我正在尝试调整Aymeric Damien's code来可视化由 TensorFlow 中实现的自动编码器执行的降维 . 我看到的所有示例都在 mnist digits数据集上工作，但我想使用此方法将2维中的虹膜数据集可视化为玩具示例，以便我可以弄清楚如何为我的真实数据集调整它 .

我的问题是： How can one get the sample-specific 2 dimensional embeddings to visualize?

例如，iris数据集的 150 samples 与 4 attributes . 我添加了 4 noise attributes 以获得 8 attributes . 编码/解码如下： [8, 4, 2, 4, 8] 但我不知道如何提取形状 (150, 2) 数组来可视化嵌入 . 我还没有找到任何关于如何使用 TensorFlow 可视化降维的教程 .

from sklearn.datasets import load_iris

from sklearn.decomposition import PCA

import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt

%matplotlib inline

# Set random seeds

np.random.seed(0)

tf.set_random_seed(0)

# Load data

iris = load_iris()

# Original Iris : (150,4)

X_iris = iris.data

# Iris with noise : (150,8)

X_iris_with_noise = np.concatenate([X_iris, np.random.random(size=X_iris.shape)], axis=1).astype(np.float32)

y_iris = iris.target

# PCA

pca_xy = PCA(n_components=2).fit_transform(X_iris_with_noise)

with plt.style.context("seaborn-white"):

fig, ax = plt.subplots()

ax.scatter(pca_xy[:,0], pca_xy[:,1], c=y_iris, cmap=plt.cm.Set2)

ax.set_title("PCA | Iris with noise")

# Training Parameters

learning_rate = 0.01

num_steps = 1000

batch_size = 10

display_step = 250

examples_to_show = 10

# Network Parameters

num_hidden_1 = 4 # 1st layer num features

num_hidden_2 = 2 # 2nd layer num features (the latent dim)

num_input = 8 # Iris data input

# tf Graph input

X = tf.placeholder(tf.float32, [None, num_input], name="input")

weights = {

'encoder_h1': tf.Variable(tf.random_normal([num_input, num_hidden_1]), dtype=tf.float32, name="encoder_h1"),

'encoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_hidden_2]), dtype=tf.float32, name="encoder_h2"),

'decoder_h1': tf.Variable(tf.random_normal([num_hidden_2, num_hidden_1]), dtype=tf.float32, name="decoder_h1"),

'decoder_h2': tf.Variable(tf.random_normal([num_hidden_1, num_input]), dtype=tf.float32, name="decoder_h2"),

}

biases = {

'encoder_b1': tf.Variable(tf.random_normal([num_hidden_1]), dtype=tf.float32, name="encoder_b1"),

'encoder_b2': tf.Variable(tf.random_normal([num_hidden_2]), dtype=tf.float32, name="encoder_b2"),

'decoder_b1': tf.Variable(tf.random_normal([num_hidden_1]), dtype=tf.float32, name="decoder_b1"),

'decoder_b2': tf.Variable(tf.random_normal([num_input]), dtype=tf.float32, name="decoder_b2"),

}

# Building the encoder

def encoder(x):

# Encoder Hidden layer with sigmoid activation #1

layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),

biases['encoder_b1']))

# Encoder Hidden layer with sigmoid activation #2

layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),

biases['encoder_b2']))

return layer_2

# Building the decoder

def decoder(x):

# Decoder Hidden layer with sigmoid activation #1

layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),

biases['decoder_b1']))

# Decoder Hidden layer with sigmoid activation #2

layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),

biases['decoder_b2']))

return layer_2

# Construct model

encoder_op = encoder(X)

decoder_op = decoder(encoder_op)

# Prediction

y_pred = decoder_op

# Targets (Labels) are the input data.

y_true = X

# Define loss and optimizer, minimize the squared error

loss = tf.reduce_mean(tf.pow(y_true - y_pred, 2))

optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(loss)

# Initialize the variables (i.e. assign their default value)

init = tf.global_variables_initializer()

# Start Training

# Start a new TF session

with tf.Session() as sess:

# Run the initializer

sess.run(init)

# Training

for i in range(1, num_steps+1):

# Prepare Data

# Get the next batch of Iris data

idx_train = np.random.RandomState(i).choice(np.arange(X_iris_with_noise.shape[0]), size=batch_size)

batch_x = X_iris_with_noise[idx_train,:]

# Run optimization op (backprop) and cost op (to get loss value)

_, l = sess.run([optimizer, loss], feed_dict={X: batch_x})

# Display logs per step

if i % display_step == 0 or i == 1:

print('Step %i: Minibatch Loss: %f' % (i, l))