tensorflow

TensorFlow High-Level Libraries: TF Estimator

TensorFlow has several high-level libraries allowing us to reduce time modeling all with core code. TF Estimator makes it simple to create and train models for training, evaluating, predicting and exporting. TF Estimator provides 4 main functions on any kind of estimator: estimator.fit() estimator.evaluate() estimator.predict() estimator.export() All predefined estimators are...

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Loss Functions (Part 1)

Implementing Loss Functions is very important to machine learning algorithms because we can measure the error from the predicted outputs to the target values. Algorithms get optimized by evaluating outcomes depending on a specified loss function, and TensorFlow works in this way as well. We can think on Loss Functions...

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Activation Functions (updated)

Table of Contents What is an activation function? Activation Functions Sigmoid ReLU (Rectified Linear Unit) ReLU6 Hyperbolic Tangent ELU (Exponential Linear Unit) Softmax Softplus Softsign Swish Sinc Leaky ReLU Mish GELU (Gaussian Error Linear Unit) SELU (Scaled Exponential Linear Unit) What is an activation function? An activation function is a...

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Declaring tensors in TensorFlow

[Requirement: Tensorflow and NumPy installed on Python +3.5][Requirement: import tensorflow as tf][Requirement: import numpy as np] Tensors are the primary data structure we use in TensorFlow, and, as Wikipedia describes them, “tensors are geometric objects that describe linear relations between geometric vectors, scalars and other tensors”. Tensors can be described...

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