Hello im working training to train a CNN with two datasets that I label manually with negative and positive.(80x60 depth images in each matrix)
# dimensions of our images.
img_width, img_height = 80, 60
n_positives_img, n_negatives_img = 17874, 26308
n_total_img = 44182
#Imports of datasets inside Drive
ds_negatives = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/negative_depth.txt')
ds_positives = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/positive_depth.txt')
#Labeled arrays for datasets
arrayceros = np.zeros(n_negatives_img)
arrayunos = np.ones(n_positives_img)
#Reshaping of datasets to convert separate them
arraynegativos= ds_negatives.reshape(( n_negatives_img, img_width, img_height))
arraypositivos= ds_positives.reshape((n_positives_img, img_width, img_height))
#Labeling datasets with the arrays
ds_negatives_target = tf.data.Dataset.from_tensor_slices((arraynegativos, arrayceros))
ds_positives_target = tf.data.Dataset.from_tensor_slices((arraypositivos, arrayunos))
#Concatenate 2 datasets and shuffle them
ds_concatenate = ds_negatives_target.concatenate(ds_positives_target)
datasetfinal = ds_concatenate.shuffle(n_total_img)
But when I try to separate my dataset 80/20 to validate my CNN:
trainingdataset, validatedataset = train_test_split(datasetfinal, test_size=0.2, random_state=25)
I get this error:
TypeError: Singleton array arrayshapes: ((80, 60), ()), types: (tf.float64, tf.float64)>, dtype=object) cannot be considered a valid collection.
Any ideas? Thank in advance!!!
