alrojo / tensorflow-tutorial Goto Github PK

Hi alrojo,

I am sure that most people have had some experience with docker. I have never used it so I had some difficulty getting Tensorboard up with the following commands. I also checked out the man page, but it seemed that there were multiple options for one flag. Would you be able to expound on the following commands?

> docker run -p 6006:6006 -v $PATH\_TO\_FOLDER/tensorflow_tutorial:/mnt/myproject -it alrojo/tf-sklearn-cpu
> cd mnt/myproject/lab6_Kaggle
> tensorboard --logdir=tensorboard

Thanks again for the great tutorial!

print("operations")
operations = [op.name for op in tf.get_default_graph().get_operations()]
print(operations)
print

print("variables")
variables = [var.name for var in tf.all_variables()]
print(operations)

The print on last line should be:
print(variables)

I had a suggestion, but realized it was not a big deal. And I don't know how to delete my own issue.

Thanks a lot for the tutorial. I have learned so much!

Hi ,

Its more or like a pull request . You have defined both training and validation in the decoder itself . So , I know the idea behind making new prediction .

The problem with the validation inside tf_utils.decoder is , it is always expecting the target .
But for making a prediction ( without having targets , basically predicting the target ) , we need to have a separate function . How to write that ? I know , the idea , taking the first target as EOS , and then keep on finding the argmax until we reach max_length or another EOS . Can you help to make a function , doing the same ?

In lab4 "Build the model", convolutional2d is called as

# wrapping conv with batch_norm
def conv(l_in, num_outputs, kernel_size, scope, stride=1):
    return convolution2d(l_in, num_outputs=num_outputs, kernel_size=kernel_size,
                         stride=stride, normalize_fn=batch_norm, scope=scope)

Which produces:

TypeError: convolution2d() got an unexpected keyword argument 'normalize_fn'

It should be "normalizer_fn". Adding that missing R makes it works perfectly.

I have cloned the repository to C:\Users\Mathias\Desktop\Docker so that i have C:\Users\Mathias\Desktop\Docker\tensorflow_tutorial\download-and-setup and so on.
if I run the following command:
docker run -p 8888:8888 -v C:\Users\Mathias\Desktop\Docker\tensorflow_tutorial:/mnt/myproject -it alrojo/tf-sklearn-cpu
I have a folder named tensorflow_tutorial within /mnt/myproject/ but no files.
If i change the folder for sharing i can see subdirectories but not still not the content of these.
I feel like the point of a shared directory is to be able to share files?

In lab2_CNN/lab2_CNN.ipynb, there is an AttributeError: 'module' object has no attribute 'pack'.
It should be renamed to tf.stack according to https://www.tensorflow.org/install/migration.