We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
I tried the DRQN for partial observations, but I got the error:
ValueError: prefix tensor must be either a scalar or vector, but saw tensor: Tensor("Placeholder_2:0", dtype=int32)
self.state_in = rnn_cell.zero_state(self.batch_size, tf.float32)
The text was updated successfully, but these errors were encountered:
I found that there are 3 consecutive lines:
self.batch_size = tf.placeholder(dtype=tf.int32) self.convFlat = tf.reshape(slim.flatten(self.conv4),[self.batch_size,self.trainLength,h_size]) self.state_in = rnn_cell.zero_state(self.batch_size, tf.float32)
I change the line:
self.batch_size = tf.placeholder(dtype=tf.int32)
into:
self.batch_size = tf.placeholder(dtype=tf.int32,shape=[])
And it works.
Sorry, something went wrong.
Thank you!!!! I thought for sure the code was doomed, per tensorflow/tensorflow#10213
But this fix is correct and worked for me, too!
Really hope @awjuliani can fix in the code (modify literally one line), thank you sir!
Just made the change! Thanks for pointing this out.
No branches or pull requests
I tried the DRQN for partial observations, but I got the error:
ValueError: prefix tensor must be either a scalar or vector, but saw tensor: Tensor("Placeholder_2:0", dtype=int32)
----Error happens in this line-------
self.state_in = rnn_cell.zero_state(self.batch_size, tf.float32)
The text was updated successfully, but these errors were encountered: