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TensorFlow | NLP | Create embedding with pre-trained models
embeddings in machine learning are used to represent text with embedding vectors. TensorFlow hub module provides several pre trained text-embeddings models to convert sentence into embedding vectors.
TensorFlow | Stock Price Prediction With TensorFlow Estimator
Code snippets for this post is written with TensorFlow2.0. This post is intended to provide information on Tensorflow canned estimators, using this in live trading is not advised
TensorFlow | How to use tf.reduce_sum in TensorFlow
tf.reduce_sum in TensorFlow reduces input_tensor along the dimensions given in axis. If axis is None, all dimensions are reduced, and a tensor with a single element is returned. Below are the example for tf.reduce_sum in TensorFlow
Extract text from images using keras-ocr in Python
This post describes how to extract text form images using keras-ocr in Python.
How to use text_dataset_from_directory in TensorFlow
This post describes how to use text_dataset_from_directory in TensorFlow.
TensorFlow | NLP | Sentence similarity using TensorFlow cosine function
tf.keras.losses.cosine_similarity function in tensorflow computes the cosine similarity between labels and predictions.
Deploying TensorFlow Models on Flask Part 1 - Set up trained model from TensorFlow Hub
Part 1 of multi part series for deploying TensorFlow pre trained model on Flask.
How to implement Sequential model with tk.keras
TensorFlow provides implementation of Sequential model with tk.keras.Sequential API. Sequential model is used when each layer has only one input tensor and one output tensor.
How to use 2D convolution layer in TensorFlow | tf.keras
The convolution layer uses filters that perform convolution operations as it is scanning the input I with respect to its dimensions.TensorFlow provides tf.keras.layers.Conv2D class for implementing 2D convolution layer.
tf.keras | Image classification with MobileNetV2 model
In this tutorial we will see how to use MobileNetV2 pre trained model for image classification. MobileNetV2 is pre-trained on the ImageNet dataset. MobileNetV2 model is available with tf.keras api.
How to use sentence Tokenizer with Keras
Tokenization is an important step of pre-processing the data,Tokenizer class in Keras turns text into a sequence of integers.
TesnorFlow | How to load mnist data with TensorFlow Datasets
Overview of TensorFlow Datasets, a collection of ready-to-use datasets. Loading datasets with TensorFlow Datsets
How to get weights of layers in TensorFlow
This tutorial explains how to get weights of dense layers in keras Sequential model.
Deploying TensorFlow Models on Flask Part 3 - Integrate ML model with Flask
In the Part 2 of this series we have set flask application, in this Part 3 we will integrate TensorFlow Model with our Flask application.
How to create tensors in TensorFlow
This post describes How to create tensors in TensorFlow
Deploying TensorFlow ML model using Flask for Beginners
This post describes how to deploy a TensorFlow model using Flask.
How to use EarlyStopping callback in TensorFlow with Keras
This post describes how to use EarlyStopping callback in TensorFlow with Keras API.
How to create Regression Model in TensorFlow
This post describes how to create regression model in TensorFlow
How to use GlobalMaxPooling2D layer in TensorFlow
This posts explains how to use GlobalMaxPooling2D layer with TensorFlow.
How to extract features from layers in TensorFlow
Feature extraction in quite common while using transfer learning in ML.In this tutorial you will learn how to extract features from tf.keras.Sequential model.
TensorFlow | one hot encoding of categorical features in TensorFlow
In TensorFlow Categorical values can be transformed to one-hot-encoded vectors by using tf.feature_column.categorical_column_with_vocabulary_list function with tf.feature_column.indicator_column function.
TensorFlow | Word embeddings with ELMo
ELMo is a pre-trained model provided by google for creating word embeddings. It can be used directly from TensorFlow hub.
How to convert word into vector with GloVe
GloVe is an unsupervised learning algorithm for obtaining vector representations for words.
How to install TensorFlow with conda
Installation of TensorFlow CPU can be done in 3 simple steps
TensorFlow tf.keras.activations.serialize
Overview of TensorFlow tf.keras.activations.serialize() method.
How to add tensors in TensorFlow
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools to build and deploy ML powered applications.
TensorFlow | Image processing with tf.io and tf.image
TensorFlow provides tf.io and tf.image modules for reading and processing the images.
TensorFlow tf.keras.activations.deserialize
Overview of TensorFlow tf.keras.activations.deserialize() method.
Convert numpy arrays to tensors in TensorFlow
This post describes how to use tf.convert_to_tensor method to covert Python objects to tensors.
How to calculate euclidean norm in TensorFlow
This post describes how to calculate euclidean norm in TensorFlow.
How to use TensorBoard in TensorFlow
This post describes how to use TensorBoard in TensorFlow.
How to install latest TensorFlow version using PIP and Conda
This post describes how to install Latest TensorFlow Version using conda and PIP.
TensorFlow | How to use tf.GradientTape()
TensorFlow tf.GradientTape() records operations for automatic differentiation. Below are the code snippet for calculating gradient of loss function. This post uses some other TensorFlow apis like tf.reduce_sum, tf.constant and tf.variable as well.
TensorFlow | How to use tf.stack() in tensorflow
stack() method of TensorFlow creates a stacked tensor whose rank is one higher than each tensors given in "value" parameter.
