我是Tensorflow的新手并尝试实施生成对抗网络。我正在关注this教程，我们正在尝试使用生成模型生成像图像一样的MNIST数据集。但是，代码似乎使用旧版本的TensorFlow（＆lt; 1.0），因为它发出以下错误：
line：trainerD = tf.train.AdamOptimizer（）。minimize（d_loss，var_list = d_vars）
ValueError：变量d_wconv1 / Adam /不存在或未创建 使用tf.get_variable（）。你的意思是在VarScope中设置reuse = None吗？
相同的代码如下：
import tensorflow as tf
import random
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
x_train = mnist.train.images[:55000,:]
#print (x_train.shape)
#randomNum = random.randint(0,55000)
#image = x_train[randomNum].reshape([28,28])
#plt.imshow(image, cmap=plt.get_cmap('gray_r'))
#plt.show()
def conv2d(x, W):
return tf.nn.conv2d(input=x, filter=W, strides=[1, 1, 1, 1], padding='SAME')
def avg_pool_2x2(x):
return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def discriminator(x_image, reuse=False):
if (reuse):
tf.get_variable_scope().reuse_variables()
#First Conv and Pool Layers
W_conv1 = tf.get_variable('d_wconv1', [5, 5, 1, 8], initializer=tf.truncated_normal_initializer(stddev=0.02))
b_conv1 = tf.get_variable('d_bconv1', [8], initializer=tf.constant_initializer(0))
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = avg_pool_2x2(h_conv1)
#Second Conv and Pool Layers
W_conv2 = tf.get_variable('d_wconv2', [5, 5, 8, 16], initializer=tf.truncated_normal_initializer(stddev=0.02))
b_conv2 = tf.get_variable('d_bconv2', [16], initializer=tf.constant_initializer(0))
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = avg_pool_2x2(h_conv2)
#First Fully Connected Layer
W_fc1 = tf.get_variable('d_wfc1', [7 * 7 * 16, 32], initializer=tf.truncated_normal_initializer(stddev=0.02))
b_fc1 = tf.get_variable('d_bfc1', [32], initializer=tf.constant_initializer(0))
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*16])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
#Second Fully Connected Layer
W_fc2 = tf.get_variable('d_wfc2', [32, 1], initializer=tf.truncated_normal_initializer(stddev=0.02))
b_fc2 = tf.get_variable('d_bfc2', [1], initializer=tf.constant_initializer(0))
#Final Layer
y_conv=(tf.matmul(h_fc1, W_fc2) + b_fc2)
return y_conv
def generator(z, batch_size, z_dim, reuse=False):
if (reuse):
tf.get_variable_scope().reuse_variables()
g_dim = 64 #Number of filters of first layer of generator
c_dim = 1 #Color dimension of output (MNIST is grayscale, so c_dim = 1 for us)
s = 28 #Output size of the image
s2, s4, s8, s16 = int(s/2), int(s/4), int(s/8), int(s/16) #We want to slowly upscale the image, so these values will help
#make that change gradual.
h0 = tf.reshape(z, [batch_size, s16+1, s16+1, 25])
h0 = tf.nn.relu(h0)
#Dimensions of h0 = batch_size x 2 x 2 x 25
#First DeConv Layer
output1_shape = [batch_size, s8, s8, g_dim*4]
W_conv1 = tf.get_variable('g_wconv1', [5, 5, output1_shape[-1], int(h0.get_shape()[-1])],
initializer=tf.truncated_normal_initializer(stddev=0.1))
b_conv1 = tf.get_variable('g_bconv1', [output1_shape[-1]], initializer=tf.constant_initializer(.1))
H_conv1 = tf.nn.conv2d_transpose(h0, W_conv1, output_shape=output1_shape, strides=[1, 2, 2, 1], padding='SAME')
H_conv1 = tf.contrib.layers.batch_norm(inputs = H_conv1, center=True, scale=True, is_training=True, scope="g_bn1")
H_conv1 = tf.nn.relu(H_conv1)
#Dimensions of H_conv1 = batch_size x 3 x 3 x 256
#Second DeConv Layer
output2_shape = [batch_size, s4 - 1, s4 - 1, g_dim*2]
W_conv2 = tf.get_variable('g_wconv2', [5, 5, output2_shape[-1], int(H_conv1.get_shape()[-1])],
initializer=tf.truncated_normal_initializer(stddev=0.1))
b_conv2 = tf.get_variable('g_bconv2', [output2_shape[-1]], initializer=tf.constant_initializer(.1))
H_conv2 = tf.nn.conv2d_transpose(H_conv1, W_conv2, output_shape=output2_shape, strides=[1, 2, 2, 1], padding='SAME')
H_conv2 = tf.contrib.layers.batch_norm(inputs = H_conv2, center=True, scale=True, is_training=True, scope="g_bn2")
H_conv2 = tf.nn.relu(H_conv2)
#Dimensions of H_conv2 = batch_size x 6 x 6 x 128
#Third DeConv Layer
output3_shape = [batch_size, s2 - 2, s2 - 2, g_dim*1]
W_conv3 = tf.get_variable('g_wconv3', [5, 5, output3_shape[-1], int(H_conv2.get_shape()[-1])],
initializer=tf.truncated_normal_initializer(stddev=0.1))
b_conv3 = tf.get_variable('g_bconv3', [output3_shape[-1]], initializer=tf.constant_initializer(.1))
H_conv3 = tf.nn.conv2d_transpose(H_conv2, W_conv3, output_shape=output3_shape, strides=[1, 2, 2, 1], padding='SAME')
H_conv3 = tf.contrib.layers.batch_norm(inputs = H_conv3, center=True, scale=True, is_training=True, scope="g_bn3")
H_conv3 = tf.nn.relu(H_conv3)
#Dimensions of H_conv3 = batch_size x 12 x 12 x 64
#Fourth DeConv Layer
output4_shape = [batch_size, s, s, c_dim]
W_conv4 = tf.get_variable('g_wconv4', [5, 5, output4_shape[-1], int(H_conv3.get_shape()[-1])],
initializer=tf.truncated_normal_initializer(stddev=0.1))
b_conv4 = tf.get_variable('g_bconv4', [output4_shape[-1]], initializer=tf.constant_initializer(.1))
H_conv4 = tf.nn.conv2d_transpose(H_conv3, W_conv4, output_shape=output4_shape, strides=[1, 2, 2, 1], padding='VALID')
H_conv4 = tf.nn.tanh(H_conv4)
#Dimensions of H_conv4 = batch_size x 28 x 28 x 1
return H_conv4
sess = tf.Session()
z_dimensions = 100
z_test_placeholder = tf.placeholder(tf.float32, [None, z_dimensions])
sample_image = generator(z_test_placeholder, 1, z_dimensions)
test_z = np.random.normal(-1, 1, [1,z_dimensions])
sess.run(tf.global_variables_initializer())
temp = (sess.run(sample_image, feed_dict={z_test_placeholder: test_z}))
my_i = temp.squeeze()
#plt.imshow(my_i, cmap='gray_r')
#plt.show()
batch_size = 16
tf.reset_default_graph() #Since we changed our batch size (from 1 to 16), we need to reset our Tensorflow graph
sess = tf.Session()
x_placeholder = tf.placeholder("float", shape = [None,28,28,1]) #Placeholder for input images to the discriminator
z_placeholder = tf.placeholder(tf.float32, [None, z_dimensions]) #Placeholder for input noise vectors to the generator
Dx = discriminator(x_placeholder) #Dx will hold discriminator prediction probabilities for the real MNIST images
Gz = generator(z_placeholder, batch_size, z_dimensions) #Gz holds the generated images
Dg = discriminator(Gz, reuse=True) #Dg will hold discriminator prediction probabilities for generated images
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dg, labels=tf.ones_like(Dg)))
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dx, labels=tf.ones_like(Dx)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dg, labels=tf.zeros_like(Dg)))
d_loss = d_loss_real + d_loss_fake
tvars = tf.trainable_variables()
d_vars = [var for var in tvars if 'd_' in var.name]
g_vars = [var for var in tvars if 'g_' in var.name]
trainerD = tf.train.AdamOptimizer().minimize(d_loss, var_list=d_vars)
trainerG = tf.train.AdamOptimizer().minimize(g_loss, var_list=g_vars)
sess.run(tf.global_variables_initializer())
iterations = 3000
for i in range(iterations):
z_batch = np.random.normal(-1, 1, size=[batch_size, z_dimensions])
real_image_batch = mnist.train.next_batch(batch_size)
real_image_batch = np.reshape(real_image_batch[0],[batch_size,28,28,1])
_,dLoss = sess.run([trainerD, d_loss],feed_dict={z_placeholder:z_batch,x_placeholder:real_image_batch}) #Update the discriminator
_,gLoss = sess.run([trainerG,g_loss],feed_dict={z_placeholder:z_batch}) #Update the generator
sample_image = generator(z_placeholder, 1, z_dimensions)
z_batch = np.random.normal(-1, 1, size=[1, z_dimensions])
temp = (sess.run(sample_image, feed_dict={z_placeholder: z_batch}))
my_i = temp.squeeze()
plt.imshow(my_i, cmap='gray_r')
plt.show()
似乎有一个微不足道的解决方案，遗憾的是我无法弄明白。任何帮助将不胜感激。
答案 0 :(得分：3)
请修改您的代码，如下所示
with tf.variable_scope(tf.get_variable_scope(),reuse=False):
trainerD = tf.train.AdamOptimizer().minimize(d_loss, var_list=d_vars)
trainerG = tf.train.AdamOptimizer().minimize(g_loss, var_list=g_vars)
答案 1 :(得分：0)
这种事情可能是由两个GPU组成的，并且层定义在它们之间划分，导致它们不存在于一个设备上。一种解决方案是通过指定export CUDA_VISIBLE_DEVICES = 0
仅使用一台设备