尝试还原我的MonitoredTrainingSession之后，我遇到了一个错误Attempting to use uninitialized value decoder/b_d_03。我该如何解决这个问题？我想还原我的VariationalAutoencoder，而只使用解码器（最后几行代码）。
随时修改以下代码。
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import os
import random
import sys
import cv2
from scipy.stats import norm
import time
def normalize(img, max=1):
a = np.shape(img)
img = np.reshape(img, (a[0],a[1],1))
return max*(img-np.amin(img))/(np.amax(img)-np.amin(img))
def loadDATA(url,prefix):
files = os.listdir(url)
img = [np.reshape(np.dot(plt.imread(url+i),[1,0,0]),(43,43,1)) for i in files if(i.startswith(prefix))]
img_name = [i for i in files if(i.startswith(prefix))]
return img, img_name
print('load data...')
url = '...'
prefix = 'SPOT'
img, name = loadDATA(url,prefix)
img = [normalize(i) for i in img]
print('data loaded:', np.shape(img)[0], 'examples with dimension:', np.shape(img)[1],'x',np.shape(img)[2],'.')
#Layers
def conv2d(input, name, kshape, strides=[1,1,1,1], padding='SAME'):
with tf.name_scope(name):
W = tf.get_variable(name='w_'+name,
shape=kshape,
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
b = tf.get_variable(name='b_'+name,
shape=[kshape[3]],
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
out = tf.nn.conv2d(input,W,strides=strides,padding=padding)
out = tf.nn.bias_add(out,b)
return out
def conv2dLEAKYRELU(input, name, kshape, strides=[1,1,1,1], padding='SAME'):
with tf.name_scope(name):
out = tf.nn.leaky_relu(conv2d(input,name,kshape,strides,padding))
return out
def fullyConnected(input, name, output_size):
with tf.name_scope(name):
input_size = input.shape[1:]
input_size = int(np.prod(input_size))
W = tf.get_variable(name='w_'+name,
shape=[input_size, output_size],
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
b = tf.get_variable(name='b_'+name,
shape=[output_size],
initializer=tf.contrib.layers.xavier_initializer(uniform=False))
input = tf.reshape(input, [-1, input_size])
out = tf.add(tf.matmul(input,W),b)
return out
def fullyConnectedRELU(input, name, output_size):
with tf.name_scope(name):
out = tf.nn.relu(fullyConnected(input,name,output_size))
return out
def fullyConnectedLEAKYRELU(input, name, output_size):
with tf.name_scope(name):
out = tf.nn.leaky_relu(fullyConnected(input,name,output_size))
return out
def fullyConnectedSIGMOID(input, name, output_size):
with tf.name_scope(name):
out = tf.nn.sigmoid(fullyConnected(input,name,output_size))
return out
def fullyConnectedSOFTPLUS(input, name, output_size):
with tf.name_scope(name):
out = tf.nn.softplus(fullyConnected(input,name,output_size))
return out
def deconv2d(input, name, kshape, n_outputs, strides=[1,1], padding='SAME'):
with tf.name_scope(name):
out = tf.contrib.layers.conv2d_transpose(input,
num_outputs=n_outputs,
kernel_size=kshape,
stride=strides,
padding=padding,
weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(uniform=False),
biases_initializer=tf.contrib.layers.xavier_initializer(uniform=False),
activation_fn=None)
return out
def deconv2dRELU(input, name, kshape, n_outputs, strides=[1,1], padding='SAME'):
with tf.name_scope(name):
out = tf.contrib.layers.conv2d_transpose(input,
num_outputs=n_outputs,
kernel_size=kshape,
stride=strides,
padding=padding,
weights_initializer=tf.contrib.layers.xavier_initializer_conv2d(uniform=False),
biases_initializer=tf.contrib.layers.xavier_initializer(uniform=False),
activation_fn=tf.nn.relu)
return out
#Ende Layers
def normalization01(input):
return tf.div(tf.subtract(input,tf.reduce_min(input)),tf.subtract(tf.reduce_max(input),tf.reduce_min(input)),name='img_norm')
def Encoder(data,name):
with tf.name_scope(name):
input = tf.reshape(data,[-1,43,43,1])
input = normalization01(input)
print(input)
x = tf.layers.flatten(input)
print(x)
x = fullyConnectedRELU(x,name='e_01',output_size=500)
print(x)
x = fullyConnectedRELU(x,name='e_02',output_size=500)
print(x)
x = fullyConnectedRELU(x,name='e_03',output_size=200)
print(x)
x = fullyConnectedRELU(x,name='e_04',output_size=200)
print(x)
loc = fullyConnected(x,name='mean',output_size=2)
scale = fullyConnectedSOFTPLUS(x,name='std',output_size=2)
print(loc)
print(scale)
return tf.contrib.distributions.MultivariateNormalDiag(loc,scale,name='z')
def Decoder(z,name):
print(z)
with tf.name_scope(name):
y = fullyConnectedRELU(z,name='d_01',output_size=200)
print(y)
y = fullyConnectedRELU(y,name='d_02',output_size=200)
print(y)
y = fullyConnectedRELU(z,name='d_03',output_size=500)
print(y)
y = fullyConnectedRELU(y,name='d_04',output_size=500)
print(y)
y = fullyConnectedLEAKYRELU(y,name='d_05',output_size=1849)
print(y)
y = tf.reshape(y,(-1,43,43))
y = tf.contrib.distributions.Independent(tf.contrib.distributions.Bernoulli(y),2,name='output')
print(y)
return y
def Prior():
loc = tf.zeros(2,name='loc')
scale = tf.ones(2,name='scale')
return tf.contrib.distributions.MultivariateNormalDiag(loc, scale,name='zz')
Encoder = tf.make_template('encoder', Encoder)
Decoder = tf.make_template('decoder', Decoder)
#Modell erstellen
data = tf.placeholder(tf.float32, [None,43,43,1])
Prior = Prior()
posterior = Encoder(data, name='Enc')
code = posterior.sample()
#Fehlerfunktion definieren
likelihood = Decoder(code, name='Dec').log_prob(tf.reshape(data,(-1,43,43)))
divergence = tf.contrib.distributions.kl_divergence(posterior,Prior)
elbo = tf.reduce_mean(likelihood-divergence)
optimize = tf.train.AdamOptimizer(0.001).minimize(-elbo,global_step=tf.train.get_or_create_global_step())
#Trainieren und Visiualisieren
samples = Decoder(Prior.sample(10), name='Dec').mean()
vector = tf.placeholder(tf.float32,[None,2], name='vector')
print(vector)
sam = Decoder(vector, name='Dec').mean()
train_img = img
test_img = img[:10]
test_samples =np.ones(1)
print('----------')
print(samples)
'''
with tf.train.MonitoredTrainingSession(checkpoint_dir='C:/Users/vcshwaf/Desktop/Studienarbeit/Bildvorverarbeitung/Update20190224/') as sess:
for epoch in range(2):
feed = {data: np.reshape(test_img,(-1,43,43,1))}
test_elbo, test_codes, test_samples = sess.run([elbo, code, samples], feed)
print('Epoch',epoch,'elbo',test_elbo)
random.shuffle(train_img)
for i in range(30):
img_batch = np.concatenate([train_img[i:i+100]])
feed = {data: np.reshape(img_batch,(-1,43,43,1))}
sess.run(optimize,feed)
if epoch%1==0:
n=15
figure=np.zeros((43*n,43*n))
grid_x = norm.ppf(np.linspace(0.05, 0.95, n))
grid_y = norm.ppf(np.linspace(0.05,0.95, n))
for i, yi in enumerate(grid_x):
for j, xi in enumerate(grid_y):
z_sample=np.array([[xi,yi]])
feed = {vector: z_sample}
example = sess.run(sam, feed)
example = np.reshape(example,(43,43))
figure[i*43:(i+1)*43,
j*43:(j+1)*43]=example
plt.figure(figsize=(10,10))
plt.imshow(figure,cmap='gray')
plt.show()
'''
with tf.Session() as sess:
saver = tf.train.import_meta_graph('.../model.ckpt-60.meta')
saver.restore(sess, '.../model.ckpt-60')
n=15
figure=np.zeros((43*n,43*n))
grid_x = norm.ppf(np.linspace(0.05, 0.95, n))
grid_y = norm.ppf(np.linspace(0.05,0.95, n))
for i, yi in enumerate(grid_x):
for j, xi in enumerate(grid_y):
z_sample=np.array([[xi,yi]])
feed = {vector: z_sample}
example = sess.run(sam, feed)
example = np.reshape(example,(43,43))
figure[i*43:(i+1)*43,
j*43:(j+1)*43]=example
plt.figure(figsize=(10,10))
plt.imshow(figure,cmap='gray')
plt.show()