Training CNN with Keras and 32FC1 matrix dataset

Good evening, my problem is that I want to train a Keras CNN that could tell me if in a image there is a sewer or not.

I have 2 datasets (one with positives images with sewer and another with no sewer) files with a 8000x60 matrix of decoded depth images, each image if 80x60 so each dataset has like 100 images.

My problem is that i dont know how to code that input to train the CNN. I have always worked with png datasets and now that type. If you have questions just ask.

Thanks in advance.

If your images are already decoded into a matrix, you can try to use method (  |  TensorFlow Core v2.8.0) to create inputs for your model. You pass a tuple into this method: the first element is decoded image matrix (could be numpy array or other array-like types), the second element is at array of integer labels (0 and 1 in your case).

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That could be nice but how do i tell () to slice my dataset each 80 lines or ¿he is gonna do it auto because there is a white line separator between matrix of images?

And also i didnt understand ““the second element is at array of integer labels (0 and 1 in your case).”” how do i say to the cnn that the first dataset for example is the 1 label (positive sewer) and the second one is 0 (no sewer)

Thank u so much for your response.

If you image data is np.array of shape=(8000, 60) and each 80 rows represent a separate image, you can do: new_data = data.reshape((100, 80, 60))
Then you create two arrays with target values (for each of your original arrays): y_1 = np.zeros(100) and y_2 = np.ones(100)
You create a dataset passing a tuple where the first element is your input data and the second element contains target values: ds =, y_1))
In your case you’ll have to create two datasets and then concatenate them and shuffle.

Thank you so much Ekaterina for your help i could have continued a lot my work with these information and i have coded this:

img_width, img_height = 80, 60

n_positives_img, n_negatives_img = 17874, 26308 

ds_negatives = ["negative_depth.txt"]

ds_positives = ["positive_depth.txt"]

arrayceros = np.zeros(n_negatives_img)

arrayunos = np.ones(n_positives_img)

arraynegativos= ds_negatives.reshape(( n_negatives_img, img_width, img_height))

arraypositivos= ds_positives.reshape((n_positives_img, img_width, img_height))

ds_negatives_target =, arrayceros))

ds_positives_target =, arrayunos))

dataset = pd.concat(ds_negatives_target, ds_positives_target)

datasetfinal = np.random.shuffle(dataset)

Im uploading right now the files to google collab to try this, do u think this is good or i have to change something, love your work.

Thanks in advance

Verified - Divvya Saxena

You should concatenate tensorflow datasets directly and then randomly shuffle the result:
ds_combined = ds1.concatenate(ds2).shuffle(n_samples)
n_samples should be total number of images in two datasets.

Thank you for your aclaration but when i run the code it gives me this error:

25 arraynegativos= ds_negatives.reshape(( n_negatives_img, img_width, img_height))
     26 arraypositivos= ds_positives.reshape((n_positives_img, img_width, img_height))

AttributeError: 'list' object has no attribute 'reshape'

So i converted my ds_negative to numpy array like this:

ds_negatives1 = np.array(ds_negatives)

But it gives me this error:

cannot reshape array of size 1 into shape (26308,80,60)

So now im a bit confused, how do i transform my dataset to be reshaped into that?

Thanks in advance.

Link to google collab script: Google Colab