Note

Go to the end to download the full example code.

TSNE vs COSNE : Euclidean vs Hyperbolic#

We compare in this example two dimensionalty reduction methods: T-SNE and CO-SNE on a synthetic hierarchical toy dataset and on singlecell data. The first method computes an embedding in a 2D Euclidean space while the second one operates in the Hyperbolic Poincaré Ball model.

Designing the synthetic hierarchical dataset#

We first construct a synthetic hierarchical dataset with the following class

import numpy as np
from torchdr.utils.visu import plotGrid
from torchdr import TSNE, COSNE
from torchdr import pairwise_distances
import torch
import itertools
import urllib.request
import matplotlib.pylab as plt
from torchdr.utils import geoopt


class SyntheticDataset(torch.utils.data.Dataset):
    """
    Implementation of a synthetic dataset by hierarchical diffusion.

    Adopted from https://github.com/emilemathieu/pvae/

    Parameters
    ----------
    ball : torchdr.utils.geoopt.PoincareBall
        The Poincaré ball used for generating the dataset.
    dim : int
        Dimension of the input sample.
    depth : int
        Depth of the tree; the root corresponds to the depth 0.
    num_children : int
        Number of children of each node in the tree.
    dist_children : float
        Distance parameter for children nodes.
    sigma_sibling : float
        Noise parameter for sibling nodes.
    num_siblings : int
        Number of noisy observations obtained from the nodes of the tree.
    """

    def __init__(
        self,
        ball,
        dim,
        depth,
        num_children=2,
        dist_children=1,
        sigma_sibling=2,
        num_siblings=1,
    ):
        assert num_children == 2
        self.dim = int(dim)
        self.ball = ball
        self.root = ball.origin(self.dim)
        self.sigma_sibling = sigma_sibling
        self.depth = int(depth)
        self.dist_children = dist_children
        self.num_children = int(num_children)
        self.num_siblings = int(num_siblings)
        self.__class_counter = itertools.count()
        self.origin_data, self.origin_labels, self.data, self.labels = map(
            torch.detach, self.bst()
        )
        self.num_classes = self.origin_labels.max().item() + 1

    def __len__(self):
        """
        Return the total number of samples/nodes.

        Returns
        -------
        int
            Number of samples in the dataset.
        """
        return len(self.data)

    def __getitem__(self, idx):
        """
        Generate one sample.

        Parameters
        ----------
        idx : int
            Index of the sample to retrieve.

        Returns
        -------
        tuple
            Contains (data, labels, max_label) for the requested index.
        """
        data, labels = self.data[idx], self.labels[idx]
        return data, labels, labels.max(-1).values

    def get_children(self, parent_value, parent_label, current_depth, offspring=True):
        """
        Generate children nodes or noisy observations from a parent node.

        Parameters
        ----------
        parent_value : torch.Tensor
            1D array representing the parent node value.
        parent_label : torch.Tensor
            1D array representing the parent node label.
        current_depth : int
            Current depth in the tree.
        offspring : bool, default=True
            If True, the parent node gives birth to num_children nodes.
            If False, the parent node gives birth to num_siblings noisy observations.

        Returns
        -------
        list
            List of 2-tuples containing the value and label of each child of the
            parent node. Length depends on offspring parameter.
        """
        if offspring:
            num_children = self.num_children
            sigma = self.dist_children
        else:
            num_children = self.num_siblings
            sigma = self.sigma_sibling
        if offspring:
            direction = torch.randn_like(parent_value)
            parent_value_n = parent_value / parent_value.norm().clamp_min(1e-15)
            direction -= parent_value_n @ direction * parent_value_n
            child_value_1 = self.ball.geodesic_unit(
                torch.tensor(sigma), parent_value, direction
            )
            child_value_2 = self.ball.geodesic_unit(
                torch.tensor(sigma), parent_value, -direction
            )
            child_label_1 = parent_label.clone()
            child_label_1[current_depth] = next(self.__class_counter)
            child_label_2 = parent_label.clone()
            child_label_2[current_depth] = next(self.__class_counter)
            children = [(child_value_1, child_label_1), (child_value_2, child_label_2)]
        else:
            children = []
            for i in range(num_children):
                child_value = self.ball.random(
                    self.dim, mean=parent_value, std=sigma**0.5
                )
                child_label = parent_label.clone()
                children.append((child_value, child_label))
        return children

    def bst(self):
        """
        Generate all nodes of a level before proceeding to the next level.

        This method builds the hierarchical tree structure level by level.

        Returns
        -------
        tuple
            Contains (images, labels_visited, values_clones, labels_clones)
            representing the original data points, their labels, and the
            noisy observations with their labels.
        """
        label = -torch.ones(self.depth + 1, dtype=torch.long)
        label[0] = next(self.__class_counter)
        queue = [(self.root, label, 0)]
        visited = []
        labels_visited = []
        values_clones = []
        labels_clones = []
        while len(queue) > 0:
            current_node, current_label, current_depth = queue.pop(0)
            visited.append(current_node)
            labels_visited.append(current_label)
            if current_depth < self.depth:
                children = self.get_children(current_node, current_label, current_depth)
                for child in children:
                    queue.append((child[0], child[1], current_depth + 1))
            if current_depth <= self.depth:
                clones = self.get_children(
                    current_node, current_label, current_depth, False
                )
                for clone in clones:
                    values_clones.append(clone[0])
                    labels_clones.append(clone[1])
        length = int(
            ((self.num_children) ** (self.depth + 1) - 1) / (self.num_children - 1)
        )
        images = torch.cat([i for i in visited]).reshape(length, self.dim)
        labels_visited = torch.cat([i for i in labels_visited]).reshape(
            length, self.depth + 1
        )[:, : self.depth]
        values_clones = torch.cat([i for i in values_clones]).reshape(
            self.num_siblings * length, self.dim
        )
        labels_clones = torch.cat([i for i in labels_clones]).reshape(
            self.num_siblings * length, self.depth + 1
        )
        return images, labels_visited, values_clones, labels_clones

Generating the data#

Let us now generate some data of interest. The dimension of the input space is set to 50

ball = geoopt.PoincareBall()

dataset = SyntheticDataset(
    ball, 50, 2, num_siblings=100, sigma_sibling=0.05, dist_children=0.7
)
data_points = dataset.data
data_points = data_points - data_points.mean(axis=0)

labels = dataset.labels
colors = dataset.labels.max(-1).values

Visualization of the original similarities#

We can observe the hierarchical nature of the input data by examining the pairwaise distance matrix in the input space

dist_matrix, _ = pairwise_distances(data_points, data_points, metric="sqeuclidean")

plt.figure()
plt.imshow(dist_matrix)
plt.title("Distance matrix in the input space")
plt.show()
Distance matrix in the input space

Computing TSNE and COSNE#

We can now proceed to computing the two DR methods and visualizing the results

tsne_model = TSNE(verbose=True, max_iter=500)
out_tsne = tsne_model.fit_transform(data_points)

cosne_model = COSNE(lr=1e-1, verbose=True, gamma=0.5, lambda1=0.01, max_iter=500)
out_cosne = cosne_model.fit_transform(data_points)


fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))
axes[0].scatter(*out_tsne.T, c=colors, cmap=plt.get_cmap("rainbow"))
axes[0].set_xticks([])
axes[0].set_yticks([])
axes[0].set_title("T-SNE", fontsize=24)
plotGrid(axes[1])
axes[1].scatter(*out_cosne.T, c=colors, cmap=plt.get_cmap("rainbow"))
axes[1].axis("off")
axes[1].set_title("CO-SNE", fontsize=24)
plt.show()
T-SNE, CO-SNE
Random state is None
[TorchDR] Initializing DR model TSNE.
[TorchDR] Affinity : computing the Entropic Affinity matrix.
[TorchDR] Affinity : sparsity mode enabled, computing 90 nearest neighbors. If this step is too slow, consider reducing the dimensionality of the data or disabling sparsity.

  0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  5.27e-01 (std =  5.68e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  3.72e-01 (std =  5.93e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  2.42e-01 (std =  5.54e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  1.48e-01 (std =  4.64e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  8.68e-02 (std =  3.58e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  5.01e-02 (std =  2.60e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  5.01e-02 (std =  2.60e-02) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  2.88e-02 (std =  1.82e-02) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  1.66e-02 (std =  1.24e-02) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  9.57e-03 (std =  8.43e-03) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  5.58e-03 (std =  5.69e-03) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  3.28e-03 (std =  3.84e-03) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  1.95e-03 (std =  2.60e-03) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  1.17e-03 (std =  1.76e-03) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  7.05e-04 (std =  1.20e-03) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  4.30e-04 (std =  8.23e-04) :   6%|▌         | 6/100 [00:00<00:01, 59.68it/s]
[TorchDR] Root search : mean abs value =  4.30e-04 (std =  8.23e-04) :  15%|█▌        | 15/100 [00:00<00:01, 77.16it/s]
[TorchDR] Root search : mean abs value =  2.64e-04 (std =  5.66e-04) :  15%|█▌        | 15/100 [00:00<00:01, 77.16it/s]
[TorchDR] Root search : mean abs value =  1.64e-04 (std =  3.92e-04) :  15%|█▌        | 15/100 [00:00<00:01, 77.16it/s]
[TorchDR] Root search : mean abs value =  1.02e-04 (std =  2.72e-04) :  15%|█▌        | 15/100 [00:00<00:01, 77.16it/s]
[TorchDR] Root search : mean abs value =  6.47e-05 (std =  1.90e-04) :  15%|█▌        | 15/100 [00:00<00:01, 77.16it/s]
[TorchDR] Root search : mean abs value =  4.11e-05 (std =  1.33e-04) :  15%|█▌        | 15/100 [00:00<00:01, 77.16it/s]
[TorchDR] Root search : mean abs value =  2.63e-05 (std =  9.35e-05) :  15%|█▌        | 15/100 [00:00<00:01, 77.16it/s]
[TorchDR] Root search : mean abs value =  1.70e-05 (std =  6.59e-05) :  15%|█▌        | 15/100 [00:00<00:01, 77.16it/s]
[TorchDR] Root search : mean abs value =  1.11e-05 (std =  4.65e-05) :  15%|█▌        | 15/100 [00:00<00:01, 77.16it/s]
[TorchDR] Root search : mean abs value =  1.11e-05 (std =  4.65e-05) :  23%|██▎       | 23/100 [00:00<00:01, 54.35it/s]
[TorchDR] Root search : mean abs value =  1.11e-05 (std =  4.65e-05) :  23%|██▎       | 23/100 [00:00<00:01, 57.65it/s]
Random state is None

  0%|          | 0/500 [00:00<?, ?it/s]
[TorchDR] DR Loss : 1.31e+01 | Grad norm : 2.50e-05 :   0%|          | 0/500 [00:00<?, ?it/s]
[TorchDR] DR Loss : 1.31e+01 | Grad norm : 2.50e-05 :   0%|          | 0/500 [00:00<?, ?it/s]
[TorchDR] DR Loss : 1.31e+01 | Grad norm : 2.50e-05 :   0%|          | 2/500 [00:00<00:49, 10.05it/s]
[TorchDR] DR Loss : 1.31e+01 | Grad norm : 2.50e-05 :   0%|          | 2/500 [00:00<00:49, 10.05it/s]
[TorchDR] DR Loss : 1.31e+01 | Grad norm : 2.50e-05 :   0%|          | 2/500 [00:00<00:49, 10.05it/s]
[TorchDR] DR Loss : 1.31e+01 | Grad norm : 2.50e-05 :   1%|          | 4/500 [00:00<00:49, 10.04it/s]
[TorchDR] DR Loss : 1.31e+01 | Grad norm : 2.50e-05 :   1%|          | 4/500 [00:00<00:49, 10.04it/s]
[TorchDR] DR Loss : 1.31e+01 | Grad norm : 2.50e-05 :   1%|          | 4/500 [00:00<00:49, 10.04it/s]
[TorchDR] DR Loss : 1.31e+01 | Grad norm : 2.50e-05 :   1%|          | 6/500 [00:00<00:38, 12.86it/s]
[TorchDR] DR Loss : 1.40e+01 | Grad norm : 2.50e-05 :   1%|          | 6/500 [00:00<00:38, 12.86it/s]
[TorchDR] DR Loss : 1.65e+01 | Grad norm : 2.50e-05 :   1%|          | 6/500 [00:00<00:38, 12.86it/s]
[TorchDR] DR Loss : 1.65e+01 | Grad norm : 2.50e-05 :   2%|▏         | 8/500 [00:00<00:42, 11.61it/s]
[TorchDR] DR Loss : 1.74e+01 | Grad norm : 2.50e-05 :   2%|▏         | 8/500 [00:00<00:42, 11.61it/s]
[TorchDR] DR Loss : 1.85e+01 | Grad norm : 2.50e-05 :   2%|▏         | 8/500 [00:00<00:42, 11.61it/s]
[TorchDR] DR Loss : 1.85e+01 | Grad norm : 2.50e-05 :   2%|▏         | 10/500 [00:00<00:44, 10.95it/s]
[TorchDR] DR Loss : 1.79e+01 | Grad norm : 2.50e-05 :   2%|▏         | 10/500 [00:00<00:44, 10.95it/s]
[TorchDR] DR Loss : 1.84e+01 | Grad norm : 2.50e-05 :   2%|▏         | 10/500 [00:01<00:44, 10.95it/s]
[TorchDR] DR Loss : 1.84e+01 | Grad norm : 2.50e-05 :   2%|▏         | 12/500 [00:01<00:45, 10.66it/s]
[TorchDR] DR Loss : 1.88e+01 | Grad norm : 2.50e-05 :   2%|▏         | 12/500 [00:01<00:45, 10.66it/s]
[TorchDR] DR Loss : 1.78e+01 | Grad norm : 2.50e-05 :   2%|▏         | 12/500 [00:01<00:45, 10.66it/s]
[TorchDR] DR Loss : 1.78e+01 | Grad norm : 2.50e-05 :   3%|▎         | 14/500 [00:01<00:39, 12.45it/s]
[TorchDR] DR Loss : 1.90e+01 | Grad norm : 2.50e-05 :   3%|▎         | 14/500 [00:01<00:39, 12.45it/s]
[TorchDR] DR Loss : 1.81e+01 | Grad norm : 2.50e-05 :   3%|▎         | 14/500 [00:01<00:39, 12.45it/s]
[TorchDR] DR Loss : 1.81e+01 | Grad norm : 2.50e-05 :   3%|▎         | 16/500 [00:01<00:41, 11.60it/s]
[TorchDR] DR Loss : 1.81e+01 | Grad norm : 2.50e-05 :   3%|▎         | 16/500 [00:01<00:41, 11.60it/s]
[TorchDR] DR Loss : 1.81e+01 | Grad norm : 2.50e-05 :   3%|▎         | 16/500 [00:01<00:41, 11.60it/s]
[TorchDR] DR Loss : 1.81e+01 | Grad norm : 2.50e-05 :   4%|▎         | 18/500 [00:01<00:43, 11.11it/s]
[TorchDR] DR Loss : 1.84e+01 | Grad norm : 2.50e-05 :   4%|▎         | 18/500 [00:01<00:43, 11.11it/s]
[TorchDR] DR Loss : 1.78e+01 | Grad norm : 2.50e-05 :   4%|▎         | 18/500 [00:01<00:43, 11.11it/s]
[TorchDR] DR Loss : 1.78e+01 | Grad norm : 2.50e-05 :   4%|▍         | 20/500 [00:01<00:37, 12.77it/s]
[TorchDR] DR Loss : 1.81e+01 | Grad norm : 2.50e-05 :   4%|▍         | 20/500 [00:01<00:37, 12.77it/s]
[TorchDR] DR Loss : 1.81e+01 | Grad norm : 2.50e-05 :   4%|▍         | 20/500 [00:01<00:37, 12.77it/s]
[TorchDR] DR Loss : 1.81e+01 | Grad norm : 2.50e-05 :   4%|▍         | 22/500 [00:01<00:40, 11.81it/s]
[TorchDR] DR Loss : 1.80e+01 | Grad norm : 2.50e-05 :   4%|▍         | 22/500 [00:01<00:40, 11.81it/s]
[TorchDR] DR Loss : 1.83e+01 | Grad norm : 2.50e-05 :   4%|▍         | 22/500 [00:02<00:40, 11.81it/s]
[TorchDR] DR Loss : 1.83e+01 | Grad norm : 2.50e-05 :   5%|▍         | 24/500 [00:02<00:35, 13.44it/s]
[TorchDR] DR Loss : 1.79e+01 | Grad norm : 2.50e-05 :   5%|▍         | 24/500 [00:02<00:35, 13.44it/s]
[TorchDR] DR Loss : 1.86e+01 | Grad norm : 2.50e-05 :   5%|▍         | 24/500 [00:02<00:35, 13.44it/s]
[TorchDR] DR Loss : 1.86e+01 | Grad norm : 2.50e-05 :   5%|▌         | 26/500 [00:02<00:38, 12.24it/s]
[TorchDR] DR Loss : 1.77e+01 | Grad norm : 2.50e-05 :   5%|▌         | 26/500 [00:02<00:38, 12.24it/s]
[TorchDR] DR Loss : 1.84e+01 | Grad norm : 2.50e-05 :   5%|▌         | 26/500 [00:02<00:38, 12.24it/s]
[TorchDR] DR Loss : 1.84e+01 | Grad norm : 2.50e-05 :   6%|▌         | 28/500 [00:02<00:34, 13.77it/s]
[TorchDR] DR Loss : 1.83e+01 | Grad norm : 2.50e-05 :   6%|▌         | 28/500 [00:02<00:34, 13.77it/s]
[TorchDR] DR Loss : 1.74e+01 | Grad norm : 2.50e-05 :   6%|▌         | 28/500 [00:02<00:34, 13.77it/s]
[TorchDR] DR Loss : 1.74e+01 | Grad norm : 2.50e-05 :   6%|▌         | 30/500 [00:02<00:37, 12.42it/s]
[TorchDR] DR Loss : 1.87e+01 | Grad norm : 2.50e-05 :   6%|▌         | 30/500 [00:02<00:37, 12.42it/s]
[TorchDR] DR Loss : 1.78e+01 | Grad norm : 2.50e-05 :   6%|▌         | 30/500 [00:02<00:37, 12.42it/s]
[TorchDR] DR Loss : 1.78e+01 | Grad norm : 2.50e-05 :   6%|▋         | 32/500 [00:02<00:40, 11.64it/s]
[TorchDR] DR Loss : 1.81e+01 | Grad norm : 2.50e-05 :   6%|▋         | 32/500 [00:02<00:40, 11.64it/s]
[TorchDR] DR Loss : 1.76e+01 | Grad norm : 2.50e-05 :   6%|▋         | 32/500 [00:02<00:40, 11.64it/s]
[TorchDR] DR Loss : 1.76e+01 | Grad norm : 2.50e-05 :   7%|▋         | 34/500 [00:02<00:35, 13.19it/s]
[TorchDR] DR Loss : 1.79e+01 | Grad norm : 2.50e-05 :   7%|▋         | 34/500 [00:02<00:35, 13.19it/s]
[TorchDR] DR Loss : 1.76e+01 | Grad norm : 2.50e-05 :   7%|▋         | 34/500 [00:02<00:35, 13.19it/s]
[TorchDR] DR Loss : 1.76e+01 | Grad norm : 2.50e-05 :   7%|▋         | 36/500 [00:02<00:38, 12.11it/s]
[TorchDR] DR Loss : 1.76e+01 | Grad norm : 2.50e-05 :   7%|▋         | 36/500 [00:03<00:38, 12.11it/s]
[TorchDR] DR Loss : 1.73e+01 | Grad norm : 2.50e-05 :   7%|▋         | 36/500 [00:03<00:38, 12.11it/s]
[TorchDR] DR Loss : 1.73e+01 | Grad norm : 2.50e-05 :   8%|▊         | 38/500 [00:03<00:40, 11.33it/s]
[TorchDR] DR Loss : 1.74e+01 | Grad norm : 2.50e-05 :   8%|▊         | 38/500 [00:03<00:40, 11.33it/s]
[TorchDR] DR Loss : 1.72e+01 | Grad norm : 2.50e-05 :   8%|▊         | 38/500 [00:03<00:40, 11.33it/s]
[TorchDR] DR Loss : 1.72e+01 | Grad norm : 2.50e-05 :   8%|▊         | 40/500 [00:03<00:42, 10.92it/s]
[TorchDR] DR Loss : 1.72e+01 | Grad norm : 2.50e-05 :   8%|▊         | 40/500 [00:03<00:42, 10.92it/s]
[TorchDR] DR Loss : 1.73e+01 | Grad norm : 2.50e-05 :   8%|▊         | 40/500 [00:03<00:42, 10.92it/s]
[TorchDR] DR Loss : 1.73e+01 | Grad norm : 2.50e-05 :   8%|▊         | 42/500 [00:03<00:43, 10.63it/s]
[TorchDR] DR Loss : 1.70e+01 | Grad norm : 2.50e-05 :   8%|▊         | 42/500 [00:03<00:43, 10.63it/s]
[TorchDR] DR Loss : 1.70e+01 | Grad norm : 2.50e-05 :   8%|▊         | 42/500 [00:03<00:43, 10.63it/s]
[TorchDR] DR Loss : 1.70e+01 | Grad norm : 2.50e-05 :   9%|▉         | 44/500 [00:03<00:43, 10.43it/s]
[TorchDR] DR Loss : 1.73e+01 | Grad norm : 2.50e-05 :   9%|▉         | 44/500 [00:03<00:43, 10.43it/s]
[TorchDR] DR Loss : 1.65e+01 | Grad norm : 2.50e-05 :   9%|▉         | 44/500 [00:03<00:43, 10.43it/s]
[TorchDR] DR Loss : 1.65e+01 | Grad norm : 2.50e-05 :   9%|▉         | 46/500 [00:03<00:37, 12.09it/s]
[TorchDR] DR Loss : 1.75e+01 | Grad norm : 2.50e-05 :   9%|▉         | 46/500 [00:04<00:37, 12.09it/s]
[TorchDR] DR Loss : 1.68e+01 | Grad norm : 2.50e-05 :   9%|▉         | 46/500 [00:04<00:37, 12.09it/s]
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[TorchDR] DR Loss : 1.67e+01 | Grad norm : 6.50e-01 :  18%|█▊        | 91/500 [00:08<00:47,  8.59it/s]
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[TorchDR] DR Loss : 1.66e+01 | Grad norm : 6.50e-01 :  19%|█▉        | 95/500 [00:09<00:43,  9.33it/s]
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[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  85%|████████▍ | 424/500 [00:35<00:06, 12.66it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  85%|████████▍ | 424/500 [00:35<00:06, 12.66it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  85%|████████▌ | 426/500 [00:35<00:05, 14.13it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  85%|████████▌ | 426/500 [00:35<00:05, 14.13it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  85%|████████▌ | 426/500 [00:35<00:05, 14.13it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  86%|████████▌ | 428/500 [00:35<00:05, 12.65it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  86%|████████▌ | 428/500 [00:35<00:05, 12.65it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  86%|████████▌ | 428/500 [00:36<00:05, 12.65it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  86%|████████▌ | 430/500 [00:36<00:04, 14.11it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  86%|████████▌ | 430/500 [00:36<00:04, 14.11it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  86%|████████▌ | 430/500 [00:36<00:04, 14.11it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  86%|████████▋ | 432/500 [00:36<00:05, 12.62it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  86%|████████▋ | 432/500 [00:36<00:05, 12.62it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  86%|████████▋ | 432/500 [00:36<00:05, 12.62it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  87%|████████▋ | 434/500 [00:36<00:04, 14.11it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  87%|████████▋ | 434/500 [00:36<00:04, 14.11it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  87%|████████▋ | 434/500 [00:36<00:04, 14.11it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  87%|████████▋ | 436/500 [00:36<00:05, 12.63it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  87%|████████▋ | 436/500 [00:36<00:05, 12.63it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  87%|████████▋ | 436/500 [00:36<00:05, 12.63it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  88%|████████▊ | 438/500 [00:36<00:04, 14.08it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  88%|████████▊ | 438/500 [00:36<00:04, 14.08it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  88%|████████▊ | 438/500 [00:36<00:04, 14.08it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  88%|████████▊ | 440/500 [00:36<00:04, 12.52it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  88%|████████▊ | 440/500 [00:36<00:04, 12.52it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  88%|████████▊ | 440/500 [00:37<00:04, 12.52it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  88%|████████▊ | 442/500 [00:37<00:04, 11.70it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  88%|████████▊ | 442/500 [00:37<00:04, 11.70it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  88%|████████▊ | 442/500 [00:37<00:04, 11.70it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  89%|████████▉ | 444/500 [00:37<00:05, 11.14it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  89%|████████▉ | 444/500 [00:37<00:05, 11.14it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  89%|████████▉ | 444/500 [00:37<00:05, 11.14it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  89%|████████▉ | 446/500 [00:37<00:04, 10.81it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  89%|████████▉ | 446/500 [00:37<00:04, 10.81it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  89%|████████▉ | 446/500 [00:37<00:04, 10.81it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  90%|████████▉ | 448/500 [00:37<00:04, 12.44it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  90%|████████▉ | 448/500 [00:37<00:04, 12.44it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  90%|████████▉ | 448/500 [00:37<00:04, 12.44it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.93e-03 :  90%|█████████ | 450/500 [00:37<00:04, 11.63it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  90%|█████████ | 450/500 [00:37<00:04, 11.63it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  90%|█████████ | 450/500 [00:37<00:04, 11.63it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  90%|█████████ | 452/500 [00:37<00:04, 11.11it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  90%|█████████ | 452/500 [00:37<00:04, 11.11it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  90%|█████████ | 452/500 [00:38<00:04, 11.11it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  91%|█████████ | 454/500 [00:38<00:04, 10.77it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  91%|█████████ | 454/500 [00:38<00:04, 10.77it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  91%|█████████ | 454/500 [00:38<00:04, 10.77it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  91%|█████████ | 456/500 [00:38<00:03, 11.05it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  91%|█████████ | 456/500 [00:38<00:03, 11.05it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  91%|█████████ | 456/500 [00:38<00:03, 11.05it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  92%|█████████▏| 458/500 [00:38<00:03, 12.00it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  92%|█████████▏| 458/500 [00:38<00:03, 12.00it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  92%|█████████▏| 458/500 [00:38<00:03, 12.00it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  92%|█████████▏| 460/500 [00:38<00:03, 11.35it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  92%|█████████▏| 460/500 [00:38<00:03, 11.35it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  92%|█████████▏| 460/500 [00:38<00:03, 11.35it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  92%|█████████▏| 462/500 [00:38<00:03, 10.93it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  92%|█████████▏| 462/500 [00:38<00:03, 10.93it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  92%|█████████▏| 462/500 [00:38<00:03, 10.93it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  93%|█████████▎| 464/500 [00:38<00:02, 12.59it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  93%|█████████▎| 464/500 [00:39<00:02, 12.59it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  93%|█████████▎| 464/500 [00:39<00:02, 12.59it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  93%|█████████▎| 466/500 [00:39<00:02, 11.78it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  93%|█████████▎| 466/500 [00:39<00:02, 11.78it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  93%|█████████▎| 466/500 [00:39<00:02, 11.78it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  94%|█████████▎| 468/500 [00:39<00:02, 13.33it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  94%|█████████▎| 468/500 [00:39<00:02, 13.33it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  94%|█████████▎| 468/500 [00:39<00:02, 13.33it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  94%|█████████▍| 470/500 [00:39<00:02, 12.17it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  94%|█████████▍| 470/500 [00:39<00:02, 12.17it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  94%|█████████▍| 470/500 [00:39<00:02, 12.17it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  94%|█████████▍| 472/500 [00:39<00:02, 11.39it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  94%|█████████▍| 472/500 [00:39<00:02, 11.39it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  94%|█████████▍| 472/500 [00:39<00:02, 11.39it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  95%|█████████▍| 474/500 [00:39<00:02, 10.98it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  95%|█████████▍| 474/500 [00:39<00:02, 10.98it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  95%|█████████▍| 474/500 [00:39<00:02, 10.98it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  95%|█████████▌| 476/500 [00:39<00:01, 12.60it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  95%|█████████▌| 476/500 [00:40<00:01, 12.60it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  95%|█████████▌| 476/500 [00:40<00:01, 12.60it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  96%|█████████▌| 478/500 [00:40<00:01, 11.73it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  96%|█████████▌| 478/500 [00:40<00:01, 11.73it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  96%|█████████▌| 478/500 [00:40<00:01, 11.73it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  96%|█████████▌| 480/500 [00:40<00:01, 11.18it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  96%|█████████▌| 480/500 [00:40<00:01, 11.18it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  96%|█████████▌| 480/500 [00:40<00:01, 11.18it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  96%|█████████▋| 482/500 [00:40<00:01, 12.80it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  96%|█████████▋| 482/500 [00:40<00:01, 12.80it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  96%|█████████▋| 482/500 [00:40<00:01, 12.80it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  97%|█████████▋| 484/500 [00:40<00:01, 11.86it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  97%|█████████▋| 484/500 [00:40<00:01, 11.86it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  97%|█████████▋| 484/500 [00:40<00:01, 11.86it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  97%|█████████▋| 486/500 [00:40<00:01, 13.42it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  97%|█████████▋| 486/500 [00:40<00:01, 13.42it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  97%|█████████▋| 486/500 [00:40<00:01, 13.42it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  98%|█████████▊| 488/500 [00:40<00:00, 12.22it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  98%|█████████▊| 488/500 [00:40<00:00, 12.22it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  98%|█████████▊| 488/500 [00:41<00:00, 12.22it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  98%|█████████▊| 490/500 [00:41<00:00, 12.13it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  98%|█████████▊| 490/500 [00:41<00:00, 12.13it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  98%|█████████▊| 490/500 [00:41<00:00, 12.13it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  98%|█████████▊| 492/500 [00:41<00:00, 12.88it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  98%|█████████▊| 492/500 [00:41<00:00, 12.88it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  98%|█████████▊| 492/500 [00:41<00:00, 12.88it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  99%|█████████▉| 494/500 [00:41<00:00, 11.93it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  99%|█████████▉| 494/500 [00:41<00:00, 11.93it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  99%|█████████▉| 494/500 [00:41<00:00, 11.93it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  99%|█████████▉| 496/500 [00:41<00:00, 13.48it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  99%|█████████▉| 496/500 [00:41<00:00, 13.48it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 :  99%|█████████▉| 496/500 [00:41<00:00, 13.48it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 : 100%|█████████▉| 498/500 [00:41<00:00, 12.25it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 : 100%|█████████▉| 498/500 [00:41<00:00, 12.25it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 : 100%|█████████▉| 498/500 [00:41<00:00, 12.25it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 : 100%|██████████| 500/500 [00:41<00:00, 13.79it/s]
[TorchDR] DR Loss : 1.11e+01 | Grad norm : 1.72e-03 : 100%|██████████| 500/500 [00:41<00:00, 11.96it/s]
Random state is None
[TorchDR] Initializing DR model COSNE.
[TorchDR] Affinity : computing the Entropic Affinity matrix.
[TorchDR] Affinity : sparsity mode enabled, computing 90 nearest neighbors. If this step is too slow, consider reducing the dimensionality of the data or disabling sparsity.

  0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  5.27e-01 (std =  5.68e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  3.72e-01 (std =  5.93e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  2.42e-01 (std =  5.54e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  1.48e-01 (std =  4.64e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  8.68e-02 (std =  3.58e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  8.68e-02 (std =  3.58e-02) :   5%|▌         | 5/100 [00:00<00:01, 49.97it/s]
[TorchDR] Root search : mean abs value =  5.01e-02 (std =  2.60e-02) :   5%|▌         | 5/100 [00:00<00:01, 49.97it/s]
[TorchDR] Root search : mean abs value =  2.88e-02 (std =  1.82e-02) :   5%|▌         | 5/100 [00:00<00:01, 49.97it/s]
[TorchDR] Root search : mean abs value =  1.66e-02 (std =  1.24e-02) :   5%|▌         | 5/100 [00:00<00:01, 49.97it/s]
[TorchDR] Root search : mean abs value =  9.57e-03 (std =  8.43e-03) :   5%|▌         | 5/100 [00:00<00:01, 49.97it/s]
[TorchDR] Root search : mean abs value =  5.58e-03 (std =  5.69e-03) :   5%|▌         | 5/100 [00:00<00:01, 49.97it/s]
[TorchDR] Root search : mean abs value =  3.28e-03 (std =  3.84e-03) :   5%|▌         | 5/100 [00:00<00:01, 49.97it/s]
[TorchDR] Root search : mean abs value =  1.95e-03 (std =  2.60e-03) :   5%|▌         | 5/100 [00:00<00:01, 49.97it/s]
[TorchDR] Root search : mean abs value =  1.17e-03 (std =  1.76e-03) :   5%|▌         | 5/100 [00:00<00:01, 49.97it/s]
[TorchDR] Root search : mean abs value =  1.17e-03 (std =  1.76e-03) :  13%|█▎        | 13/100 [00:00<00:01, 67.60it/s]
[TorchDR] Root search : mean abs value =  7.05e-04 (std =  1.20e-03) :  13%|█▎        | 13/100 [00:00<00:01, 67.60it/s]
[TorchDR] Root search : mean abs value =  4.30e-04 (std =  8.23e-04) :  13%|█▎        | 13/100 [00:00<00:01, 67.60it/s]
[TorchDR] Root search : mean abs value =  2.64e-04 (std =  5.66e-04) :  13%|█▎        | 13/100 [00:00<00:01, 67.60it/s]
[TorchDR] Root search : mean abs value =  1.64e-04 (std =  3.92e-04) :  13%|█▎        | 13/100 [00:00<00:01, 67.60it/s]
[TorchDR] Root search : mean abs value =  1.02e-04 (std =  2.72e-04) :  13%|█▎        | 13/100 [00:00<00:01, 67.60it/s]
[TorchDR] Root search : mean abs value =  6.47e-05 (std =  1.90e-04) :  13%|█▎        | 13/100 [00:00<00:01, 67.60it/s]
[TorchDR] Root search : mean abs value =  4.11e-05 (std =  1.33e-04) :  13%|█▎        | 13/100 [00:00<00:01, 67.60it/s]
[TorchDR] Root search : mean abs value =  2.63e-05 (std =  9.35e-05) :  13%|█▎        | 13/100 [00:00<00:01, 67.60it/s]
[TorchDR] Root search : mean abs value =  2.63e-05 (std =  9.35e-05) :  21%|██        | 21/100 [00:00<00:01, 73.07it/s]
[TorchDR] Root search : mean abs value =  1.70e-05 (std =  6.59e-05) :  21%|██        | 21/100 [00:00<00:01, 73.07it/s]
[TorchDR] Root search : mean abs value =  1.11e-05 (std =  4.65e-05) :  21%|██        | 21/100 [00:00<00:01, 73.07it/s]
[TorchDR] Root search : mean abs value =  1.11e-05 (std =  4.65e-05) :  23%|██▎       | 23/100 [00:00<00:01, 58.00it/s]

  0%|          | 0/500 [00:00<?, ?it/s]
[TorchDR] DR Loss : 3.32e+01 | Grad norm : 2.69e+00 :   0%|          | 0/500 [00:00<?, ?it/s]
[TorchDR] DR Loss : 3.32e+01 | Grad norm : 2.69e+00 :   0%|          | 1/500 [00:00<02:29,  3.33it/s]
[TorchDR] DR Loss : 3.16e+01 | Grad norm : 2.69e+00 :   0%|          | 1/500 [00:00<02:29,  3.33it/s]
[TorchDR] DR Loss : 3.16e+01 | Grad norm : 2.69e+00 :   0%|          | 2/500 [00:00<02:29,  3.34it/s]
[TorchDR] DR Loss : 3.00e+01 | Grad norm : 2.69e+00 :   0%|          | 2/500 [00:00<02:29,  3.34it/s]
[TorchDR] DR Loss : 3.00e+01 | Grad norm : 2.69e+00 :   1%|          | 3/500 [00:00<02:07,  3.91it/s]
[TorchDR] DR Loss : 2.83e+01 | Grad norm : 2.69e+00 :   1%|          | 3/500 [00:01<02:07,  3.91it/s]
[TorchDR] DR Loss : 2.83e+01 | Grad norm : 2.69e+00 :   1%|          | 4/500 [00:01<02:14,  3.68it/s]
[TorchDR] DR Loss : 2.66e+01 | Grad norm : 2.69e+00 :   1%|          | 4/500 [00:01<02:14,  3.68it/s]
[TorchDR] DR Loss : 2.66e+01 | Grad norm : 2.69e+00 :   1%|          | 5/500 [00:01<02:19,  3.55it/s]
[TorchDR] DR Loss : 2.49e+01 | Grad norm : 2.69e+00 :   1%|          | 5/500 [00:01<02:19,  3.55it/s]
[TorchDR] DR Loss : 2.49e+01 | Grad norm : 2.69e+00 :   1%|          | 6/500 [00:01<02:06,  3.90it/s]
[TorchDR] DR Loss : 2.32e+01 | Grad norm : 2.69e+00 :   1%|          | 6/500 [00:01<02:06,  3.90it/s]
[TorchDR] DR Loss : 2.32e+01 | Grad norm : 2.69e+00 :   1%|▏         | 7/500 [00:01<02:12,  3.72it/s]
[TorchDR] DR Loss : 2.16e+01 | Grad norm : 2.69e+00 :   1%|▏         | 7/500 [00:02<02:12,  3.72it/s]
[TorchDR] DR Loss : 2.16e+01 | Grad norm : 2.69e+00 :   2%|▏         | 8/500 [00:02<02:17,  3.59it/s]
[TorchDR] DR Loss : 1.99e+01 | Grad norm : 2.69e+00 :   2%|▏         | 8/500 [00:02<02:17,  3.59it/s]
[TorchDR] DR Loss : 1.99e+01 | Grad norm : 2.69e+00 :   2%|▏         | 9/500 [00:02<02:14,  3.64it/s]
[TorchDR] DR Loss : 1.84e+01 | Grad norm : 2.69e+00 :   2%|▏         | 9/500 [00:02<02:14,  3.64it/s]
[TorchDR] DR Loss : 1.84e+01 | Grad norm : 2.69e+00 :   2%|▏         | 10/500 [00:02<02:09,  3.80it/s]
[TorchDR] DR Loss : 1.69e+01 | Grad norm : 2.69e+00 :   2%|▏         | 10/500 [00:02<02:09,  3.80it/s]
[TorchDR] DR Loss : 1.69e+01 | Grad norm : 2.69e+00 :   2%|▏         | 11/500 [00:02<01:59,  4.10it/s]
[TorchDR] DR Loss : 1.56e+01 | Grad norm : 2.69e+00 :   2%|▏         | 11/500 [00:03<01:59,  4.10it/s]
[TorchDR] DR Loss : 1.56e+01 | Grad norm : 2.69e+00 :   2%|▏         | 12/500 [00:03<02:06,  3.85it/s]
[TorchDR] DR Loss : 1.44e+01 | Grad norm : 2.69e+00 :   2%|▏         | 12/500 [00:03<02:06,  3.85it/s]
[TorchDR] DR Loss : 1.44e+01 | Grad norm : 2.69e+00 :   3%|▎         | 13/500 [00:03<02:12,  3.67it/s]
[TorchDR] DR Loss : 1.33e+01 | Grad norm : 2.69e+00 :   3%|▎         | 13/500 [00:03<02:12,  3.67it/s]
[TorchDR] DR Loss : 1.33e+01 | Grad norm : 2.69e+00 :   3%|▎         | 14/500 [00:03<02:16,  3.57it/s]
[TorchDR] DR Loss : 1.23e+01 | Grad norm : 2.69e+00 :   3%|▎         | 14/500 [00:04<02:16,  3.57it/s]
[TorchDR] DR Loss : 1.23e+01 | Grad norm : 2.69e+00 :   3%|▎         | 15/500 [00:04<02:18,  3.50it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 2.69e+00 :   3%|▎         | 15/500 [00:04<02:18,  3.50it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 2.69e+00 :   3%|▎         | 16/500 [00:04<02:06,  3.84it/s]
[TorchDR] DR Loss : 1.07e+01 | Grad norm : 2.69e+00 :   3%|▎         | 16/500 [00:04<02:06,  3.84it/s]
[TorchDR] DR Loss : 1.07e+01 | Grad norm : 2.69e+00 :   3%|▎         | 17/500 [00:04<02:11,  3.68it/s]
[TorchDR] DR Loss : 1.01e+01 | Grad norm : 2.69e+00 :   3%|▎         | 17/500 [00:04<02:11,  3.68it/s]
[TorchDR] DR Loss : 1.01e+01 | Grad norm : 2.69e+00 :   4%|▎         | 18/500 [00:04<02:01,  3.97it/s]
[TorchDR] DR Loss : 9.49e+00 | Grad norm : 2.69e+00 :   4%|▎         | 18/500 [00:05<02:01,  3.97it/s]
[TorchDR] DR Loss : 9.49e+00 | Grad norm : 2.69e+00 :   4%|▍         | 19/500 [00:05<02:07,  3.78it/s]
[TorchDR] DR Loss : 9.01e+00 | Grad norm : 2.69e+00 :   4%|▍         | 19/500 [00:05<02:07,  3.78it/s]
[TorchDR] DR Loss : 9.01e+00 | Grad norm : 2.69e+00 :   4%|▍         | 20/500 [00:05<02:12,  3.63it/s]
[TorchDR] DR Loss : 8.59e+00 | Grad norm : 2.69e+00 :   4%|▍         | 20/500 [00:05<02:12,  3.63it/s]
[TorchDR] DR Loss : 8.59e+00 | Grad norm : 2.69e+00 :   4%|▍         | 21/500 [00:05<02:15,  3.53it/s]
[TorchDR] DR Loss : 8.24e+00 | Grad norm : 2.69e+00 :   4%|▍         | 21/500 [00:06<02:15,  3.53it/s]
[TorchDR] DR Loss : 8.24e+00 | Grad norm : 2.69e+00 :   4%|▍         | 22/500 [00:06<02:31,  3.16it/s]
[TorchDR] DR Loss : 7.94e+00 | Grad norm : 2.69e+00 :   4%|▍         | 22/500 [00:06<02:31,  3.16it/s]
[TorchDR] DR Loss : 7.94e+00 | Grad norm : 2.69e+00 :   5%|▍         | 23/500 [00:06<02:15,  3.52it/s]
[TorchDR] DR Loss : 7.68e+00 | Grad norm : 2.69e+00 :   5%|▍         | 23/500 [00:06<02:15,  3.52it/s]
[TorchDR] DR Loss : 7.68e+00 | Grad norm : 2.69e+00 :   5%|▍         | 24/500 [00:06<02:16,  3.48it/s]
[TorchDR] DR Loss : 7.46e+00 | Grad norm : 2.69e+00 :   5%|▍         | 24/500 [00:06<02:16,  3.48it/s]
[TorchDR] DR Loss : 7.46e+00 | Grad norm : 2.69e+00 :   5%|▌         | 25/500 [00:06<02:18,  3.44it/s]
[TorchDR] DR Loss : 7.27e+00 | Grad norm : 2.69e+00 :   5%|▌         | 25/500 [00:07<02:18,  3.44it/s]
[TorchDR] DR Loss : 7.27e+00 | Grad norm : 2.69e+00 :   5%|▌         | 26/500 [00:07<02:05,  3.77it/s]
[TorchDR] DR Loss : 7.10e+00 | Grad norm : 2.69e+00 :   5%|▌         | 26/500 [00:07<02:05,  3.77it/s]
[TorchDR] DR Loss : 7.10e+00 | Grad norm : 2.69e+00 :   5%|▌         | 27/500 [00:07<02:23,  3.30it/s]
[TorchDR] DR Loss : 6.96e+00 | Grad norm : 2.69e+00 :   5%|▌         | 27/500 [00:07<02:23,  3.30it/s]
[TorchDR] DR Loss : 6.96e+00 | Grad norm : 2.69e+00 :   6%|▌         | 28/500 [00:07<02:09,  3.65it/s]
[TorchDR] DR Loss : 6.83e+00 | Grad norm : 2.69e+00 :   6%|▌         | 28/500 [00:07<02:09,  3.65it/s]
[TorchDR] DR Loss : 6.83e+00 | Grad norm : 2.69e+00 :   6%|▌         | 29/500 [00:07<01:58,  3.96it/s]
[TorchDR] DR Loss : 6.70e+00 | Grad norm : 2.69e+00 :   6%|▌         | 29/500 [00:08<01:58,  3.96it/s]
[TorchDR] DR Loss : 6.70e+00 | Grad norm : 2.69e+00 :   6%|▌         | 30/500 [00:08<02:04,  3.77it/s]
[TorchDR] DR Loss : 6.57e+00 | Grad norm : 2.69e+00 :   6%|▌         | 30/500 [00:08<02:04,  3.77it/s]
[TorchDR] DR Loss : 6.57e+00 | Grad norm : 2.69e+00 :   6%|▌         | 31/500 [00:08<02:08,  3.64it/s]
[TorchDR] DR Loss : 6.44e+00 | Grad norm : 2.69e+00 :   6%|▌         | 31/500 [00:08<02:08,  3.64it/s]
[TorchDR] DR Loss : 6.44e+00 | Grad norm : 2.69e+00 :   6%|▋         | 32/500 [00:08<01:58,  3.94it/s]
[TorchDR] DR Loss : 6.32e+00 | Grad norm : 2.69e+00 :   6%|▋         | 32/500 [00:08<01:58,  3.94it/s]
[TorchDR] DR Loss : 6.32e+00 | Grad norm : 2.69e+00 :   7%|▋         | 33/500 [00:08<01:51,  4.19it/s]
[TorchDR] DR Loss : 6.20e+00 | Grad norm : 2.69e+00 :   7%|▋         | 33/500 [00:09<01:51,  4.19it/s]
[TorchDR] DR Loss : 6.20e+00 | Grad norm : 2.69e+00 :   7%|▋         | 34/500 [00:09<01:45,  4.40it/s]
[TorchDR] DR Loss : 6.09e+00 | Grad norm : 2.69e+00 :   7%|▋         | 34/500 [00:09<01:45,  4.40it/s]
[TorchDR] DR Loss : 6.09e+00 | Grad norm : 2.69e+00 :   7%|▋         | 35/500 [00:09<01:55,  4.04it/s]
[TorchDR] DR Loss : 6.00e+00 | Grad norm : 2.69e+00 :   7%|▋         | 35/500 [00:09<01:55,  4.04it/s]
[TorchDR] DR Loss : 6.00e+00 | Grad norm : 2.69e+00 :   7%|▋         | 36/500 [00:09<01:49,  4.26it/s]
[TorchDR] DR Loss : 5.92e+00 | Grad norm : 2.69e+00 :   7%|▋         | 36/500 [00:09<01:49,  4.26it/s]
[TorchDR] DR Loss : 5.92e+00 | Grad norm : 2.69e+00 :   7%|▋         | 37/500 [00:09<01:57,  3.94it/s]
[TorchDR] DR Loss : 5.85e+00 | Grad norm : 2.69e+00 :   7%|▋         | 37/500 [00:10<01:57,  3.94it/s]
[TorchDR] DR Loss : 5.85e+00 | Grad norm : 2.69e+00 :   8%|▊         | 38/500 [00:10<02:31,  3.05it/s]
[TorchDR] DR Loss : 5.79e+00 | Grad norm : 2.69e+00 :   8%|▊         | 38/500 [00:10<02:31,  3.05it/s]
[TorchDR] DR Loss : 5.79e+00 | Grad norm : 2.69e+00 :   8%|▊         | 39/500 [00:10<02:50,  2.71it/s]
[TorchDR] DR Loss : 5.74e+00 | Grad norm : 2.69e+00 :   8%|▊         | 39/500 [00:11<02:50,  2.71it/s]
[TorchDR] DR Loss : 5.74e+00 | Grad norm : 2.69e+00 :   8%|▊         | 40/500 [00:11<02:45,  2.78it/s]
[TorchDR] DR Loss : 5.68e+00 | Grad norm : 2.69e+00 :   8%|▊         | 40/500 [00:11<02:45,  2.78it/s]
[TorchDR] DR Loss : 5.68e+00 | Grad norm : 2.69e+00 :   8%|▊         | 41/500 [00:11<03:13,  2.38it/s]
[TorchDR] DR Loss : 5.63e+00 | Grad norm : 2.69e+00 :   8%|▊         | 41/500 [00:12<03:13,  2.38it/s]
[TorchDR] DR Loss : 5.63e+00 | Grad norm : 2.69e+00 :   8%|▊         | 42/500 [00:12<03:14,  2.36it/s]
[TorchDR] DR Loss : 5.58e+00 | Grad norm : 2.69e+00 :   8%|▊         | 42/500 [00:12<03:14,  2.36it/s]
[TorchDR] DR Loss : 5.58e+00 | Grad norm : 2.69e+00 :   9%|▊         | 43/500 [00:12<03:10,  2.40it/s]
[TorchDR] DR Loss : 5.53e+00 | Grad norm : 2.69e+00 :   9%|▊         | 43/500 [00:12<03:10,  2.40it/s]
[TorchDR] DR Loss : 5.53e+00 | Grad norm : 2.69e+00 :   9%|▉         | 44/500 [00:12<02:54,  2.62it/s]
[TorchDR] DR Loss : 5.48e+00 | Grad norm : 2.69e+00 :   9%|▉         | 44/500 [00:13<02:54,  2.62it/s]
[TorchDR] DR Loss : 5.48e+00 | Grad norm : 2.69e+00 :   9%|▉         | 45/500 [00:13<02:42,  2.80it/s]
[TorchDR] DR Loss : 5.43e+00 | Grad norm : 2.69e+00 :   9%|▉         | 45/500 [00:13<02:42,  2.80it/s]
[TorchDR] DR Loss : 5.43e+00 | Grad norm : 2.69e+00 :   9%|▉         | 46/500 [00:13<02:34,  2.93it/s]
[TorchDR] DR Loss : 5.39e+00 | Grad norm : 2.69e+00 :   9%|▉         | 46/500 [00:13<02:34,  2.93it/s]
[TorchDR] DR Loss : 5.39e+00 | Grad norm : 2.69e+00 :   9%|▉         | 47/500 [00:13<02:28,  3.05it/s]
[TorchDR] DR Loss : 5.35e+00 | Grad norm : 2.69e+00 :   9%|▉         | 47/500 [00:14<02:28,  3.05it/s]
[TorchDR] DR Loss : 5.35e+00 | Grad norm : 2.69e+00 :  10%|▉         | 48/500 [00:14<02:24,  3.14it/s]
[TorchDR] DR Loss : 5.31e+00 | Grad norm : 2.69e+00 :  10%|▉         | 48/500 [00:14<02:24,  3.14it/s]
[TorchDR] DR Loss : 5.31e+00 | Grad norm : 2.69e+00 :  10%|▉         | 49/500 [00:14<02:21,  3.19it/s]
[TorchDR] DR Loss : 5.28e+00 | Grad norm : 2.69e+00 :  10%|▉         | 49/500 [00:14<02:21,  3.19it/s]
[TorchDR] DR Loss : 5.28e+00 | Grad norm : 2.69e+00 :  10%|█         | 50/500 [00:14<02:19,  3.22it/s]
[TorchDR] DR Loss : 5.25e+00 | Grad norm : 5.96e-01 :  10%|█         | 50/500 [00:15<02:19,  3.22it/s]
[TorchDR] DR Loss : 5.25e+00 | Grad norm : 5.96e-01 :  10%|█         | 51/500 [00:15<02:18,  3.25it/s]
[TorchDR] DR Loss : 5.21e+00 | Grad norm : 5.96e-01 :  10%|█         | 51/500 [00:15<02:18,  3.25it/s]
[TorchDR] DR Loss : 5.21e+00 | Grad norm : 5.96e-01 :  10%|█         | 52/500 [00:15<02:16,  3.27it/s]
[TorchDR] DR Loss : 5.18e+00 | Grad norm : 5.96e-01 :  10%|█         | 52/500 [00:15<02:16,  3.27it/s]
[TorchDR] DR Loss : 5.18e+00 | Grad norm : 5.96e-01 :  11%|█         | 53/500 [00:15<02:15,  3.29it/s]
[TorchDR] DR Loss : 5.14e+00 | Grad norm : 5.96e-01 :  11%|█         | 53/500 [00:15<02:15,  3.29it/s]
[TorchDR] DR Loss : 5.14e+00 | Grad norm : 5.96e-01 :  11%|█         | 54/500 [00:15<02:27,  3.02it/s]
[TorchDR] DR Loss : 5.11e+00 | Grad norm : 5.96e-01 :  11%|█         | 54/500 [00:16<02:27,  3.02it/s]
[TorchDR] DR Loss : 5.11e+00 | Grad norm : 5.96e-01 :  11%|█         | 55/500 [00:16<02:23,  3.09it/s]
[TorchDR] DR Loss : 5.08e+00 | Grad norm : 5.96e-01 :  11%|█         | 55/500 [00:16<02:23,  3.09it/s]
[TorchDR] DR Loss : 5.08e+00 | Grad norm : 5.96e-01 :  11%|█         | 56/500 [00:16<02:46,  2.66it/s]
[TorchDR] DR Loss : 5.05e+00 | Grad norm : 5.96e-01 :  11%|█         | 56/500 [00:17<02:46,  2.66it/s]
[TorchDR] DR Loss : 5.05e+00 | Grad norm : 5.96e-01 :  11%|█▏        | 57/500 [00:17<02:49,  2.62it/s]
[TorchDR] DR Loss : 5.02e+00 | Grad norm : 5.96e-01 :  11%|█▏        | 57/500 [00:17<02:49,  2.62it/s]
[TorchDR] DR Loss : 5.02e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 58/500 [00:17<02:33,  2.87it/s]
[TorchDR] DR Loss : 4.99e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 58/500 [00:17<02:33,  2.87it/s]
[TorchDR] DR Loss : 4.99e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 59/500 [00:17<02:45,  2.67it/s]
[TorchDR] DR Loss : 4.97e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 59/500 [00:18<02:45,  2.67it/s]
[TorchDR] DR Loss : 4.97e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 60/500 [00:18<02:35,  2.84it/s]
[TorchDR] DR Loss : 4.94e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 60/500 [00:18<02:35,  2.84it/s]
[TorchDR] DR Loss : 4.94e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 61/500 [00:18<02:53,  2.52it/s]
[TorchDR] DR Loss : 4.92e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 61/500 [00:18<02:53,  2.52it/s]
[TorchDR] DR Loss : 4.92e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 62/500 [00:18<02:40,  2.73it/s]
[TorchDR] DR Loss : 4.89e+00 | Grad norm : 5.96e-01 :  12%|█▏        | 62/500 [00:19<02:40,  2.73it/s]
[TorchDR] DR Loss : 4.89e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 63/500 [00:19<02:18,  3.15it/s]
[TorchDR] DR Loss : 4.86e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 63/500 [00:19<02:18,  3.15it/s]
[TorchDR] DR Loss : 4.86e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 64/500 [00:19<02:15,  3.21it/s]
[TorchDR] DR Loss : 4.84e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 64/500 [00:19<02:15,  3.21it/s]
[TorchDR] DR Loss : 4.84e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 65/500 [00:19<02:13,  3.25it/s]
[TorchDR] DR Loss : 4.81e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 65/500 [00:20<02:13,  3.25it/s]
[TorchDR] DR Loss : 4.81e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 66/500 [00:20<02:00,  3.61it/s]
[TorchDR] DR Loss : 4.79e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 66/500 [00:20<02:00,  3.61it/s]
[TorchDR] DR Loss : 4.79e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 67/500 [00:20<02:02,  3.54it/s]
[TorchDR] DR Loss : 4.77e+00 | Grad norm : 5.96e-01 :  13%|█▎        | 67/500 [00:20<02:02,  3.54it/s]
[TorchDR] DR Loss : 4.77e+00 | Grad norm : 5.96e-01 :  14%|█▎        | 68/500 [00:20<02:04,  3.48it/s]
[TorchDR] DR Loss : 4.75e+00 | Grad norm : 5.96e-01 :  14%|█▎        | 68/500 [00:20<02:04,  3.48it/s]
[TorchDR] DR Loss : 4.75e+00 | Grad norm : 5.96e-01 :  14%|█▍        | 69/500 [00:20<01:53,  3.80it/s]
[TorchDR] DR Loss : 4.72e+00 | Grad norm : 5.96e-01 :  14%|█▍        | 69/500 [00:21<01:53,  3.80it/s]
[TorchDR] DR Loss : 4.72e+00 | Grad norm : 5.96e-01 :  14%|█▍        | 70/500 [00:21<01:57,  3.67it/s]
[TorchDR] DR Loss : 4.70e+00 | Grad norm : 5.96e-01 :  14%|█▍        | 70/500 [00:21<01:57,  3.67it/s]
[TorchDR] DR Loss : 4.70e+00 | Grad norm : 5.96e-01 :  14%|█▍        | 71/500 [00:21<01:48,  3.97it/s]
[TorchDR] DR Loss : 4.68e+00 | Grad norm : 5.96e-01 :  14%|█▍        | 71/500 [00:21<01:48,  3.97it/s]
[TorchDR] DR Loss : 4.68e+00 | Grad norm : 5.96e-01 :  14%|█▍        | 72/500 [00:21<01:41,  4.23it/s]
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[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  98%|█████████▊| 492/500 [02:28<00:02,  3.65it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  98%|█████████▊| 492/500 [02:29<00:02,  3.65it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▊| 493/500 [02:29<00:01,  3.55it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▊| 493/500 [02:29<00:01,  3.55it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▉| 494/500 [02:29<00:01,  3.48it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▉| 494/500 [02:29<00:01,  3.48it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▉| 495/500 [02:29<00:01,  3.44it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▉| 495/500 [02:29<00:01,  3.44it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▉| 496/500 [02:29<00:01,  3.41it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▉| 496/500 [02:30<00:01,  3.41it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▉| 497/500 [02:30<00:00,  3.39it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 :  99%|█████████▉| 497/500 [02:30<00:00,  3.39it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 : 100%|█████████▉| 498/500 [02:30<00:00,  3.73it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 : 100%|█████████▉| 498/500 [02:30<00:00,  3.73it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 : 100%|█████████▉| 499/500 [02:30<00:00,  3.61it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 : 100%|█████████▉| 499/500 [02:30<00:00,  3.61it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 : 100%|██████████| 500/500 [02:30<00:00,  3.53it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 1.80e-05 : 100%|██████████| 500/500 [02:30<00:00,  3.31it/s]

Load the SNARE-seq dataset (gene expression) with cell type labels#

def load_numpy_from_url(url, delimiter="\t"):
    """
    Load a numpy array from a URL.

    Parameters
    ----------
    url : str
        URL to load data from.
    delimiter : str, default="\t"
        Delimiter used in the data file.

    Returns
    -------
    numpy.ndarray
        Loaded data as a numpy array.
    """
    response = urllib.request.urlopen(url)
    data = response.read().decode("utf-8")
    data = data.split("\n")
    data = [row.split(delimiter) for row in data if row]
    numpy_array = np.array(data, dtype=float)
    return numpy_array


url_x = "https://rsinghlab.github.io/SCOT/data/snare_rna.txt"
snare_data = load_numpy_from_url(url_x) / 100

url_y = "https://rsinghlab.github.io/SCOT/data/SNAREseq_types.txt"
snare_labels = load_numpy_from_url(url_y)

Computing TSNE and COSNE on SNARE-seq data#

We can now proceed to computing the two DR methods and visualizing the results on the SNARE-seq dataset

tsne_model = TSNE(verbose=True, max_iter=500)
out_tsne = tsne_model.fit_transform(snare_data)

cosne_model = COSNE(lr=1e-1, verbose=True, gamma=0.5, lambda1=0.01, max_iter=500)
out_cosne = cosne_model.fit_transform(snare_data)


fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 8))
axes[0].scatter(*out_tsne.T, c=snare_labels.squeeze(1), cmap=plt.get_cmap("rainbow"))
axes[0].set_xticks([])
axes[0].set_yticks([])
axes[0].set_title("T-SNE", fontsize=24)
plotGrid(axes[1])
axes[1].scatter(*out_cosne.T, c=snare_labels.squeeze(1), cmap=plt.get_cmap("rainbow"))
axes[1].axis("off")
axes[1].set_title("CO-SNE", fontsize=24)
plt.show()
T-SNE, CO-SNE
Random state is None
[TorchDR] Initializing DR model TSNE.
[TorchDR] Affinity : computing the Entropic Affinity matrix.
[TorchDR] Affinity : sparsity mode enabled, computing 90 nearest neighbors. If this step is too slow, consider reducing the dimensionality of the data or disabling sparsity.

  0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  3.40e-01 (std =  8.51e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  2.16e-01 (std =  7.86e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  1.31e-01 (std =  6.42e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  1.31e-01 (std =  6.42e-02) :   3%|▎         | 3/100 [00:00<00:03, 29.64it/s]
[TorchDR] Root search : mean abs value =  7.78e-02 (std =  4.84e-02) :   3%|▎         | 3/100 [00:00<00:03, 29.64it/s]
[TorchDR] Root search : mean abs value =  4.59e-02 (std =  3.47e-02) :   3%|▎         | 3/100 [00:00<00:03, 29.64it/s]
[TorchDR] Root search : mean abs value =  2.72e-02 (std =  2.42e-02) :   3%|▎         | 3/100 [00:00<00:03, 29.64it/s]
[TorchDR] Root search : mean abs value =  2.72e-02 (std =  2.42e-02) :   6%|▌         | 6/100 [00:00<00:03, 29.75it/s]
[TorchDR] Root search : mean abs value =  1.62e-02 (std =  1.66e-02) :   6%|▌         | 6/100 [00:00<00:03, 29.75it/s]
[TorchDR] Root search : mean abs value =  9.73e-03 (std =  1.13e-02) :   6%|▌         | 6/100 [00:00<00:03, 29.75it/s]
[TorchDR] Root search : mean abs value =  5.90e-03 (std =  7.69e-03) :   6%|▌         | 6/100 [00:00<00:03, 29.75it/s]
[TorchDR] Root search : mean abs value =  3.60e-03 (std =  5.23e-03) :   6%|▌         | 6/100 [00:00<00:03, 29.75it/s]
[TorchDR] Root search : mean abs value =  3.60e-03 (std =  5.23e-03) :  10%|█         | 10/100 [00:00<00:03, 26.10it/s]
[TorchDR] Root search : mean abs value =  2.22e-03 (std =  3.56e-03) :  10%|█         | 10/100 [00:00<00:03, 26.10it/s]
[TorchDR] Root search : mean abs value =  1.38e-03 (std =  2.43e-03) :  10%|█         | 10/100 [00:00<00:03, 26.10it/s]
[TorchDR] Root search : mean abs value =  8.67e-04 (std =  1.66e-03) :  10%|█         | 10/100 [00:00<00:03, 26.10it/s]
[TorchDR] Root search : mean abs value =  8.67e-04 (std =  1.66e-03) :  13%|█▎        | 13/100 [00:00<00:03, 24.96it/s]
[TorchDR] Root search : mean abs value =  5.47e-04 (std =  1.14e-03) :  13%|█▎        | 13/100 [00:00<00:03, 24.96it/s]
[TorchDR] Root search : mean abs value =  3.48e-04 (std =  7.87e-04) :  13%|█▎        | 13/100 [00:00<00:03, 24.96it/s]
[TorchDR] Root search : mean abs value =  2.23e-04 (std =  5.45e-04) :  13%|█▎        | 13/100 [00:00<00:03, 24.96it/s]
[TorchDR] Root search : mean abs value =  1.44e-04 (std =  3.79e-04) :  13%|█▎        | 13/100 [00:00<00:03, 24.96it/s]
[TorchDR] Root search : mean abs value =  9.31e-05 (std =  2.65e-04) :  13%|█▎        | 13/100 [00:00<00:03, 24.96it/s]
[TorchDR] Root search : mean abs value =  9.31e-05 (std =  2.65e-04) :  18%|█▊        | 18/100 [00:00<00:02, 32.42it/s]
[TorchDR] Root search : mean abs value =  6.07e-05 (std =  1.86e-04) :  18%|█▊        | 18/100 [00:00<00:02, 32.42it/s]
[TorchDR] Root search : mean abs value =  3.98e-05 (std =  1.31e-04) :  18%|█▊        | 18/100 [00:00<00:02, 32.42it/s]
[TorchDR] Root search : mean abs value =  2.62e-05 (std =  9.25e-05) :  18%|█▊        | 18/100 [00:00<00:02, 32.42it/s]
[TorchDR] Root search : mean abs value =  1.73e-05 (std =  6.56e-05) :  18%|█▊        | 18/100 [00:00<00:02, 32.42it/s]
[TorchDR] Root search : mean abs value =  1.73e-05 (std =  6.56e-05) :  22%|██▏       | 22/100 [00:00<00:02, 28.66it/s]
[TorchDR] Root search : mean abs value =  1.15e-05 (std =  4.67e-05) :  22%|██▏       | 22/100 [00:00<00:02, 28.66it/s]
[TorchDR] Root search : mean abs value =  1.15e-05 (std =  4.67e-05) :  23%|██▎       | 23/100 [00:00<00:02, 28.71it/s]
Random state is None

  0%|          | 0/500 [00:00<?, ?it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   0%|          | 0/500 [00:00<?, ?it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   0%|          | 1/500 [00:00<01:54,  4.35it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   0%|          | 1/500 [00:00<01:54,  4.35it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   0%|          | 2/500 [00:00<01:46,  4.67it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   0%|          | 2/500 [00:00<01:46,  4.67it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|          | 3/500 [00:00<02:21,  3.52it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|          | 3/500 [00:01<02:21,  3.52it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|          | 4/500 [00:01<02:10,  3.80it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|          | 4/500 [00:01<02:10,  3.80it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|          | 5/500 [00:01<02:11,  3.77it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|          | 5/500 [00:01<02:11,  3.77it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|          | 6/500 [00:01<02:05,  3.94it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|          | 6/500 [00:01<02:05,  3.94it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|▏         | 7/500 [00:01<02:23,  3.43it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   1%|▏         | 7/500 [00:02<02:23,  3.43it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   2%|▏         | 8/500 [00:02<02:29,  3.29it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   2%|▏         | 8/500 [00:02<02:29,  3.29it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   2%|▏         | 9/500 [00:02<02:28,  3.31it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   2%|▏         | 9/500 [00:02<02:28,  3.31it/s]
[TorchDR] DR Loss : 1.39e+01 | Grad norm : 2.30e-05 :   2%|▏         | 10/500 [00:02<02:13,  3.68it/s]
[TorchDR] DR Loss : 1.41e+01 | Grad norm : 2.30e-05 :   2%|▏         | 10/500 [00:03<02:13,  3.68it/s]
[TorchDR] DR Loss : 1.41e+01 | Grad norm : 2.30e-05 :   2%|▏         | 11/500 [00:03<02:16,  3.58it/s]
[TorchDR] DR Loss : 1.43e+01 | Grad norm : 2.30e-05 :   2%|▏         | 11/500 [00:03<02:16,  3.58it/s]
[TorchDR] DR Loss : 1.43e+01 | Grad norm : 2.30e-05 :   2%|▏         | 12/500 [00:03<02:04,  3.91it/s]
[TorchDR] DR Loss : 1.44e+01 | Grad norm : 2.30e-05 :   2%|▏         | 12/500 [00:03<02:04,  3.91it/s]
[TorchDR] DR Loss : 1.44e+01 | Grad norm : 2.30e-05 :   3%|▎         | 13/500 [00:03<01:56,  4.17it/s]
[TorchDR] DR Loss : 1.46e+01 | Grad norm : 2.30e-05 :   3%|▎         | 13/500 [00:03<01:56,  4.17it/s]
[TorchDR] DR Loss : 1.46e+01 | Grad norm : 2.30e-05 :   3%|▎         | 14/500 [00:03<01:50,  4.40it/s]
[TorchDR] DR Loss : 1.48e+01 | Grad norm : 2.30e-05 :   3%|▎         | 14/500 [00:03<01:50,  4.40it/s]
[TorchDR] DR Loss : 1.48e+01 | Grad norm : 2.30e-05 :   3%|▎         | 15/500 [00:03<02:00,  4.03it/s]
[TorchDR] DR Loss : 1.48e+01 | Grad norm : 2.30e-05 :   3%|▎         | 15/500 [00:04<02:00,  4.03it/s]
[TorchDR] DR Loss : 1.48e+01 | Grad norm : 2.30e-05 :   3%|▎         | 16/500 [00:04<01:53,  4.27it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   3%|▎         | 16/500 [00:04<01:53,  4.27it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   3%|▎         | 17/500 [00:04<01:48,  4.47it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   3%|▎         | 17/500 [00:04<01:48,  4.47it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   4%|▎         | 18/500 [00:04<01:44,  4.61it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   4%|▎         | 18/500 [00:04<01:44,  4.61it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   4%|▍         | 19/500 [00:04<01:41,  4.72it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   4%|▍         | 19/500 [00:04<01:41,  4.72it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   4%|▍         | 20/500 [00:05<01:49,  4.37it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   4%|▍         | 20/500 [00:05<01:49,  4.37it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   4%|▍         | 21/500 [00:05<01:49,  4.36it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   4%|▍         | 21/500 [00:05<01:49,  4.36it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   4%|▍         | 22/500 [00:05<01:59,  3.99it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   4%|▍         | 22/500 [00:05<01:59,  3.99it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▍         | 23/500 [00:05<01:52,  4.23it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▍         | 23/500 [00:05<01:52,  4.23it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▍         | 24/500 [00:05<01:47,  4.44it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▍         | 24/500 [00:06<01:47,  4.44it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▌         | 25/500 [00:06<02:11,  3.61it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▌         | 25/500 [00:06<02:11,  3.61it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▌         | 26/500 [00:06<02:00,  3.93it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▌         | 26/500 [00:06<02:00,  3.93it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▌         | 27/500 [00:06<02:06,  3.73it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   5%|▌         | 27/500 [00:07<02:06,  3.73it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▌         | 28/500 [00:07<02:10,  3.61it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▌         | 28/500 [00:07<02:10,  3.61it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▌         | 29/500 [00:07<02:09,  3.64it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▌         | 29/500 [00:07<02:09,  3.64it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▌         | 30/500 [00:07<02:02,  3.83it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▌         | 30/500 [00:07<02:02,  3.83it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▌         | 31/500 [00:07<01:54,  4.10it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▌         | 31/500 [00:08<01:54,  4.10it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▋         | 32/500 [00:08<02:01,  3.84it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   6%|▋         | 32/500 [00:08<02:01,  3.84it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   7%|▋         | 33/500 [00:08<02:02,  3.80it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   7%|▋         | 33/500 [00:08<02:02,  3.80it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   7%|▋         | 34/500 [00:08<01:57,  3.96it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   7%|▋         | 34/500 [00:08<01:57,  3.96it/s]
[TorchDR] DR Loss : 1.50e+01 | Grad norm : 2.30e-05 :   7%|▋         | 35/500 [00:08<01:50,  4.20it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   7%|▋         | 35/500 [00:09<01:50,  4.20it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   7%|▋         | 36/500 [00:09<02:13,  3.49it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   7%|▋         | 36/500 [00:09<02:13,  3.49it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   7%|▋         | 37/500 [00:09<02:14,  3.45it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   7%|▋         | 37/500 [00:09<02:14,  3.45it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   8%|▊         | 38/500 [00:09<02:01,  3.80it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   8%|▊         | 38/500 [00:10<02:01,  3.80it/s]
[TorchDR] DR Loss : 1.49e+01 | Grad norm : 2.30e-05 :   8%|▊         | 39/500 [00:10<02:30,  3.07it/s]
[TorchDR] DR Loss : 1.48e+01 | Grad norm : 2.30e-05 :   8%|▊         | 39/500 [00:10<02:30,  3.07it/s]
[TorchDR] DR Loss : 1.48e+01 | Grad norm : 2.30e-05 :   8%|▊         | 40/500 [00:10<02:26,  3.14it/s]
[TorchDR] DR Loss : 1.48e+01 | Grad norm : 2.30e-05 :   8%|▊         | 40/500 [00:10<02:26,  3.14it/s]
[TorchDR] DR Loss : 1.48e+01 | Grad norm : 2.30e-05 :   8%|▊         | 41/500 [00:10<02:37,  2.92it/s]
[TorchDR] DR Loss : 1.48e+01 | Grad norm : 2.30e-05 :   8%|▊         | 41/500 [00:11<02:37,  2.92it/s]
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[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  93%|█████████▎| 464/500 [02:26<00:27,  1.32it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  93%|█████████▎| 464/500 [02:27<00:27,  1.32it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  93%|█████████▎| 465/500 [02:27<00:27,  1.26it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  93%|█████████▎| 465/500 [02:27<00:27,  1.26it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  93%|█████████▎| 466/500 [02:27<00:24,  1.40it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  93%|█████████▎| 466/500 [02:28<00:24,  1.40it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  93%|█████████▎| 467/500 [02:28<00:22,  1.47it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  93%|█████████▎| 467/500 [02:28<00:22,  1.47it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▎| 468/500 [02:28<00:18,  1.77it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▎| 468/500 [02:28<00:18,  1.77it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▍| 469/500 [02:28<00:15,  2.06it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▍| 469/500 [02:29<00:15,  2.06it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▍| 470/500 [02:29<00:12,  2.50it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▍| 470/500 [02:29<00:12,  2.50it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▍| 471/500 [02:29<00:10,  2.70it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▍| 471/500 [02:29<00:10,  2.70it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▍| 472/500 [02:29<00:09,  2.86it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  94%|█████████▍| 472/500 [02:30<00:09,  2.86it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▍| 473/500 [02:30<00:09,  3.00it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▍| 473/500 [02:30<00:09,  3.00it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▍| 474/500 [02:30<00:07,  3.40it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▍| 474/500 [02:30<00:07,  3.40it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▌| 475/500 [02:30<00:07,  3.39it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▌| 475/500 [02:30<00:07,  3.39it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▌| 476/500 [02:30<00:06,  3.74it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▌| 476/500 [02:30<00:06,  3.74it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▌| 477/500 [02:30<00:05,  4.04it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  95%|█████████▌| 477/500 [02:31<00:05,  4.04it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▌| 478/500 [02:31<00:05,  3.95it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▌| 478/500 [02:31<00:05,  3.95it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▌| 479/500 [02:31<00:05,  4.06it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▌| 479/500 [02:31<00:05,  4.06it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▌| 480/500 [02:31<00:04,  4.46it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▌| 480/500 [02:31<00:04,  4.46it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▌| 481/500 [02:31<00:04,  4.62it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▌| 481/500 [02:32<00:04,  4.62it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▋| 482/500 [02:32<00:04,  3.98it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  96%|█████████▋| 482/500 [02:32<00:04,  3.98it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 483/500 [02:32<00:04,  3.90it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 483/500 [02:32<00:04,  3.90it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 484/500 [02:32<00:03,  4.02it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 484/500 [02:32<00:03,  4.02it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 485/500 [02:32<00:03,  3.78it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 485/500 [02:33<00:03,  3.78it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 486/500 [02:33<00:03,  3.76it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 486/500 [02:33<00:03,  3.76it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 487/500 [02:33<00:03,  3.91it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  97%|█████████▋| 487/500 [02:33<00:03,  3.91it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 488/500 [02:33<00:03,  3.35it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 488/500 [02:34<00:03,  3.35it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 489/500 [02:34<00:03,  3.34it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 489/500 [02:34<00:03,  3.34it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 490/500 [02:34<00:02,  3.44it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 490/500 [02:34<00:02,  3.44it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 491/500 [02:34<00:02,  3.31it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 491/500 [02:35<00:02,  3.31it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 492/500 [02:35<00:02,  3.32it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  98%|█████████▊| 492/500 [02:35<00:02,  3.32it/s]
[TorchDR] DR Loss : 1.20e+01 | Grad norm : 3.23e-03 :  99%|█████████▊| 493/500 [02:35<00:01,  3.69it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 :  99%|█████████▊| 493/500 [02:35<00:01,  3.69it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 :  99%|█████████▉| 494/500 [02:35<00:01,  3.57it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 :  99%|█████████▉| 494/500 [02:35<00:01,  3.57it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 :  99%|█████████▉| 495/500 [02:35<00:01,  3.50it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 :  99%|█████████▉| 495/500 [02:36<00:01,  3.50it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 :  99%|█████████▉| 496/500 [02:36<00:01,  3.46it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 :  99%|█████████▉| 496/500 [02:36<00:01,  3.46it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 :  99%|█████████▉| 497/500 [02:36<00:00,  3.79it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 :  99%|█████████▉| 497/500 [02:36<00:00,  3.79it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 : 100%|█████████▉| 498/500 [02:36<00:00,  3.77it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 : 100%|█████████▉| 498/500 [02:36<00:00,  3.77it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 : 100%|█████████▉| 499/500 [02:36<00:00,  4.06it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 : 100%|█████████▉| 499/500 [02:37<00:00,  4.06it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 : 100%|██████████| 500/500 [02:37<00:00,  4.32it/s]
[TorchDR] DR Loss : 1.19e+01 | Grad norm : 3.23e-03 : 100%|██████████| 500/500 [02:37<00:00,  3.18it/s]
Random state is None
[TorchDR] Initializing DR model COSNE.
[TorchDR] Affinity : computing the Entropic Affinity matrix.
[TorchDR] Affinity : sparsity mode enabled, computing 90 nearest neighbors. If this step is too slow, consider reducing the dimensionality of the data or disabling sparsity.

  0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  3.40e-01 (std =  8.51e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  2.16e-01 (std =  7.86e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  1.31e-01 (std =  6.42e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  7.78e-02 (std =  4.84e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  4.59e-02 (std =  3.47e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  2.72e-02 (std =  2.42e-02) :   0%|          | 0/100 [00:00<?, ?it/s]
[TorchDR] Root search : mean abs value =  2.72e-02 (std =  2.42e-02) :   6%|▌         | 6/100 [00:00<00:02, 36.22it/s]
[TorchDR] Root search : mean abs value =  1.62e-02 (std =  1.66e-02) :   6%|▌         | 6/100 [00:00<00:02, 36.22it/s]
[TorchDR] Root search : mean abs value =  9.73e-03 (std =  1.13e-02) :   6%|▌         | 6/100 [00:00<00:02, 36.22it/s]
[TorchDR] Root search : mean abs value =  5.90e-03 (std =  7.69e-03) :   6%|▌         | 6/100 [00:00<00:02, 36.22it/s]
[TorchDR] Root search : mean abs value =  3.60e-03 (std =  5.23e-03) :   6%|▌         | 6/100 [00:00<00:02, 36.22it/s]
[TorchDR] Root search : mean abs value =  3.60e-03 (std =  5.23e-03) :  10%|█         | 10/100 [00:00<00:02, 33.54it/s]
[TorchDR] Root search : mean abs value =  2.22e-03 (std =  3.56e-03) :  10%|█         | 10/100 [00:00<00:02, 33.54it/s]
[TorchDR] Root search : mean abs value =  1.38e-03 (std =  2.43e-03) :  10%|█         | 10/100 [00:00<00:02, 33.54it/s]
[TorchDR] Root search : mean abs value =  8.67e-04 (std =  1.66e-03) :  10%|█         | 10/100 [00:00<00:02, 33.54it/s]
[TorchDR] Root search : mean abs value =  5.47e-04 (std =  1.14e-03) :  10%|█         | 10/100 [00:00<00:02, 33.54it/s]
[TorchDR] Root search : mean abs value =  3.48e-04 (std =  7.87e-04) :  10%|█         | 10/100 [00:00<00:02, 33.54it/s]
[TorchDR] Root search : mean abs value =  2.23e-04 (std =  5.45e-04) :  10%|█         | 10/100 [00:00<00:02, 33.54it/s]
[TorchDR] Root search : mean abs value =  2.23e-04 (std =  5.45e-04) :  16%|█▌        | 16/100 [00:00<00:01, 43.13it/s]
[TorchDR] Root search : mean abs value =  1.44e-04 (std =  3.79e-04) :  16%|█▌        | 16/100 [00:00<00:01, 43.13it/s]
[TorchDR] Root search : mean abs value =  9.31e-05 (std =  2.65e-04) :  16%|█▌        | 16/100 [00:00<00:01, 43.13it/s]
[TorchDR] Root search : mean abs value =  6.07e-05 (std =  1.86e-04) :  16%|█▌        | 16/100 [00:00<00:01, 43.13it/s]
[TorchDR] Root search : mean abs value =  3.98e-05 (std =  1.31e-04) :  16%|█▌        | 16/100 [00:00<00:01, 43.13it/s]
[TorchDR] Root search : mean abs value =  2.62e-05 (std =  9.25e-05) :  16%|█▌        | 16/100 [00:00<00:01, 43.13it/s]
[TorchDR] Root search : mean abs value =  2.62e-05 (std =  9.25e-05) :  21%|██        | 21/100 [00:00<00:01, 45.49it/s]
[TorchDR] Root search : mean abs value =  1.73e-05 (std =  6.56e-05) :  21%|██        | 21/100 [00:00<00:01, 45.49it/s]
[TorchDR] Root search : mean abs value =  1.15e-05 (std =  4.67e-05) :  21%|██        | 21/100 [00:00<00:01, 45.49it/s]
[TorchDR] Root search : mean abs value =  1.15e-05 (std =  4.67e-05) :  23%|██▎       | 23/100 [00:00<00:01, 46.24it/s]

  0%|          | 0/500 [00:00<?, ?it/s]
[TorchDR] DR Loss : 3.45e+01 | Grad norm : 2.15e+00 :   0%|          | 0/500 [00:00<?, ?it/s]
[TorchDR] DR Loss : 3.45e+01 | Grad norm : 2.15e+00 :   0%|          | 1/500 [00:00<04:22,  1.90it/s]
[TorchDR] DR Loss : 3.29e+01 | Grad norm : 2.15e+00 :   0%|          | 1/500 [00:01<04:22,  1.90it/s]
[TorchDR] DR Loss : 3.29e+01 | Grad norm : 2.15e+00 :   0%|          | 2/500 [00:01<05:13,  1.59it/s]
[TorchDR] DR Loss : 3.12e+01 | Grad norm : 2.15e+00 :   0%|          | 2/500 [00:01<05:13,  1.59it/s]
[TorchDR] DR Loss : 3.12e+01 | Grad norm : 2.15e+00 :   1%|          | 3/500 [00:01<04:37,  1.79it/s]
[TorchDR] DR Loss : 2.96e+01 | Grad norm : 2.15e+00 :   1%|          | 3/500 [00:02<04:37,  1.79it/s]
[TorchDR] DR Loss : 2.96e+01 | Grad norm : 2.15e+00 :   1%|          | 4/500 [00:02<05:10,  1.60it/s]
[TorchDR] DR Loss : 2.79e+01 | Grad norm : 2.15e+00 :   1%|          | 4/500 [00:03<05:10,  1.60it/s]
[TorchDR] DR Loss : 2.79e+01 | Grad norm : 2.15e+00 :   1%|          | 5/500 [00:03<07:09,  1.15it/s]
[TorchDR] DR Loss : 2.62e+01 | Grad norm : 2.15e+00 :   1%|          | 5/500 [00:04<07:09,  1.15it/s]
[TorchDR] DR Loss : 2.62e+01 | Grad norm : 2.15e+00 :   1%|          | 6/500 [00:04<07:26,  1.11it/s]
[TorchDR] DR Loss : 2.46e+01 | Grad norm : 2.15e+00 :   1%|          | 6/500 [00:05<07:26,  1.11it/s]
[TorchDR] DR Loss : 2.46e+01 | Grad norm : 2.15e+00 :   1%|▏         | 7/500 [00:05<06:52,  1.20it/s]
[TorchDR] DR Loss : 2.29e+01 | Grad norm : 2.15e+00 :   1%|▏         | 7/500 [00:06<06:52,  1.20it/s]
[TorchDR] DR Loss : 2.29e+01 | Grad norm : 2.15e+00 :   2%|▏         | 8/500 [00:06<07:01,  1.17it/s]
[TorchDR] DR Loss : 2.13e+01 | Grad norm : 2.15e+00 :   2%|▏         | 8/500 [00:07<07:01,  1.17it/s]
[TorchDR] DR Loss : 2.13e+01 | Grad norm : 2.15e+00 :   2%|▏         | 9/500 [00:07<07:26,  1.10it/s]
[TorchDR] DR Loss : 1.98e+01 | Grad norm : 2.15e+00 :   2%|▏         | 9/500 [00:08<07:26,  1.10it/s]
[TorchDR] DR Loss : 1.98e+01 | Grad norm : 2.15e+00 :   2%|▏         | 10/500 [00:08<07:24,  1.10it/s]
[TorchDR] DR Loss : 1.83e+01 | Grad norm : 2.15e+00 :   2%|▏         | 10/500 [00:08<07:24,  1.10it/s]
[TorchDR] DR Loss : 1.83e+01 | Grad norm : 2.15e+00 :   2%|▏         | 11/500 [00:09<07:07,  1.14it/s]
[TorchDR] DR Loss : 1.69e+01 | Grad norm : 2.15e+00 :   2%|▏         | 11/500 [00:09<07:07,  1.14it/s]
[TorchDR] DR Loss : 1.69e+01 | Grad norm : 2.15e+00 :   2%|▏         | 12/500 [00:09<06:40,  1.22it/s]
[TorchDR] DR Loss : 1.56e+01 | Grad norm : 2.15e+00 :   2%|▏         | 12/500 [00:10<06:40,  1.22it/s]
[TorchDR] DR Loss : 1.56e+01 | Grad norm : 2.15e+00 :   3%|▎         | 13/500 [00:10<06:36,  1.23it/s]
[TorchDR] DR Loss : 1.44e+01 | Grad norm : 2.15e+00 :   3%|▎         | 13/500 [00:11<06:36,  1.23it/s]
[TorchDR] DR Loss : 1.44e+01 | Grad norm : 2.15e+00 :   3%|▎         | 14/500 [00:11<06:33,  1.23it/s]
[TorchDR] DR Loss : 1.33e+01 | Grad norm : 2.15e+00 :   3%|▎         | 14/500 [00:12<06:33,  1.23it/s]
[TorchDR] DR Loss : 1.33e+01 | Grad norm : 2.15e+00 :   3%|▎         | 15/500 [00:12<06:17,  1.29it/s]
[TorchDR] DR Loss : 1.23e+01 | Grad norm : 2.15e+00 :   3%|▎         | 15/500 [00:12<06:17,  1.29it/s]
[TorchDR] DR Loss : 1.23e+01 | Grad norm : 2.15e+00 :   3%|▎         | 16/500 [00:12<06:00,  1.34it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 2.15e+00 :   3%|▎         | 16/500 [00:13<06:00,  1.34it/s]
[TorchDR] DR Loss : 1.14e+01 | Grad norm : 2.15e+00 :   3%|▎         | 17/500 [00:13<05:53,  1.37it/s]
[TorchDR] DR Loss : 1.07e+01 | Grad norm : 2.15e+00 :   3%|▎         | 17/500 [00:14<05:53,  1.37it/s]
[TorchDR] DR Loss : 1.07e+01 | Grad norm : 2.15e+00 :   4%|▎         | 18/500 [00:14<06:02,  1.33it/s]
[TorchDR] DR Loss : 1.01e+01 | Grad norm : 2.15e+00 :   4%|▎         | 18/500 [00:15<06:02,  1.33it/s]
[TorchDR] DR Loss : 1.01e+01 | Grad norm : 2.15e+00 :   4%|▍         | 19/500 [00:15<06:56,  1.16it/s]
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[TorchDR] DR Loss : 8.62e+00 | Grad norm : 2.15e+00 :   4%|▍         | 22/500 [00:18<06:08,  1.30it/s]
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[TorchDR] DR Loss : 8.40e+00 | Grad norm : 2.15e+00 :   5%|▍         | 23/500 [00:19<06:12,  1.28it/s]
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[TorchDR] DR Loss : 8.23e+00 | Grad norm : 2.15e+00 :   5%|▍         | 24/500 [00:19<06:14,  1.27it/s]
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[TorchDR] DR Loss : 7.28e+00 | Grad norm : 2.15e+00 :   6%|▌         | 31/500 [00:24<05:49,  1.34it/s]
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[TorchDR] DR Loss : 7.16e+00 | Grad norm : 2.15e+00 :   6%|▋         | 32/500 [00:25<05:56,  1.31it/s]
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[TorchDR] DR Loss : 6.90e+00 | Grad norm : 2.15e+00 :   7%|▋         | 36/500 [00:27<05:21,  1.45it/s]
[TorchDR] DR Loss : 6.84e+00 | Grad norm : 2.15e+00 :   7%|▋         | 36/500 [00:27<05:21,  1.45it/s]
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[TorchDR] DR Loss : 6.78e+00 | Grad norm : 2.15e+00 :   7%|▋         | 37/500 [00:28<05:06,  1.51it/s]
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[TorchDR] DR Loss : 6.72e+00 | Grad norm : 2.15e+00 :   8%|▊         | 38/500 [00:29<04:58,  1.55it/s]
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[TorchDR] DR Loss : 6.46e+00 | Grad norm : 2.15e+00 :   9%|▊         | 43/500 [00:32<04:40,  1.63it/s]
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[TorchDR] DR Loss : 6.40e+00 | Grad norm : 2.15e+00 :   9%|▉         | 45/500 [00:33<05:16,  1.44it/s]
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[TorchDR] DR Loss : 6.37e+00 | Grad norm : 2.15e+00 :   9%|▉         | 46/500 [00:34<05:16,  1.43it/s]
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[TorchDR] DR Loss : 6.35e+00 | Grad norm : 2.15e+00 :   9%|▉         | 47/500 [00:35<05:16,  1.43it/s]
[TorchDR] DR Loss : 6.35e+00 | Grad norm : 2.15e+00 :  10%|▉         | 48/500 [00:35<05:25,  1.39it/s]
[TorchDR] DR Loss : 6.32e+00 | Grad norm : 2.15e+00 :  10%|▉         | 48/500 [00:36<05:25,  1.39it/s]
[TorchDR] DR Loss : 6.32e+00 | Grad norm : 2.15e+00 :  10%|▉         | 49/500 [00:36<05:52,  1.28it/s]
[TorchDR] DR Loss : 6.29e+00 | Grad norm : 2.15e+00 :  10%|▉         | 49/500 [00:36<05:52,  1.28it/s]
[TorchDR] DR Loss : 6.29e+00 | Grad norm : 2.15e+00 :  10%|█         | 50/500 [00:36<05:27,  1.37it/s]
[TorchDR] DR Loss : 6.27e+00 | Grad norm : 4.75e-01 :  10%|█         | 50/500 [00:37<05:27,  1.37it/s]
[TorchDR] DR Loss : 6.27e+00 | Grad norm : 4.75e-01 :  10%|█         | 51/500 [00:37<05:10,  1.45it/s]
[TorchDR] DR Loss : 6.24e+00 | Grad norm : 4.75e-01 :  10%|█         | 51/500 [00:38<05:10,  1.45it/s]
[TorchDR] DR Loss : 6.24e+00 | Grad norm : 4.75e-01 :  10%|█         | 52/500 [00:38<05:19,  1.40it/s]
[TorchDR] DR Loss : 6.21e+00 | Grad norm : 4.75e-01 :  10%|█         | 52/500 [00:38<05:19,  1.40it/s]
[TorchDR] DR Loss : 6.21e+00 | Grad norm : 4.75e-01 :  11%|█         | 53/500 [00:38<05:08,  1.45it/s]
[TorchDR] DR Loss : 6.19e+00 | Grad norm : 4.75e-01 :  11%|█         | 53/500 [00:39<05:08,  1.45it/s]
[TorchDR] DR Loss : 6.19e+00 | Grad norm : 4.75e-01 :  11%|█         | 54/500 [00:39<04:55,  1.51it/s]
[TorchDR] DR Loss : 6.16e+00 | Grad norm : 4.75e-01 :  11%|█         | 54/500 [00:40<04:55,  1.51it/s]
[TorchDR] DR Loss : 6.16e+00 | Grad norm : 4.75e-01 :  11%|█         | 55/500 [00:40<04:59,  1.49it/s]
[TorchDR] DR Loss : 6.14e+00 | Grad norm : 4.75e-01 :  11%|█         | 55/500 [00:40<04:59,  1.49it/s]
[TorchDR] DR Loss : 6.14e+00 | Grad norm : 4.75e-01 :  11%|█         | 56/500 [00:40<05:02,  1.47it/s]
[TorchDR] DR Loss : 6.12e+00 | Grad norm : 4.75e-01 :  11%|█         | 56/500 [00:41<05:02,  1.47it/s]
[TorchDR] DR Loss : 6.12e+00 | Grad norm : 4.75e-01 :  11%|█▏        | 57/500 [00:41<04:50,  1.52it/s]
[TorchDR] DR Loss : 6.11e+00 | Grad norm : 4.75e-01 :  11%|█▏        | 57/500 [00:42<04:50,  1.52it/s]
[TorchDR] DR Loss : 6.11e+00 | Grad norm : 4.75e-01 :  12%|█▏        | 58/500 [00:42<04:52,  1.51it/s]
[TorchDR] DR Loss : 6.09e+00 | Grad norm : 4.75e-01 :  12%|█▏        | 58/500 [00:42<04:52,  1.51it/s]
[TorchDR] DR Loss : 6.09e+00 | Grad norm : 4.75e-01 :  12%|█▏        | 59/500 [00:42<04:47,  1.53it/s]
[TorchDR] DR Loss : 6.07e+00 | Grad norm : 4.75e-01 :  12%|█▏        | 59/500 [00:43<04:47,  1.53it/s]
[TorchDR] DR Loss : 6.07e+00 | Grad norm : 4.75e-01 :  12%|█▏        | 60/500 [00:43<05:19,  1.38it/s]
[TorchDR] DR Loss : 6.05e+00 | Grad norm : 4.75e-01 :  12%|█▏        | 60/500 [00:44<05:19,  1.38it/s]
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[TorchDR] DR Loss : 6.04e+00 | Grad norm : 4.75e-01 :  12%|█▏        | 61/500 [00:45<05:02,  1.45it/s]
[TorchDR] DR Loss : 6.04e+00 | Grad norm : 4.75e-01 :  12%|█▏        | 62/500 [00:45<05:11,  1.40it/s]
[TorchDR] DR Loss : 6.02e+00 | Grad norm : 4.75e-01 :  12%|█▏        | 62/500 [00:45<05:11,  1.40it/s]
[TorchDR] DR Loss : 6.02e+00 | Grad norm : 4.75e-01 :  13%|█▎        | 63/500 [00:45<04:47,  1.52it/s]
[TorchDR] DR Loss : 6.00e+00 | Grad norm : 4.75e-01 :  13%|█▎        | 63/500 [00:46<04:47,  1.52it/s]
[TorchDR] DR Loss : 6.00e+00 | Grad norm : 4.75e-01 :  13%|█▎        | 64/500 [00:46<05:05,  1.43it/s]
[TorchDR] DR Loss : 5.99e+00 | Grad norm : 4.75e-01 :  13%|█▎        | 64/500 [00:47<05:05,  1.43it/s]
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[TorchDR] DR Loss : 5.97e+00 | Grad norm : 4.75e-01 :  13%|█▎        | 65/500 [00:48<05:56,  1.22it/s]
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[TorchDR] DR Loss : 5.96e+00 | Grad norm : 4.75e-01 :  13%|█▎        | 66/500 [00:48<05:53,  1.23it/s]
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[TorchDR] DR Loss : 5.94e+00 | Grad norm : 4.75e-01 :  13%|█▎        | 67/500 [00:49<05:24,  1.33it/s]
[TorchDR] DR Loss : 5.94e+00 | Grad norm : 4.75e-01 :  14%|█▎        | 68/500 [00:49<04:51,  1.48it/s]
[TorchDR] DR Loss : 5.93e+00 | Grad norm : 4.75e-01 :  14%|█▎        | 68/500 [00:49<04:51,  1.48it/s]
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[TorchDR] DR Loss : 5.91e+00 | Grad norm : 4.75e-01 :  14%|█▍        | 70/500 [00:51<04:20,  1.65it/s]
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[TorchDR] DR Loss : 5.89e+00 | Grad norm : 4.75e-01 :  14%|█▍        | 71/500 [00:51<04:19,  1.65it/s]
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[TorchDR] DR Loss : 5.88e+00 | Grad norm : 4.75e-01 :  14%|█▍        | 72/500 [00:52<04:09,  1.71it/s]
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[TorchDR] DR Loss : 5.87e+00 | Grad norm : 4.75e-01 :  15%|█▍        | 73/500 [00:53<04:32,  1.57it/s]
[TorchDR] DR Loss : 5.87e+00 | Grad norm : 4.75e-01 :  15%|█▍        | 74/500 [00:53<04:56,  1.44it/s]
[TorchDR] DR Loss : 5.86e+00 | Grad norm : 4.75e-01 :  15%|█▍        | 74/500 [00:53<04:56,  1.44it/s]
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[TorchDR] DR Loss : 5.85e+00 | Grad norm : 4.75e-01 :  15%|█▌        | 75/500 [00:54<04:43,  1.50it/s]
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[TorchDR] DR Loss : 5.82e+00 | Grad norm : 4.75e-01 :  16%|█▌        | 79/500 [00:56<04:19,  1.62it/s]
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[TorchDR] DR Loss : 5.80e+00 | Grad norm : 4.75e-01 :  16%|█▌        | 81/500 [00:58<04:05,  1.71it/s]
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[TorchDR] DR Loss : 5.79e+00 | Grad norm : 4.75e-01 :  17%|█▋        | 83/500 [00:59<04:45,  1.46it/s]
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[TorchDR] DR Loss : 5.78e+00 | Grad norm : 4.75e-01 :  17%|█▋        | 84/500 [01:00<04:25,  1.57it/s]
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[TorchDR] DR Loss : 5.77e+00 | Grad norm : 4.75e-01 :  17%|█▋        | 86/500 [01:01<04:17,  1.61it/s]
[TorchDR] DR Loss : 5.77e+00 | Grad norm : 4.75e-01 :  17%|█▋        | 87/500 [01:01<04:05,  1.68it/s]
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Total running time of the script: (12 minutes 54.890 seconds)

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