Basic-Tensorflow-Codes

This repository contains codes for implementation of ML and DL algorithms in tensorflow.

Dependencies

• Libraries tensorflow,matplotlib and numpy in python 2.x

Codes

• basics_math.py : Basics of creating and using tensors and plotting graphs.
• linreg.py : Implementing linear regression on a generated set of points.
• polyreg.py : Implementing regression with polynomial features on a generated set of points.
• logreg.py : Implementing logistic regression using a single layer neural net on the MNIST data set.
• Feedforward_NN/ffnn_raw_tf.py : Implementing a 2-layer feedforward neural network to classify the MNIST data set(Raw Tensorflow Implementation)
• Feedforward_NN/ffnn_using_layers_tf.py : Implementing a 2-layer feedforward neural network to classify the MNIST data set(Implemented using tf.layers.dense and tf.estimatorAPIs) .
• CNN/cnn_raw_tf.py : Implementing a convolutional neural network to classify the MNIST data set(Raw Tensorflow Implementation)
• RNN/singlelayer_lstm_mnist.py : Implementing a single(hidden)layer RNN-LSTM to classify the MNIST data set.