如何可视化或绘制多维张量？

Question

我想知道这里有没有人试图想象一下numpy中的多维张量。如果是这样，你能和我分享我怎么做这个吗？我正在考虑将其缩减为2D可视化。

我已经包含了一些示例输出。这是奇怪的结构，有椭圆“......”，它有一个4D张量布局[[[[内容在这里]]]]

示例数据：

[[[[ -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

• 为了绘制高维数据，有一种称为T-SNE
的技术
• T-SNE由tensorflow作为tesnorboard功能提供
• 您可以将张量提供为嵌入和运行张量板
• 您可以在3D或2d
中显示高维数据
• 以下是使用Tensor-board进行数据可视化的链接：https://github.com/jayshah19949596/Tensorboard-Visualization-Freezing-Graph

• 您的代码应该是这样的：

  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可视化：

• 您可以使用scikit learn的TSNE绘制高维数据

• 以下是使用scikit learn的TSNE

  的示例

  # 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的TSNE成绩：

• 您还可以使用PCA绘制高维数据到2D