This question was asked here: TensorFlow ValueError: Variable does not exist, or was not created with tf.get_variable()
However, I tried the solution which is implemented in my code example and I still get the error. The error is the same as what is said in the link which is: ValueError: Variable w1_d/Adam/ does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?
Here is my code (Note this will not run even with the error corrected as for one thing sess.run is not in the code, however as you can see the code is already long so I cut it down to a length that would still reproduce the same error from the entire code):
#code much from EZGAN github
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
np.random.seed(112)
tf.set_random_seed(232)
data = []
datalist = [[[0.0 for each2 in range(26)]for each in range(100)] for each in range(20)]
datasum = [0.0 for each in range(20)]
def discriminator(x_data, y_data, drate, is_training, market, reuse=False):
if (reuse):
tf.get_variable_scope().reuse_variables()
hidden = 64
input111 = tf.concat([x_data, y_data, market], 1)
features = 26
sdev = np.sqrt(float(1/features))
w1_d = tf.get_variable("w1_d",[features, hidden],initializer = tf.random_normal_initializer(stddev=sdev))
sdev = np.sqrt(float(1/hidden))
weights2_d = tf.get_variable("w2_d", [hidden, hidden], initializer = tf.random_normal_initializer(stddev=sdev))
weights17_d = tf.get_variable("w3_d", [hidden, 1], initializer = tf.random_normal_initializer(stddev=sdev))
bias1_d = tf.get_variable("b1_d", [hidden], initializer = tf.random_normal_initializer(stddev=0))
bias2_d = tf.get_variable("b2_d", [hidden],initializer = tf.random_normal_initializer(stddev=0))
bias17_d = tf.get_variable("b3_d", [], initializer = tf.random_normal_initializer(stddev=0))
mul_d = tf.matmul(input111, w1_d) + bias1_d
layer1_d = mul_d
layer16_d = tf.matmul(layer1_d, weights2_d) + bias2_d + layer1_d
out_d = tf.matmul(layer16_d, weights17_d) + bias17_d
sigout = tf.sigmoid(out_d)*.9999998+.0000001
return sigout
def generator(x_in, drate, is_training, samples):
demlen = 4
normdraws = np.random.randn()
demodraws = np.random.rand(demlen,1)
demodraws = demodraws.astype(np.float32)
features = 6
hidden = 64
sdev = tf.sqrt(1.0/features)
weights1_g = tf.get_variable("w1_g", [features, hidden], initializer = tf.random_normal_initializer(stddev=sdev))
delta_g= tf.get_variable("delta_g", [features, 1], initializer = tf.random_normal_initializer(stddev=sdev))
demweights_g = tf.get_variable("dw_g", [features, demlen], initializer = tf.random_normal_initializer(stddev=sdev))
sdev = tf.sqrt(1/hidden)
weights2_g = tf.get_variable("w2_g", [hidden,1], initializer = tf.random_normal_initializer(stddev=sdev))
bias1_g = tf.get_variable("b1_g", [hidden], initializer = tf.random_normal_initializer(stddev=0))
bias2_g = tf.get_variable("b2_g", [], initializer=tf.random_normal_initializer(stddev=0))
biasdelt_g = tf.get_variable("bdelt2_g", [], initializer=tf.random_normal_initializer(stddev=0))
mul_g = tf.matmul(x_in, weights1_g) + bias1_g
layer1_g = mul_g
out_g = tf.matmul(layer1_g, weights2_g) + bias2_g
utility_g = tf.matmul(x_in,delta_g) + biasdelt_g + tf.exp(out_g)*normdraws+tf.matmul(x_in,tf.matmul(demweights_g,demodraws))
output_g = tf.nn.softmax(utility_g, 0)#*samples
return (x_in, output_g)
sess = tf.Session()
x_placeholder = tf.placeholder("float", shape = [None, 6])
y_placeholder = tf.placeholder("float", shape = [None, 1])
drate1 = tf.placeholder("float")
is_training1= tf.placeholder("bool")
market_place = tf.placeholder("float", shape = [None, 19])
shape1 = tf.placeholder("float", shape = [])
Gz = generator(x_placeholder, drate1, is_training1, shape1)
Dx = discriminator(x_placeholder, y_placeholder, drate1, is_training1, market_place)
Dg = discriminator(Gz[0], Gz[1], drate1, is_training1, market_place, reuse=True)
g_loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dg, labels=tf.ones_like(Dg)))
d_loss_real = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=Dx, labels=tf.fill([tf.shape(x_placeholder)[0], 1], 1.0)))
d_loss_fake = tf.reduce_sum(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]
with tf.variable_scope(tf.get_variable_scope(), reuse=False) as scope:
d_trainer_fake = tf.train.AdamOptimizer(0.0001).minimize(d_loss_fake, var_list=d_vars)
d_trainer_real = tf.train.AdamOptimizer(0.0001).minimize(d_loss_real, var_list=d_vars)
# Train the generator
# Decreasing from 0.004 in GitHub version
g_trainer = tf.train.AdamOptimizer(0.0001).minimize(g_loss, var_list=g_vars)
