tensorlayer學習日誌7_chapter3_3.5_2~9漸變求助路過的大神

這第3.5節中的漸變，我嘗試了很多次都無法跑出來，有沒大神路過的幫忙一下？

import tensorflow as tf
import tensorlayer as tl
import numpy as np


learning_rate = 0.0001
lambda_l2_w = 0.01
n_epochs = 200
batch_size =128
print_interval = 200
hidden_size = 196
input_size = 784
image_width = 28
model = 'sigmoid'
# model = 'relu'

x = tf.placeholder(tf.float32, shape=[None, 784], name='x')

print('~~~~~~~~~~~~~~~~build network~~~~~~~~~~~~~~~~~')

if model == 'relu':
    network = tl.layers.InputLayer(x, name='input')
    network = tl.layers.DropoutLayer(network, keep=0.5, is_fix=True, name='denoising1')  #這裏要加is_fix=True，否則默認是False會報錯
    network = tl.layers.DenseLayer(network, hidden_size, tf.nn.relu, name='relu1')

    encoded_img = network.outputs
    recon_layer1 = tl.layers.DenseLayer(network, input_size, tf.nn.softplus, name='recon_layer1')

elif model == 'sigmoid':
    network = tl.layers.InputLayer(x, name='input')
    network = tl.layers.DropoutLayer(network, keep=0.5, is_fix=True, name='denoising1')  #這裏要加is_fix=True，否則默認是False會報錯
    network = tl.layers.DenseLayer(network, hidden_size, tf.nn.sigmoid, name='sigmoid1')

    encoded_img = network.outputs
    recon_layer1 = tl.layers.DenseLayer(network, input_size, tf.nn.sigmoid, name='recon_layer1')

y = recon_layer1.outputs
train_params = recon_layer1.all_params[-4:]

mse = tf.reduce_sum(tf.squared_difference(y, x), 1)
mse = tf.reduce_mean(mse)

L2_w = tf.contrib.layers.l2_regularizer(lambda_l2_w)(train_params[0] + tf.contrib.layers.l2_regularizer(lambda_l2_w)(train_params[2]))

activation_out = recon_layer1.all_layers[-2]
L1_a = 0.001 * tf.reduce_mean(activation_out)

beta = 5
rho = 0.15
p_hat = tf.reduce_mean(activation_out, 0)
KLD = beta * tf.reduce_sum(rho * tf.log(tf.divide(rho, p_hat)) + (1-rho) * tf.log((1-rho)/(tf.subtract(float(1), p_hat))))

if model == 'sigmoid':
    cost = mse + L2_w + KLD
if model == 'relu':
    cost = mse +L2_w +L1_a

train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)

saver = tf.train.Saver()

# print('~~~~~~~~~~~模型訓練~~~~~~~~~~~~~~~~~~')

X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))

# total_batch = X_train.shape[0] // batch_size

# with tf.Session() as sess:
#     tl.layers.initialize_global_variables(sess)
#     for epoch in range(n_epochs):
#         avg_cost = 0.
#         for i in range(total_batch):
#             batch_x, batch_y = X_train[i*batch_size:(i+1)*batch_size], y_train[i*batch_size:(i+1)*batch_size]
#             batch_x = np.array(batch_x).astype(np.float32)
#             batch_cost,_ = sess.run([cost,train_op], feed_dict={x:batch_x})
#             avg_cost += batch_cost

#             if not i % print_interval:          
#                 print('Minibatch:    %03d | Cost:    %.3f' %(i+1, batch_cost))

#         print('Epoch:    %03d | AvgCost:    %.3f' %(epoch+1, avg_cost/(i+1)) )
#     saver.save(sess, save_path='./ae_tl_3.4/autoencoder-sigmoid.ckpt')
#     # saver.save(sess, save_path='./ae_tl_3.4/autoencoder-relu.ckpt')       


print('~~~~~~~~~~~~~~~15圖展示~~~~~~~~~~~~~~~~')

import matplotlib.pyplot as plt

n_images = 15
fig, axes = plt.subplots(nrows=2, ncols=n_images, sharex=True, sharey=True, figsize=(20, 2.5))
test_images = X_test[:n_images]
with tf.Session() as sess:  
    saver.restore(sess, save_path='./ae_tl_3.4/autoencoder-sigmoid.ckpt')
    decoded = sess.run(recon_layer1.outputs, feed_dict={x:test_images})
    if model =='relu':
        weights = sess.run(tl.layers.get_variables_with_name('relu/W:0', train_only=True, printable=True))
    elif model == 'sigmoid':
        weights = sess.run(tl.layers.get_variables_with_name('sigmoid/W:0', train_only=True, printable=True))
    recon_weights = sess.run(tl.layers.get_variables_with_name('recon_layer1/W:0', train_only=True, printable=True))
    recon_bias = sess.run(tl.layers.get_variables_with_name('recon_layer1/b:0', train_only=True, printable=True))

for i in range(n_images):
    for ax, img in zip(axes, [test_images, decoded]):
        ax[i].imshow(img[i].reshape((image_width, image_width)), cmap='binary')
# plt.show()


print('~~~~~~~~~~~~~~~2到9的漸變~~~~~~~~~~~~')


import matplotlib.pyplot as plt

sample1_idx = 1
sample2_idx = 12
sample1 = test_images[sample1_idx].reshape([-1,784])
sample2 = test_images[sample2_idx].reshape([-1,784])

saver = tf.train.Saver()
with tf.Session() as sess:
    saver.restore(sess, save_path='./ae_tl_3.4/autoencoder-sigmoid.ckpt')
    dp_dict = tl.utils.dict_to_one(recon_layer1.all_drop)
    feed_dict = {x:sample1}
    feed_dict.update(dp_dict)
    encoded1 = sess.run(encoded_img, feed_dict=feed_dict)
    feed_dict = {x:sample2}
    feed_dict.update(dp_dict)
    encoded2 = sess.run(encoded_img, feed_dict=feed_dict)

encoded = tf.placeholder(tf.float32, shape = [None, hidden_size],name='encoded')

recon_weights, recon_bias = recon_weights[-1],recon_bias[-1]

test_network = tl.layers.InputLayer(encoded, name='test_input')
test_recon_layer1 = tl.layers.DenseLayer(test_network, input_size, act=tf.nn.sigmoid, name='test_recon_layer1')

diff = encoded2-encoded1
num_inter = 10
delta = diff/num_inter
encoded_all = encoded1

for i in range(1, num_inter+1):
    encoded_all = np.vstack((encoded_all, encoded1 + delta*i))

with tf.Session() as sess:
    tl.layers.initialize_global_variables(sess)
    decoded_all = sess.run(test_recon_layer1.outputs, feed_dict={encoded:encoded_all})

fig, axes = plt.subplots(nrows=1, ncols=num_inter+1, sharex=True, sharey=True,figsize=(15,1.5))

for i in range(num_inter+1):
    axes[i].imshow(decoded_all[i].reshape((image_width, image_width)), cmap='binary')
plt.show()

 15圖展示，沒有問題，但是2到9的漸變就是出不來，是不是這裏我加錯了東東？

with tf.Session() as sess:
    tl.layers.initialize_global_variables(sess) #自己加的
    decoded_all = sess.run(test_recon_layer1.outputs, feed_dict={encoded:encoded_all})

但要是不加的話會報錯：Attempting to use uninitialized value test_recon_layer1/b

加上的話，卻只以得到11張雪花圖~~

 要是有大神路過，幫忙指點下哦~~~~