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TensorFlow ValueError: the variable does not exist or was not created using tf.get_variable ()

I am new to Tensorflow and trying to implement the Generative Adverarial Network. I follow this tutorial for the same one in which we are trying to create an MNIST dataset, such as an image, using generative models. However, the code seems to use an older version of TensorFlow (<1.0), due to which it throws the following error:

line: trainerD = tf.train.AdamOptimizer (). Collapse (d_loss, var_list = d_vars)

ValueError: variable d_wconv1 / Adam / does not exist or was not created with tf.get_variable (). Do you want to set reuse = None in VarScope?

The code for it is as follows:

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()

It seems that we have a trivial solution, unfortunately, I can not understand. Any help would be appreciated.

+4
1

:

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)

+3

Source: https://habr.com/ru/post/1673884/

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