Comprehensive Guide to Installing Keras and Theano with Anaconda Python on Windows

Introduction

In the field of deep learning, Keras has gained popularity as a high-level neural network API due to its simplicity and modular design. It supports multiple backend engines, with Theano being a powerful numerical computation library, especially for multi-dimensional array operations. However, when installing and configuring Keras and Theano on Windows systems, users often encounter dependency conflicts or import errors, such as ImportError: cannot import name gof. Based on community-verified best answers and supplemented by alternative approaches, this article provides a complete installation guide to help readers efficiently set up their development environment.

Environment Preparation and Tool Installation

Before installing Keras and Theano, ensure the system meets basic requirements. First, it is recommended to use a 64-bit Windows operating system and install the Anaconda Python distribution, which integrates package management tools like conda and pip to simplify dependency handling. If Anaconda is not yet installed, download it from the official website and follow the setup wizard. Next, install the TDM GCC compiler, a GNU compiler collection for Windows that provides necessary C language support for Theano. After downloading the TDM GCC x64 version, run the installer and ensure it is added to the system environment variables for command-line access.

With basic tools installed, open Anaconda Prompt, a command-line interface designed for Anaconda. First, update conda itself and all packages to ensure the latest versions are used. Execute the following commands:

conda update conda
conda update --all

These commands check and upgrade all installed packages, reducing the risk of version incompatibilities. Then, install mingw and libpython, which are essential for compiling Theano on Windows. Run:

conda install mingw libpython

This step resolves common compilation errors, such as missing GCC or Python library files.

Installing Theano and Keras

With the environment prepared, proceed to install Theano. Instead of using the standard pip install, which might fetch an outdated version, install the latest development version directly from the GitHub repository. This ensures compatibility with recent updates and bug fixes. In the Anaconda Prompt, run:

pip install git+git://github.com/Theano/Theano.git

This command uses Git to clone the repository and install Theano. If Git is not installed, download it from the official site, set up environment variables, and retry. After installation, verify Theano by importing it in Python: import theano. If no errors occur, proceed to install Keras similarly:

pip install git+git://github.com/fchollet/keras.git

Keras will automatically detect Theano as the backend. To confirm, run a test script that checks the backend:

python -c "from keras import backend; print(backend._BACKEND)"

This should output theano, indicating successful configuration.

Common Issues and Alternative Solutions

Despite following the above steps, some users may encounter persistent issues. An alternative solution is to create a conda virtual environment to isolate dependencies. For example, for Python 3.5, execute:

conda create --name neuralnets python=3.5
activate neuralnets

Within this environment, install Theano and Keras using conda and pip, respectively. This method minimizes conflicts with other Python installations. Additionally, if a simpler installation is preferred, try installing Keras from the conda-forge channel with the command: conda install --channel https://conda.anaconda.org/conda-forge keras. However, this may not include the latest version of Theano, so compatibility verification is recommended.

Code Example and Verification

To demonstrate functionality, consider a simple neural network example with Keras and Theano. The following code defines a sequential model with dense layers and uses the SGD optimizer:

from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.optimizers import SGD

model = Sequential()
model.add(Dense(units=64, input_dim=100))
model.add(Activation('relu'))
model.add(Dense(units=10))
model.add(Activation('softmax'))

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])

Running this code without import errors confirms that Keras and Theano are correctly installed. If errors like ImportError: cannot import name gof occur, review the Theano installation steps, as this often indicates an incompatible version or missing compilation dependencies.

Conclusion

Installing Keras and Theano on Windows via Anaconda requires attention to details such as compiler installation and using up-to-date versions. By following this step-by-step guide, which includes updating conda, installing mingw and libpython, and fetching directly from Git repositories, users can overcome common errors and establish a robust environment for neural network projects. It is always recommended to verify the setup with simple tests and consider virtual environments for managing dependencies in complex projects.