Remove Tensor FLow

conda remove tensorflow.

Ubuntu - Check Tensorflow version

Run:

python -c 'import tensorflow as tf; print(tf.__version__)'  # for Python 2
python3 -c 'import tensorflow as tf; print(tf.__version__)'  # for Python 3

Note that python is symlinked to /usr/bin/python3 in some Linux distributions, so use python instead of python3 in these cases.
pip list | grep tensorflow for Python 2 or pip3 list | grep tensorflow for Python 3 will also show the version of Tensorflow installed.

---

Virtualenv installation

Run:

python -c 'import tensorflow as tf; print(tf.__version__)'  # for both Python 2 and Python 3

pip list | grep tensorflow will also show the version of Tensorflow installed.
For example, I have installed TensorFlow 0.9.0 in a virtualenv for Python 3. So, I get:

$ python -c 'import tensorflow as tf; print(tf.__version__)'
0.9.0

$ pip list | grep tensorflow
tensorflow (0.9.0)

Reference

https://stackoverflow.com/questions/38549253/how-to-find-which-version-of-tensorflow-is-installed-in-my-system 

Installing Jupyter Notebook Ubuntu 16.04

Prerequisites

To follow this tutorial, you will need the following:

• Ubuntu 16.04 Droplet
• Non-root user with sudo privileges (Initial Server Setup with Ubuntu 16.04 explains how to set this up.)

All the commands in this tutorial should be run as a non-root user. If root access is required for the command, it will be preceded by sudo. Initial Server Setup with Ubuntu 16.04 explains how to add users and give them sudo access.

Step 1 — Installing Python 2.7 and Pip

In this section we will install Python 2.7 and Pip.
First, update the system's package index. This will ensure that old or outdated packages do not interfere with the installation.

sudo apt-get update

Next, install Python 2.7, Python Pip, and Python Development:

sudo apt-get -y install python2.7 python-pip python-dev

Installing python2.7 will update to the latest version of Python 2.7, and python-pip will install Pip which allows us to manage Python packages we would like to use. Some of Jupyter’s dependencies may require compilation, in which case you would need the ability to compile Python C-extensions, so we are installing python-dev as well.
To verify that you have python installed:

python --version

This will output:

Output
Python 2.7.11+

Depending on the latest version of Python 2.7, the output might be different.
You can also check if pip is installed using the following command:

pip --version

You should something similar to the following:

Output
pip 8.1.1 from /usr/lib/python2.7/dist-packages (python 2.7)

Similarly depending on your version of pip, the output might be slightly different.

Step 2 — Installing Ipython and Jupyter Notebook

In this section we will install Ipython and Jupyter Notebook.
First, install Ipython:

sudo apt-get -y install ipython ipython-notebook

Now we can move on to installing Jupyter Notebook:

sudo -H pip install jupyter

Depending on what version of pip is in the Ubuntu apt-get repository, you might get the following error when trying to install Jupyter:

Output
You are using pip version 8.1.1, however version 8.1.2 is available.
You should consider upgrading via the 'pip install --upgrade pip' command.

If so, you can use pip to upgrade pip to the latest version:

sudo -H pip install --upgrade pip

Upgrade pip, and then try installing Jupyter again:

sudo -H pip install jupyter

Step 3 — Running Jupyter Notebook

You now have everything you need to run Jupyter Notebook! To run it, execute the following command:

jupyter notebook

If you are running Jupyter on a system with JavaScript installed, it will still run, but it might give you an error stating that the Jupyter Notebook requires JavaScript:

Output
Jupyter Notebook requires JavaScript.
Please enable it to proceed.
...

Reference 

https://www.digitalocean.com/community/tutorials/how-to-set-up-a-jupyter-notebook-to-run-ipython-on-ubuntu-16-04 

_AttributeError: 'LinearClassifier' object has no attribute 'weights_' error.

getting an error when trying to visualize the weights
*weights = classifier.weights_* throws an _AttributeError: 'LinearClassifier' object has no attribute 'weights_' error. Does anyone have a clue about what's happening or how to the the weights for the classifier?

Solution

Replace: *weights= classifier.weights_* with: *weights = classifier.get_variable_value(classifier.get_variable_names()[1])*

InvalidArgumentError (see above for traceback): tensor_name = linear//weight

print ("Predicted %d, Label: %d" % (classifier.predict(test_data[0]), test_labels[0]))

the below error occurred.
InvalidArgumentError (see above for traceback): tensor_name = linear//weight; shape in shape_and_slice spec [1,10] does not match the shape stored in checkpoint: [784,10]
[[Node: save/RestoreV2_1 = RestoreV2[dtypes=[DT_FLOAT], _device="/job:localhost/replica:0/task:0/cpu:0"](_arg_save/Const_0_0, save/RestoreV2_1/tensor_names, save/RestoreV2_1/shape_and_slices)]]

Although classifier.evaluate(test_data[0:1,:], test_labels[0:1]) is working..
{'accuracy': 1.0, 'global_step': 1000, 'loss': 0.010729363}

Resolution

prediction = classifier.predict(np.array([test_data[0]], dtype=float), as_iterable=False)
print("Predicted %d, Label: %d" % (prediction, test_labels[0]))