You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
#Print the shapes of the effusion and normal datasets and labels
print("Effusion dataset shape:", np.shape(dataset_effusion))
print("Effusion labels shape:", np.shape(label_effusion))
print("Normal datasets shape:", np.shape(dataset_normal))
print("Normal labels shape:", np.shape(label_normal))
#Print the shapes of the combined datasets
print("Combined datasets shape:", np.shape(dataset))
print("Combined labels shape:", np.shape(labels))
Also swap them round? Normal label is 0
Would be good to show the impact of preprocessing, show an image before and after
Show the image. Show the mean, min, max, stdev.
Then preprocess. Show the image again (looks the same). Show the mean, min, max, stdev (different).
Preprocessing keeps the information (hopefully) but makes the data easier for a neural network to work with
The text was updated successfully, but these errors were encountered:
#Print the shapes of the effusion and normal datasets and labels
print("Effusion dataset shape:", np.shape(dataset_effusion))
print("Effusion labels shape:", np.shape(label_effusion))
print("Normal datasets shape:", np.shape(dataset_normal))
print("Normal labels shape:", np.shape(label_normal))
#Print the shapes of the combined datasets
print("Combined datasets shape:", np.shape(dataset))
print("Combined labels shape:", np.shape(labels))
Also swap them round? Normal label is 0
Would be good to show the impact of preprocessing, show an image before and after
Show the image. Show the mean, min, max, stdev.
Then preprocess. Show the image again (looks the same). Show the mean, min, max, stdev (different).
Preprocessing keeps the information (hopefully) but makes the data easier for a neural network to work with
The text was updated successfully, but these errors were encountered: