I'm building a RNN model to do the image classification. I used a pipeline to feed in the data. However it returns

ValueError: Variable rnn/rnn/basic_rnn_cell/weights already exists, disallowed. Did you mean to set reuse=True in VarScope? Originally defined at:

I wonder what can I do to fix this since there are not many examples of implementing RNN with an input pipeline. I know it would work if I use the placeholder, but my data is already in the form of tensors. Unless I can feed the placeholder with tensors, I prefer just to use the pipeline.

def RNN(inputs):
with tf.variable_scope('cells', reuse=True):
basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=batch_size)

with tf.variable_scope('rnn'):
outputs, states = tf.nn.dynamic_rnn(basic_cell, inputs, dtype=tf.float32)

fc_drop = tf.nn.dropout(states, keep_prob)

logits = tf.contrib.layers.fully_connected(fc_drop, batch_size, activation_fn=None)

return logits

#Training
with tf.name_scope("cost_function") as scope:
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=train_label_batch, logits=RNN(train_batch)))
train_step = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(cost)


#Accuracy
with tf.name_scope("accuracy") as scope:
correct_prediction = tf.equal(tf.argmax(RNN(test_image), 1), tf.argmax(test_image_label, 0))
accuracy = tf.cast(correct_prediction, tf.float32)

You need to use the reuse option correctly. following changes would solve it. For prediction you need to use the already existed variables in the graph.

def RNN(inputs, reuse):
with tf.variable_scope('cells', reuse=reuse):
basic_cell = tf.contrib.rnn.BasicRNNCell(num_units=batch_size, reuse=reuse)

...

...
#Training
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=train_label_batch, logits=RNN(train_batch, reuse=None)))

#Accuracy
...
correct_prediction = tf.equal(tf.argmax(RNN(test_image, reuse=True), 1), tf.argmax(test_image_label, 0)) This post was edited by Raji Reddy A at June 11, 2019 4:14 PM IST

the reuse mechanism is very useful for prediction phase of a network. Since you need to use the learned parameters for prediction; and in this case you are using the same graph; so setting reuse=True will give you access to the already learned variables for prediction. You can read about this more at tensorflow.org/programmers_guide/variable_scope