$ git clone https://github.com/TensorFlow-World.git Cloning into 'TensorFlow-World'... remote: Not Found fatal: repository 'https://github.com/TensorFlow-World.git/' not found

This issue is regarding your policy regarding typos in your codebase. Here is the relevant section in your CONTRIBUTING.rst: For typos, please do not create a pull request. Instead, declare them in issues or email the repository owner.

I suggest this policy be revised as it creates an extra step for you, the maintainer of this repo. For example, here is your current process:

1. Contributor finds a typo.
2. Contributor opens an issue.
3. Repo owner reads the issue.
4. Repo owner decides to create a code change to fix the typo and pushes the change.

Here is the suggested process:

1. Contributor finds a typo.
2. Contributor creates a code change to fix the typo and creates a pull request
3. Repo owner decides to accept the pull request and merges the changes.

If typos can be discussed within a pull request, I don't see the point for a contributor to create an issue and then the repo owner creates a code change to fix the typo. I suggest using Github Issues to discuss lengthy proposals, but typos should be handled directly within a pull request. For example, see this Contributing guide for Github's open source guide.

• http://flake8.pycqa.org with find syntax errors and undefined names that can halt your program.
  • --select=E901,E999,F821,F822,F823 focuses the tool on the most critical issues
• Fxxx codes are here: http://flake8.pycqa.org/en/latest/user/error-codes.html
• Other codes are here: https://pycodestyle.readthedocs.io/en/latest/intro.html#error-codes
• The output is here: https://travis-ci.org/astorfi/TensorFlow-World/builds/272817787

F821 is really helpful for finding Python 2 / 3 differences but also for typos, copy/paste errors, etc.

So, I cleaned up the grammar / spelling and got to the section about contributing to this repository.

Based on this - it's definitely going to the top.

Also. No. Here's your pull request.

Undefined names can raise NameErrorat runtime.

https://travis-ci.org/astorfi/TensorFlow-World/jobs/272817788#L623-L626

https://github.com/astorfi/TensorFlow-World/blob/master/codes/3-neural_networks/multi-layer-perceptron/code/test_classifier.py#L113

Is defining train_op for each data point and epoch anew really needed? I'm new to TensorFlow so I can't tell why or why not this would make sense. For me, the regression seems to work fine (and much faster) if the line is removed.

Hello, just wanted to say this is a great guide. but when i execute : sudo apt-get install nvidia-current-updates nvidia-settings-updates its says: E: Unable to locate package nvidia-settings-updates

can someone help me with this?

I noticed in the notebook for the linear regression that the cost was only being calculated for the last piece of data in each epoch.

with tf.Session() as sess:

    # Initialize the variables[w and b].
    sess.run(tf.global_variables_initializer())

    # Get the input tensors
    X, Y = inputs()

    # Return the train loss and create the train_op.
    train_loss = loss(X, Y)
    train_op = train(train_loss)

    # Step 8: train the model
    for epoch_num in range(num_epochs): # run 100 epochs
        for x, y in data:
          train_op = train(train_loss)

          # Session runs train_op to minimize loss
          loss_value,_ = sess.run([train_loss,train_op], feed_dict={X: x, Y: y})

        # Displaying the loss per epoch.
        print('epoch %d, loss=%f' %(epoch_num+1, loss_value))

        # save the values of weight and bias
        wcoeff, bias = sess.run([W, b])

data is being iterated over and the loss_value that is calculated is written over each time through the loop. Thus, the loss is only for the last piece of data. Since the loss needs to be computed over all of the data being used to train, the cost function should probably be something more like the following:

def loss(X, Y):
    '''
    compute the loss by comparing the predicted value to the actual label.
    :param X: The inputs.
    :param Y: The labels.
    :return: The loss over the samples.
    '''

    # Making the prediction.
    Y_predicted = inference(X)
    return tf.reduce_sum(tf.squared_difference(Y, Y_predicted))/(2*data.shape[0])

With this change above, the training section could be changed to the following (with the looping over data removed completely):

with tf.Session() as sess:

    # Initialize the variables[w and b].
    sess.run(tf.global_variables_initializer())

    # Get the input tensors
    X, Y = inputs()

    # Return the train loss and create the train_op.
    train_loss = loss(X, Y)
    train_op = train(loss(X, Y))

    # Step 8: train the model
    for epoch_num in range(num_epochs): # run 100 epochs
        loss_value, _ = sess.run([train_loss,train_op], feed_dict={X: data[:,0], Y: data[:,1]})

        # Displaying the loss per epoch.
        print('epoch %d, loss=%f' %(epoch_num+1, loss_value))

        # save the values of weight and bias
        wcoeff, bias = sess.run([W, b])

This would result in output like the following:

epoch 1, loss=1573.599976
epoch 2, loss=1332.513916
epoch 3, loss=1128.868408
epoch 4, loss=956.848999
epoch 5, loss=811.544067

I would be glad to submit a pull request with these and other minor changes. Please let me know if I have some misunderstanding.

Hey,

I see that there are no tutorial notebooks for Transformer implementations in this repository yet. Transformers are used primarily in the field of natural language processing. Like recurrent neural networks, Transformers are designed to handle sequential data, such as natural language, for tasks such as translation and text summarization.

I would like to add such tutorial notebooks.

There is a small typo in docs/tutorials/1-basics/basic_math_operations/README.rst.

Should read visualising rather than visualiaing.

Semi-automated pull request generated by https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

In the tutorial doc of chapter 1 "Basics/variables", there might be a misktake here:

# "variable_list_custom" is the list of variables that we want to initialize.
variable_list_custom = [weights, custom_variable]

# The initializer
init_custom_op = tf.variables_initializer(var_list=all_variables_list)

The last line of the code above might end up with var_list=variable_list_custom, not all_variables_list.

Here's url of the doc: https://github.com/astorfi/TensorFlow-World/tree/master/docs/tutorials/1-basics/variables#initializing-specific-variables Thank you for your repo, it helps me a lot.