python - t-SNE for image data

Question

Can some please help me to do t-SNE on my image data?. I am using this step to know how well my image data is clustered to know the probabilities of each class. Here is my code but I am getting some not able plot I am not able to understand what's wrong with my code.

def scale_to_01_range(x, images):
    # compute the distribution range
    value_range = (np.max(images) - np.min(images))
    # move the distribution so that it starts from zero
    # by extracting the minimal value from all its values
    starts_from_zero = images - np.min(images)
    # make the distribution fit [0; 1] by dividing by its ran
    return x, starts_from_zero / value_range


def plot_tsne(all_tsne_images, all_tsne_classes):
    labels = np.unique(all_tsne_classes)
    print("labels of class", labels)
    temp_total_images = []
    temp_classes = []
    class_index = 0
    for i in all_tsne_images:
        if all_tsne_classes[class_index] == 0:
            temp_classes.append(0)
        else:
            temp_classes.append(1)
        # Resizing the images from 640*480 to 128*128 without losing any data. To improve the the speed of the model
        temp = cv2.resize(i, (128, 128))
        temp = cv2.cvtColor(temp, cv2.COLOR_BGR2GRAY)  # converting image RGB to luminance mode(grey scale)
       
        temp_total_images.append(np.array(temp))
        # Expand the dimension of the image
        class_index += 1
    print(np.asarray(temp_total_images[0]).shape)
    temp_total_images = np.expand_dims(temp_total_images, axis=3)
    temp_classes = np.asarray(temp_classes)
    temp_total_images = (np.asarray(temp_total_images, np.float32)) / 255.0

    tsne = TSNE(n_components=2)
    tsne_obj = tsne.fit_transform(temp_total_images)
    print(tsne_obj)
    tx = tsne[:, 0]
    ty = tsne[:, 1]
    tx = scale_to_01_range(tx, temp_total_images)
    ty = scale_to_01_range(ty, temp_classes)

    # initialize a matplotlib plot
    fig = plt.figure()
    ax = fig.add_subplot(111)

    # for every class, we'll add a scatter plot separately
    for label in all_tsne_classes:
        # find the samples of the current class in the data
        indices = [i for i, l in enumerate(labels) if l == label]
        # extract the coordinates of the points of this class only
        current_tx = np.take(tx, indices)
        current_ty = np.take(ty, indices)
        # convert the class color to matplotlib format
        color = np.array(all_tsne_classes[label], dtype=np.float32) / 255
        # add a scatter plot with the corresponding color and label
        ax.scatter(current_tx, current_ty, c=color, label=label)
    # build a legend using the labels we set previously
    ax.legend(loc='best')
    # finally, show the plot
    plt.show()

Please help me. Thank you Jyo

question from:https://stackoverflow.com/questions/65950163/t-sne-for-image-data

Answer 1

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