TensorFlow - TFLearn And Its Installation
TensorFlow - TFLearn And Its Installation - TFLearn can be defined as a modular and transparent deep learning aspect used in TensorFlow framework. The main motive of TFLearn is to provide a higher level A
TFLearn can be defined as a modular and transparent deep learning aspect used in TensorFlow framework. The main motive of TFLearn is to provide a higher-level API to TensorFlow for facilitating and showing up new experiments.
Consider the following important features of TFLearn −
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TFLearn is easy to use and understand.
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It includes easy concepts to build highly modular network layers, optimizers and various metrics embedded within them.
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It includes full transparency with TensorFlow work system.
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It includes powerful helper functions to train the built-in tensors which accept multiple inputs, outputs, and optimizers.
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It includes easy and beautiful graph visualization.
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The graph visualization includes various details of weights, gradients, and activations.
Install TFLearn by executing the following command −
pip install tflearn
Upon execution of the above code, the following output will be generated −
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])