如何使用自动编码器可视化降维？ （Python | TensorFlow）

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))