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下面的代碼導入MNIST數據集並訓練堆疊降噪自動編碼器,以破壞,編碼然後解碼數據。基本上我想用它作爲非線性尺寸縮減技術。如何訪問模型編碼的較低維特徵,以便將這些特徵放入聚類模型中?理想情況下,我會期望較低的維度特徵是循環或直線(顯然這實際上並非如此)。如何使用python和tensorflow從降噪堆疊自動編碼器中提取低維特徵向量
import numpy as np
import os
import sys
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/")
def plot_image(image, shape=[28, 28]):
plt.imshow(image.reshape(shape), cmap="Greys", interpolation="nearest")
plt.axis("off")
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_seed(seed)
np.random.seed(seed)
def show_reconstructed_digits(X, outputs, model_path = None, n_test_digits = 2):
with tf.Session() as sess:
if model_path:
saver.restore(sess, model_path)
X_test = mnist.test.images[:n_test_digits]
outputs_val = outputs.eval(feed_dict={X: X_test})
fig = plt.figure(figsize=(8, 3 * n_test_digits))
for digit_index in range(n_test_digits):
plt.subplot(n_test_digits, 2, digit_index * 2 + 1)
plot_image(X_test[digit_index])
plt.subplot(n_test_digits, 2, digit_index * 2 + 2)
plot_image(outputs_val[digit_index])
reset_graph()
n_inputs = 28 * 28
n_hidden1 = 300
n_hidden2 = 150 # codings
n_hidden3 = n_hidden1
n_outputs = n_inputs
learning_rate = 0.01
noise_level = 1.0
X = tf.placeholder(tf.float32, shape=[None, n_inputs])
X_noisy = X + noise_level * tf.random_normal(tf.shape(X))
hidden1 = tf.layers.dense(X_noisy, n_hidden1, activation=tf.nn.relu,
name="hidden1")
hidden2 = tf.layers.dense(hidden1, n_hidden2, activation=tf.nn.relu, # not shown in the book
name="hidden2") # not shown
hidden3 = tf.layers.dense(hidden2, n_hidden3, activation=tf.nn.relu, # not shown
name="hidden3") # not shown
outputs = tf.layers.dense(hidden3, n_outputs, name="outputs") # not shown
reconstruction_loss = tf.reduce_mean(tf.square(outputs - X)) # MSE
optimizer = tf.train.AdamOptimizer(learning_rate)
training_op = optimizer.minimize(reconstruction_loss)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
n_epochs = 10
batch_size = 150
with tf.Session() as sess:
init.run()
for epoch in range(n_epochs):
n_batches = mnist.train.num_examples // batch_size
for iteration in range(n_batches):
print("\r{}%".format(100 * iteration // n_batches), end="")
sys.stdout.flush()
X_batch, y_batch = mnist.train.next_batch(batch_size)
sess.run(training_op, feed_dict={X: X_batch})
loss_train = reconstruction_loss.eval(feed_dict={X: X_batch})
print("\r{}".format(epoch), "Train MSE:", loss_train)
saver.save(sess, "./my_model_stacked_denoising_gaussian.ckpt")
show_reconstructed_digits(X, outputs, "./my_model_stacked_denoising_gaussian.ckpt")
Whta你的意思是循環或直線? –
例如,8的較低維度特徵是兩個循環,或者9是循環和直線 –