Think about the accompanying significant highlights of TFLearn −

- TFLearn is not difficult to utilize and comprehend.
- It incorporates simple ideas to fabricate profoundly particular organization layers, analyzers and different measurements installed inside them.
- It incorporates full straightforwardness with TensorFlow work framework.
- It incorporates strong assistant capacities to prepare the underlying tensors which acknowledge different information sources, results and analyzers.
- It incorporates simple and delightful chart representation.
- The diagram perception incorporates different subtleties of loads, slopes and actuations.

```
pip install tflearn
```

The following illustration shows the implementation of TFLearn with 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 = False>
```

m = RandomForestClassifier(n_estimators = 100, max_nodes = 1000>

m.fit(X, Y, batch_size = 10000, display_step = 10>

print("Compute the accuracy on train data:">

print(m.evaluate(X, Y, tflearn.accuracy_op>

>

print("Compute the accuracy on test set:">

print(m.evaluate(testX, testY, tflearn.accuracy_op>

>

print("Digits for test images id 0 to 5:">

print(m.predict(testX[:5]>

>

print("True digits:">

print(testY[:5]>