Steps for the machine learning Meetup
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Machine Learning Tutorial

This gives you the basic guidiance for what to do in the project. We have provided you with example projects for steps 3 to 5/6. Note that you will alyways have to do steps 0-2, to have your environment and data set up. Once you have setup your environment and data, feel free to copy the data to any downloaded project.


The presentation is available under this link.

Step 0 - Setup

Download and install Anaconda 3.6 Version from Anaconda. When installing under Windows, make sure to check the "Add Anaconda to my PATH environment variable" and "Register Anaconda as my default Python 3.6". You may have to logout and login again, to be able to use the conda command. As an alternative, you can use the Anaconda Propmpt for the following commands.

If you are using Windows, you can install Tensorflow with the following commands. First create and activate an environment for Tensorflow with

conda create -n tensorflow pip python=3.6
activate tensorflow

Conda environments are awesome, as they do not pollute your Python installation and you start from a clean environment. Then install Tensorflow with

pip install --ignore-installed --upgrade tensorflow

See Tensorflow for details and for instructions on how to install Tensorflow on Ubuntu and Mac Os. You also may want to install tensorflow-gpu, if you have a GPU. In that case, make sure to install tensorflow-gpu and also install the needed CUDA drivers. See the Tensorflow website for details. Please also take a look at the Problems you will encounter section in this readme.

Once you have installed Tensorflow, try to execute the following program:

import tensorflow as tf

data = tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=tf.float32)
data = tf.reshape(data, shape=[1, 2, 5, 1])
pool = tf.layers.max_pooling2d(data, pool_size=[2, 2], strides=2, padding='same')

sess = tf.Session()

Your output should be

[[[[ 7.]
   [ 9.]

You should use an IDE. I suggest using Pycharm, for me it is one of the best Python IDEs. To get the code either

  • clone the project, so you have the latest and completed version or
  • download the respective release and unzip it

Then open this cloned or unzipped folder with Pycharm, see here for details on opening projects with Pycharm. In Pycharm you may have to add the newly created conda environment. To do so, run the command conda env list and note down the path to the newly created environment tensorflow. In PyCharm go to File > Settings > Project: [Project name] > Project Interpreter. Then click the icon highlighted in this image: project_interpreter_settings

Then select Conda Environment and Existing Environment. As path choose the previously noted path to the conda environment. You have to select an executable, so select the python.exe in this folder. add_conda

Install all the requirements listet in requirements.txt. If you use Pycharm, open the requirements.txt file. PyCharm will analyze your installed packages in the conda environment and suggest to install the missing packages. If you later on find some dependencies to be missing, feel free to install them.

Step 1 - Download the data

You can either follow the steps described for Step 1 and 2, or you can download the images from the release of Step 3. In that case, download and extract the files.

Visit Microsoft and download the dataset. Unpack it to somwhere. You will have to move files from this directory in the next step.

Step 2 - Training Data vs Test Data

You can do this step manually or use the script provided.

The dataset does not come with separated training and test data. The separation of these two types of data is crucial, so do that now. Create a directory structure like this:

+-- data
    +-- train
        +-- Cat
        +-- Dog
    +-- test
        +-- Cat
        +-- Dog

Move into the folders under data/train the images with indices 0-9999 and move the images with indices from 10000-12500 to the test images. This corresponds to 80% train data and 20% test data.

Step 3 - Read the data

In order to learn from the data, you first have to pass this data to tensorflow. To do so, create a function that you can use as input_fn for the estimator. It should return cats and dogs similarily often, so don't have first only cats and then only dogs. The images should be randomly selected from all available training images. Also make sure, that this method is reusable: You will have to be able to use it for both the training and the test set.

Step 4 - Create the neural net

We provided you with a rough framework, you manly have to fill in the blanks. Take a look at reference implementations of Estimators for the MNIST dataset. You can learn a lot from there. Also consider using a highlevel wrapper, such as Keras for the creation of the net. But if you just want to start playing around, the pure tensorflow code will be enough.

Step 5 - Learn and evaluate the net

Now actually learn the net. But beware: There are so many hyperparameters, you will have your fun playing with these. You also may want to change the network architecture, as ours is not actually intended to win this competition :)

Problems you will encounter

Designing the input pipeline

This step is crucial. If you mess up here, things will be slower and, depending on your setup, by magnitudes slower. See here for some quick tips.

Image input

If you use the Tensorflow Dataset API, you will have to take special care of your input images. Some images may have a different format, than others, so you might want to do some preprocessing here. Maybe even external, to keep your code nice and clean. There will also be very bad examples of images in the pipeline. Do you really want to include those? And more importantly: How do you find out, that an image is a bad example? For example, look for images with the index 666. Here you may want to process images with another python script first and the either write them to the disk and use the input pipeline or use the images in RAM as input. This saves you from slow disk access.

Long learning times

In case your learning takes forever, consider a few points:

  • Is your net to huge? Having many Convolution Layers with large inputs will slow down things a lot. Also notice, that Tensorflow offers profiling tools, you might want to use them. There are many different architectures, that can cope with limited resources, e.g. SqueezeNet (this actually would run on a Raspberry Pi :) )
  • Use Cloud Computing. If your computers hardware simply is not powerful enough, consider using cloud services. Take a look at FloydHub (easy to setup, 20h of CPU time for free) or AWS.

Also make sure to check out Google Colaboratory, here you can run your code in a python notebook. Also they will give you access to a good GPU (for free), so make sure to check that out. The only "problem" is, that you cannot run your training for too long (e.g. over night). Using it for a few hours should be good though. In case you have access to a VPN, please use it. We are not sure, how Google is going to react to a larger amount of people draining their resources, all connecting from the same IP...


Although it generally is great, the scientific mode that comes with it, may break your code. If you see an error like Intel MKL FATAL ERROR: Cannot load mkl_intel_thread.dll. which disappears as soon as you run it in your console, you may want to disable the Show plots in tool window feature. See here for details.


If you use a GPU and CUDA on Windows, make sure to install the correct versions of the individual components. When we last installed tensorflow-gpu, we installed CUDA Version 9.0, cuDNN v7.0. Also make sure to add cuDNN to your PATH variable. Details can be found (somewhere) on the Tensorflow installation page and the linked websites.

Things to try

Just a short list of things on my mind. Some relate to architecture others to preprocessing.

  • Spatial Pyramid Pooling
  • Global Max Pooling
  • More Layers
  • More Dense Layers
  • More Filters
  • Larger Filters
  • Different Padding
  • Image Augmentation
    • Rotation
    • Scaling
    • Noise
    • Cropping
    • Other distortions
  • Grayscale images
  • Only external preprocessing
  • Cache images in RAM