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README.md

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@@ -148,9 +148,60 @@ I'm currently running this tutorial with **Python 3** on **Anaconda**
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Python 3.5.2
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---
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## Setting the Environment
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In this repository, files to re-create virtual env with `conda` are provided for Linux and OSX systems,
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namely `deep-learning.yml` and `deep-learning-osx.yml`, respectively.
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To re-create the virtual environments (on Linux, for example):
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```shell
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conda env create -f deep-learning.yml
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```
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For OSX, just change the filename, accordingly.
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### Installing Tensorflow
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To date `tensorflow` comes in two different packages, namely `tensorflow` and `tensorflow-gpu`, whether you want to install
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the framework with CPU-only or GPU support, respectively.
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For this reason, `tensorflow` has **not** been included in the conda envs and has to be installed separately.
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#### Tensorflow for CPU only:
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```shell
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pip install tensorflow
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```
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#### Tensorflow with GPU support:
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```shell
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pip install tensorflow-gpu
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```
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**Note**: NVIDIA Drivers and CuDNN **must** be installed and configured before hand. Please refer to the official
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[Tensorflow documentation](https://www.tensorflow.org/install/) for further details.
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#### Important Note:
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All the code provided+ in this tutorial can run even if `tensorflow` is **not** installed, and so using `theano` as the (default) backend!
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___**This** is exactly the power of Keras!___
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Therefore, installing `tensorflow` is **not** stricly required!
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+: Apart from the **1.2 Introduction to Tensorflow** tutorial, of course.
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### Configure Keras with tensorflow
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By default, Keras is configured with `theano` as backend.
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If you want to use `tensorflow` instead, these are the simple steps to follow:
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1) Create the `keras.json` (if it does not exist):
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```shell
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}
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```
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3) Verify it is properly configured:
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```python
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!cat ~/.keras/keras.json

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