How do I visualize or plot a multidimensional tensor?

Question

I was wondering if anyone here has ever tried to visualize a multidimensional tensor in numpy. If so, could you share with me how I might go about doing this? I was thinking of reducing it to a 2D visualization.

I've included some sample output. It's weirdly structured, there are ellipses "..." and it's got a 4D tensor layout [[[[ content here]]]]

Sample Data:

[[[[ -9.37186633e-05  -9.89684777e-05  -8.97786958e-05 ...,
     -1.08984910e-04  -1.07056971e-04  -8.68257193e-05]
  [[ -9.61350961e-05  -8.75062251e-05  -9.39425736e-05 ...,
     -1.17737654e-04  -9.66376538e-05  -8.78447026e-05]
   [ -1.06558400e-04  -9.04031331e-05  -1.04479543e-04 ...,
     -1.02786013e-04  -1.07974607e-04  -1.07524407e-04]]
 [[[ -1.09648725e-04  -1.01073667e-04  -9.39013553e-05 ...,
     -8.94383265e-05  -9.06078858e-05  -9.83356076e-05]
   [ -9.76310257e-05  -1.04029998e-04  -1.01905476e-04 ...,
     -9.50643880e-05  -8.29156561e-05  -9.75912480e-05]]]
   [ -1.12038200e-04  -1.00154917e-04  -9.00980813e-05 ...,
     -1.10244124e-04  -1.16597665e-04  -1.10604939e-04]]]]

Answer 1

• For plotting high dimensional data there is a technique called as T-SNE

• T-SNE is provided by tensorflow as a tesnorboard feature

• You can just provide the tensor as an embedding and run tensorboard

• You can visualize high dimensional data in either 3D or 2d

• Here is a link for Data Visualization using Tensor-board: https://github.com/jayshah19949596/Tensorboard-Visualization-Freezing-Graph

• Your code should be something like this :

    tensor_x = tf.Variable(mnist.test.images, name='images')
    config = projector.ProjectorConfig()
    # One can add multiple embeddings.
    embedding = config.embeddings.add()
    embedding.tensor_name = tensor_x.name
    # Link this tensor to its metadata file (e.g. labels).
    embedding.metadata_path = metadata
    # Saves a config file that TensorBoard will read during startup.
    projector.visualize_embeddings(tf.summary.FileWriter(logs_path), config)
  

• Tensorboard visualization:

• You can use scikit learn's TSNE to plot high dimensional data

• Below is sample coede to use scikit learn's TSNE

    # x is my data which is a nd-array
    # You have to convert your tensor to nd-array before using scikit-learn's tsne
    # Convert your tensor to x =====> x = tf.Session().run(tensor_x)
    standard = StandardScaler()
    x_std = standard.fit_transform(x)
    plt.figure()
  
    label_encoder = LabelEncoder()
    y = label_encoder.fit_transform(y)
  
    tsne = TSNE(n_components=2, random_state=0)  # n_components means you mean to plot your dimensional data to 2D
    x_test_2d = tsne.fit_transform(x_std)
  
    print()
  
    markers = ('s', 'd', 'o', '^', 'v', '8', 's', 'p', "_", '2')
    color_map = {0: 'red', 1: 'blue', 2: 'lightgreen', 3: 'purple', 4: 'cyan', 5: 'black', 6: 'yellow', 7: 'magenta',
             8: 'plum', 9: 'yellowgreen'}
    for idx, cl in enumerate(np.unique(y)):
  
        plt.scatter(x=x_test_2d[y == cl, 0], y=x_test_2d[y == cl, 1], c=color_map[idx], marker=markers[idx],
                label=cl)
    plt.xlabel('X in t-SNE')
    plt.ylabel('Y in t-SNE')
    plt.legend(loc='upper left')
    plt.title('t-SNE visualization of test data')
    plt.show()
  

• ScikitLearn's TSNE Results:

• You can also use PCA for plotting high dimensional data to 2D

• Here is implementation of PCA.

• Scikit Learn PCA: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html