Python train convolutional neural network on csv numpy error input shape

I would like to train a convolutional neural network autoencoder on a csv file. The csv file contains pixel neighborhood position of an original image of 1024×1024.
When I try to train it, I have the following error that I don’t manage to resolve.
ValueError: Input 0 of layer max_pooling2d is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: (None, 1, 1024, 1024, 16). Any idea, what I am doing wrong in my coding?

Let’s me explain my code:

My csv file has this structure:

0 0 1.875223e+01
1 0 1.875223e+01
2 0 2.637685e+01
3 0 2.637685e+01
4 0 2.637685e+01

I managed to load my dataset, extract the x, y, and value columns as NumPy arrays and extract the relevant columns as NumPy arrays

x = data[0].values
y = data[1].values
values = data[2].values

Then, I create an empty image with the correct dimensions and fill in the image with the pixel values

image = np.empty((1024, 1024))


for i, (xi, yi, value) in enumerate(zip(x, y, values)):
    image[xi.astype(int), yi.astype(int)] = value

To use this array as input to my convolutional autoencoder I reshaped it to a 4D array with dimensions

# Reshape the image array to a 4D tensor
image = image.reshape((1, image.shape[0], image.shape[1], 1))

Finally, I declare the convolutional autoencoder structure, at this stage I have the error `incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: (None, 1, 1024, 1024, 16)’

import keras
from keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model

# Define the input layer
input_layer = Input(shape=(1,image.shape[1], image.shape[2], 1))

# Define the encoder layers
x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_layer)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

# Define the decoder layers
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

# Define the autoencoder model
autoencoder = Model(input_layer, decoded)

# Compile the model
autoencoder.compile(optimizer='adam', loss='binary_crossentropy')

# Reshape the image array to a 4D tensor
image = image.reshape((1, image.shape[0], image.shape[1], 1))

# Train the model
autoencoder.fit(image, image, epochs=50, batch_size=1, shuffle=True)