Installing Tensorflow was once challenging, bordering on impossible. Now... not so much.
Anaconda is a data science ecosystem, giving users access to all sorts of toolkits and frameworks. Download the Anaconda installer for Windows (it's ~500MB) and install. See, that was easy!
If you don't already have them installed, you'll need to download and install the Visual C++ Build Tools.
Anaconda provides a simple tool for installing TF in a virtual environment. Open up a terminal prompt and type conda create -n tensorflow python=3.5 . This will create a virtual environment for called tensorflow that you'll use to encapsulate all your tensorflow libraries!
Conda Forge is a community led source for conda packages. It provides a simple way to install the packages we're looking for!
Activate your environment using activate tensorflow.
Now, we're going to install tf and keras into our virtual anaconda environment using packages from Conda Forge. With your tensorflow env active:
conda install -c conda-forge keras tensorflow
Now use conda list to see all packages installed in your env.
Look at that! Tensorflow and Keras are both installed!
Copy the following code into a new file called kerastest.py
from keras.models import Sequential from keras.layers import Dense, Dropout, Activation from keras.optimizers import SGD model = Sequential() model.add(Dense(64, input_dim=20, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add(Dense(64, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add(Dense(10, init='uniform')) model.add(Activation('softmax')) sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) print('done')
If it works, then your environment is ready to go.
Get Learning!