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如果我想將序列(特徵)A,B和C投影到帶有張量LSTM的目標序列,我怎麼能知道每個特徵對目標影響的重要性?主成分分析有幫助嗎?如果pca可以幫助,該怎麼辦?數據的RNN中的主成分分析
的結構(列)設定如下面:
A sequence
B sequence
C sequence
Target sequence
如果我想將序列(特徵)A,B和C投影到帶有張量LSTM的目標序列,我怎麼能知道每個特徵對目標影響的重要性?主成分分析有幫助嗎?如果pca可以幫助,該怎麼辦?數據的RNN中的主成分分析
的結構(列)設定如下面:
A sequence
B sequence
C sequence
Target sequence
什麼將這個序列的主成分是?你可以做的是採取的序列,B序列和C序列的PCA和可視化... 這裏是與Tensorboard可視化PCA一個簡單的教程:http://www.pinchofintelligence.com/simple-introduction-to-tensorboard-embedding-visualisation/
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import os
from tensorflow.contrib.tensorboard.plugins import projector
from tensorflow.examples.tutorials.mnist import input_data
LOG_DIR = 'minimalsample'
NAME_TO_VISUALISE_VARIABLE = "mnistembedding"
TO_EMBED_COUNT = 500
path_for_mnist_sprites = os.path.join(LOG_DIR,'mnistdigits.png')
path_for_mnist_metadata = os.path.join(LOG_DIR,'metadata.tsv')
mnist = input_data.read_data_sets("MNIST_data/", one_hot=False)
batch_xs, batch_ys = mnist.train.next_batch(TO_EMBED_COUNT)
embedding_var = tf.Variable(batch_xs, name=NAME_TO_VISUALISE_VARIABLE)
summary_writer = tf.summary.FileWriter(LOG_DIR)
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embedding_var.name
# Specify where you find the metadata
embedding.metadata_path = path_for_mnist_metadata #'metadata.tsv'
# Specify where you find the sprite (we will create this later)
embedding.sprite.image_path = path_for_mnist_sprites #'mnistdigits.png'
embedding.sprite.single_image_dim.extend([28,28])
# Say that you want to visualise the embeddings
projector.visualize_embeddings(summary_writer, config)
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.save(sess, os.path.join(LOG_DIR, "model.ckpt"), 1)
with open(path_for_mnist_metadata,'w') as f:
f.write("Index\tLabel\n")
for index,label in enumerate(batch_ys):
f.write("%d\t%d\n" % (index,label))
希望這有助於你想想PCA !
非常感謝。這項工作的主要目的是使用A,B和C預測RNN的目標序列,並定量分析(PCA)A,B,C變量對目標序列的影響。 –