Tag: TensorFlow

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TensorFlow: Romancing with TensorFlow and NLP Elena Daehnhardt · 11 Jul, 2022 In this post we will create a simple poem generation model with Keras Sequential API. Read more...
Floating-point format and Mixed Precision in TensorFlow Elena Daehnhardt · 19 May, 2022 When creating large Machine Learning models, we want to minimise the training time. In TensorFlow, it is possible to do mixed precision model training, which helps in significant performance improvement because it uses lower-precision operations with 16 bits (such as float16) together with single-precision operations (f.i. using float32 data type). Google TPUs and NVIDIA GPUs devices can perform operations with 16-bit datatype much faster Read more...
TensorFlow: Evaluating the Saved Bird Species Prediction Model Elena Daehnhardt · 2 May, 2022 In this post, I have described the process of in-depth model evaluation. I have reused the previously created EffecientNetB0 model, which is fine-tuned with the 400 Bird Species Kaggle dataset. As a result, I have found out which bird species are not well predicted. Read more...
TensorFlow: Transfer Learning (Fine-Tuning) in Image Classification Elena Daehnhardt · 6 Apr, 2022 We used a 400 species birds dataset for building bird species predictive models based on EffeicientNetB0 from Keras. The baseline model showed already an excellent Accuracy=0.9845. However, data augmentation did not help in improving accuracy, which slightly lowered to 0.9690. Further, this model with a data augmentation layer was partially unfrozen, retrained with a lower learning rate, and reached an Accuracy=0.9850. Read more...
TensorFlow: Transfer Learning (Feature Extraction) in Image Classification Elena Daehnhardt · 3 Mar, 2022 Image classification is a complex task. However, we can approach the problem while reusing state-of-the-art pre-trained models. Using previously learned patterns from other models is named "Transfer Learning." This way, we can efficiently apply well-tested models, potentially leading to excellent performance. Read more...
TensorFlow: Convolutional Neural Networks for Image Classification Elena Daehnhardt · 19 Feb, 2022 In this post, I have demonstrated CNN usage for birds recognition using TensorFlow and Kaggle 400 birds species dataset. We observed how the model works with the original and augmented images. Read more...
TensorFlow: Multiclass Classification Model Elena Daehnhardt · 6 Feb, 2022 In Machine Learning, the classification problem is categorising input data into different classes. For instance, we can categorise email messages into two groups, spam or not spam. In this case, we have two classes, we talk about binary classification. When we have more than two classes, we talk about multiclass classification. In this post, I am going to address the latest multiclass classification, on the example of categorising clothing items into clothing types. Read more...
Feature preprocessing Elena Daehnhardt · 29 Jan, 2022 Machine Learning algorithms often require that data is in a specific type. For instance, we can use only numerical data. In other cases, ML algorithms would perform better or converge faster when we preprocess data before training the model. Since we do this step before training the model, we call it preprocessing. Read more...
TensorFlow: Evaluating the Regression Model Elena Daehnhardt · 25 Jan, 2022 In this post, we have performed the evaluation of four regression models using TensorFlow. MAE and MSE error metrics were used to compare the Sequential models while finding the best neural network architecture regarding the defined hyperparameters. Read more...
TensorFlow: Regression Model Elena Daehnhardt · 21 Jan, 2022 I have described regression modeling in TensorFlow. We have predicted a numerical value and adjusted hyperparameters to better model performance with a simple neural network. We generated a dataset, demonstrated a simple data split into training and testing sets, visualised our data and the created neural network, evaluated our model using a testing dataset. Read more...
TensorFlow: Global and Operation-level Seeds Elena Daehnhardt · 15 Jan, 2022 In training Machine Learning models, we want to avoid any ordering biases in the data. In some cases, such as in Cross-Validation experiments, it is essential to mix data and ensure that the order of data is the same between different runs or system restarts. We can use operation-level and global seeds to achieve the reproducibility of results. Read more...
Tensors in TensorFlow Elena Daehnhardt · 13 Jan, 2022 TensorFlow is a free OS library for machine learning created by Google Brain. Tensorflow has excellent functionality for building deep neural networks. I have chosen TensorFlow because it is pretty robust, efficient, and can be used with Python. In this post, I am going to write about how we can create tensors, shuffle them, index them, get information about tensors with simple examples. Read more...
TensorFlow on M1 Elena Daehnhardt · 5 Jan, 2022 TensorFlow is a free OS library for machine learning created by Google Brain. Tensorflow has excellent functionality for building deep neural networks. I have chosen TensorFlow because it is pretty robust, efficient, and can be used with Python. Since I like Jupyter Notebooks and Conda, they were also installed on my system. Next, I am going through simple steps to install TensorFlow and the packages above on M1 macOS Monterey. Read more...