(units=32, activation='relu'),
      layers.Dense(units=1)
    ])

    model.compile(loss='mean_absolute_error', optimizer=tf.keras.optimizers.Adam(0.001))
    
    return model

When I do

dnn_model = build_and_compile_model(normalizer)
dnn_model.summary()

I get the below error:

ValueError: The channel dimension of the inputs should be defined. The input_shape received is (None, None, None, None), where axis -1 (0-based) is the channel dimension, which found to be `None`.

What am I doing wrong here?

I have tried to get insights from this, this, this and this. But, I have not found a workable solution yet.

What should I do to remove the error and get the model to work?

I will appreciate any help.

Define the input shape directly in the normalization layer (or add an Input layer), since it cannot be inferred directly:

import numpy as np
import tensorflow as tf

x_train = np.random.rand(10, 128, 128, 1)
normalizer = tf.keras.layers.Normalization(input_shape=[128, 128, 1], axis=-1)
normalizer.adapt(x_train)


def build_and_compile_model(norm):
    model = tf.keras.Sequential([
        norm,
        tf.keras.layers.Conv2D(64, 64, activation='relu'),
        tf.keras.layers.Conv2D(3, 3, activation='relu'),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(units=32, activation='relu'),
        tf.keras.layers.Dense(units=1)
    ])

    model.compile(loss='mean_absolute_error', optimizer=tf.keras.optimizers.Adam(0.001))

    return model


dnn_model = build_and_compile_model(normalizer)
dnn_model.summary()

Also, your model does not work as it is, you are using a kernel size of 128 in your first Conv2D layer and then another Conv2D layer with a kernel size of 3 but your data has the shape (10, 128, 128, 1). I changed it to make your code executable.