TFLearn can be defined as a modular and transparent deep learning tool used in the TensorFlow framework. The main goal of TFLearn is to provide a higher-level API for TensorFlow to facilitate new experiments and research.
Consider these important features of TFLearn:
1. TFLearn is easy to use and understand.
2. It includes simple concepts for building highly modular network layers, optimizers, and various metrics embedded within them.
3. It has fully transparent interoperability with the TensorFlow working system.
4. It includes powerful helper functions for training built-in tensors that accept multiple inputs, outputs, and optimizers.
5. It provides easy-to-use and beautiful graphical visualization.
Graph visualizations include various details such as weights, gradients, and activations.
Install TFLearn by executing the following command −
pip install tflearn
After executing the above code, the following output will be generated −
The following example shows implementation of TFLearn using random forest classifier −
from __future__ import division, print_function, absolute_import
#TFLearn module implementation
import tflearn
from tflearn.estimators import RandomForestClassifier
# Data loading and pre-processing with respect to dataset
import tflearn.datasets.mnist as mnist
X, Y, testX, testY = mnist.load_data(one_hot