# ---------- Visualizing PCA ---------
if visualize_pca:
n_classes = 3
x = np.random.random((100, 32, 32))
y = np.random.randint(0, n_classes, size=100)
dv = DataVisualizer(x=x, y=y)
n_pca_components = 3
dv.visualize_pca(n_pca_components=n_pca_components)
# ---- Visualizing CNN Activations ----
if visualize_cnn_activations:
# Load CNN to visualize layer activations
cnn_path = 'Models/CNN/VGG16/Classic Training/Model 1'
file_manager = FileManager()
cnn = file_manager.load_file(file_path=cnn_path + '/nn.pickle')
cnn.load_model(model_path=cnn_path + '/model')
# Get just few samples data from which the CNN trained
x = cnn.data[0:2] # We are visualizing two training samples
dv = DataVisualizer(x=x)
# Visualize layers, usually we want the last one
layers = [-1]
dv.visualize_activations(model=cnn.model, layers=layers)
# ----------------------------------- DicomFileManager -----------------------------------
if test_dicom_file_manager:
# ---------------- Visualize Dicom Files in a folder ----------------
