Problem with tensorflow initialization when it gets encapsulated - tensorflow

Question

I am encapsulating an autoencoder cost calculation, in order to allow to be used with an swarm algorithms. The goal is to get a cost summary of the autoencoder sending a few parameters, so the method creates a model, train it and returns its cost tensor
def getAECost(dfnormalized, adamParam, iterations):
N_VISIBLE = 31
N_HIDDEN = 20
DEVICE = '/gpu:0' #Or '/cpu:0'
ITERATIONS = 1 + iterations
with tf.device(DEVICE):
# create node for input data(entiendo none columns and N_VISIBLE rows)
X = tf.placeholder("float", [None, N_VISIBLE], name='X')
# create nodes for hidden variables
W_init_max = 4 * np.sqrt(6. / (N_VISIBLE + N_HIDDEN))
W_init = tf.random_uniform(shape=[N_VISIBLE, N_HIDDEN])#,
# minval=-W_init_max,
# maxval=W_init_max)
#Inicialite our weight and bias
#W [784,500]
W = tf.Variable(W_init, name='W')
#Inicializate only bias of hidden layer
b = tf.Variable(tf.zeros([N_HIDDEN]), name='b')
#W_prime[500,784]
W_prime = tf.transpose(W) # tied weights between encoder and decoder
b_prime = tf.Variable(tf.zeros([N_VISIBLE]), name='b_prime')
#model that take our variables parameters
#Comportamiento de la red neuronal
def model(X, W, b, W_prime, b_prime):
tilde_X = X
#To decode ?
Y = tf.nn.sigmoid(tf.matmul(tilde_X, W) + b) # hidden state
#to reconstructed the input
Z = tf.nn.sigmoid(tf.matmul(Y, W_prime) + b_prime) # reconstructed input
return Z
# build model graph
pred = model(X, W, b, W_prime, b_prime)
# create cost function
#Sum of squared error
cost = tf.reduce_sum(tf.pow(X - pred, 2)) # minimize squared error
#Tensor to parameter learning rate
learning = tf.placeholder("float", name='learning')
train_op = tf.train.AdamOptimizer(learning).minimize(cost) # construct an optimizer
with tf.Session() as sess:
# you need to initialize all variables
tf.global_variables_initializer()
RATIO = adamParam
for i in range(ITERATIONS):
#Prepare input(minibach) from feed autoencoder
input_ = dfnormalized
# train autoencoder
sess.run(train_op, feed_dict={X: input_, learning: RATIO})
#Save last epoch and test
if(i == ITERATIONS-1):
#Get output as dataframe after training(Z is a array, we cast to list to append with a dataframe)
costAE = sess.run(cost, feed_dict={X: input_})
return costAE
It worked a few days ago (maybe I had another session on background), returning the method a float number, but nowadays is not working, getting the inizialization error
FailedPreconditionError: Attempting to use uninitialized value W
[[{{node W/read}}]]
in the training step
sess.run(train_op, feed_dict={X: input_, learning: RATIO})
Any advice about how this initialization problem can be solved, or how can I encapsulate a tensorflow model and session?
Thanks

Answer 1

You have to actually run the variables initializer, tf.global_variables_initializer() returns an op to be executed, it does not run the initialization for you. So the solution to your problem should be replacing the line
tf.global_variables_initializer()
with
sess.run(tf.global_variables_initializer())

Answer 2

I have tried what #Addy said, and reestructured the code to see more legible, and now works perfectly
class Model:
N_VISIBLE = 31
N_HIDDEN = 20
DEVICE = '/gpu:0' #Or '/cpu:0'
with tf.device(DEVICE):
# create node for input data(entiendo none columns and N_VISIBLE rows)
X = tf.placeholder("float", [None, N_VISIBLE], name='X')
# create nodes for hidden variables
W_init_max = 4 * np.sqrt(6. / (N_VISIBLE + N_HIDDEN))
W_init = tf.random_uniform(shape=[N_VISIBLE, N_HIDDEN])#,
# minval=-W_init_max,
# maxval=W_init_max)
#Inicialite our weight and bias
#W [784,500]
W = tf.Variable(W_init, name='W')
#Inicializate only bias of hidden layer
b = tf.Variable(tf.zeros([N_HIDDEN]), name='b')
#W_prime[500,784]
W_prime = tf.transpose(W) # tied weights between encoder and decoder
b_prime = tf.Variable(tf.zeros([N_VISIBLE]), name='b_prime')
#model that take our variables parameters
#Comportamiento de la red neuronal
def model(X, W, b, W_prime, b_prime):
tilde_X = X
#To decode ?
Y = tf.nn.sigmoid(tf.matmul(tilde_X, W) + b) # hidden state
#to reconstructed the input
Z = tf.nn.sigmoid(tf.matmul(Y, W_prime) + b_prime) # reconstructed input
return Z
# build model graph
pred = model(X, W, b, W_prime, b_prime)
# create cost function
#Sum of squared error
cost = tf.reduce_sum(tf.pow(X - pred, 2)) # minimize squared error
#Tensor to parameter learning rate
learning = tf.placeholder("float", name='learning')
train_op = tf.train.AdamOptimizer(learning).minimize(cost) # construct an optimizer
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
def train (self, data, adamParam, iterations):
input_ = data
RATIO = adamParam
for i in range(iterations):
# train autoencoder
_= self.sess.run(self.train_op, feed_dict={self.X: input_, self.learning: RATIO})
#print ("Model trained")
def getAECost(self, data):
input_ = data
return self.sess.run(self.cost, {self.X: data})
def trainAndGetCost (self, dataTrain, dataCost, adamParam, iterations):
self.train(dataTrain, adamParam, iterations)
return self.getAECost(dataCost)