![]() ![]() The optimize troubleshoots the model during training and removes errors. It is used to enhance the model performance as it learns from the train set. This determines the probability of model-making accurate predictions. It is used to calculate the accuracy score of the neural network. In model compiling, we determine the metrics, the optimizer, and the loss function to be used by the neural network. The number of non-trainable parameters is more as compared to the trainable parameters. The non-trainable parameters (2,257,984) are from the feature_extractor_layer and they are already trained. The trainable parameters (3,843) are the ones the neural network will train. Some parameters are trainable while others are non-trainable. It also shows the total model parameters (2,261,827). The image shows the model type (Sequential) and the initialized layers. We extract the layer using the following code: For further understanding of how the convolutional and pooling layers work, read this article. This layer is very important and is used to extract the important features from the input image. The feature extractor layer of the MobileNet-v2 model is made up of a collection of stacked convolutional and pooling layers. Extract the feature extractor layer from the MobileNet-v2 model We then use the feature extractor layer as the input layer when building the model. To use this model, we extract the feature extractor layer from the MobileNet-v2 model. We will apply this model to classify images of hands playing rock, paper, scissor games. For further understanding of the convolutional neural network architecture, read this article. It is made up of a feature extractor layer (collection of convolutional and pooling layers) and fully connected layers. MobileNet-v2 follows the convolutional neural network architecture. This model is already pre-trained using different images. We will download the rock, paper, scissors image dataset from tensorflow_datasets using the following code: It is a TensorFlow repository that is made up of a collection of ready-to-use datasets. It also enables us to perform mathematical operations on arrays. It will convert the image dataset into arrays. The OS module in Python provides functions for creating and removing a directory, fetching its contents, changing and identifying the current directory. It enables us to interact with the operating system. It is a TensorFlow repository that contains a collection of pre-trained models. We use it to create the input, dropout, and dense layers for our image classification model. It is an open-source library for machine learning and artificial intelligence. We use Matplotlib to plot line graphs, figures, and diagrams. The functions of each of these libraries are as follows: The libraries are important in building our transfer learning model. Know basics of convolution neural networks.įor this tutorial, import the following libraries.Know how to build deep learning models using TensorFlow.To follow along with this tutorial, a reader should: Extract the feature extractor layer from the MobileNet-v2 model.Downloading the MobileNet-v2 convolutional neural network.We will then fine-tune it to classify images of hands playing rock, paper, scissor games. We will download a pre-trained MobileNet-v2 convolutional neural network from the TensorFlow hub. In this tutorial, we will build a model that classifies images of hands playing rock, paper, scissor games. The neural network is fine-tuned to meet the user’s needs rather than being trained from scratch. Lemons and oranges are different but related problems. Transfer learning decreases the training time and produces a model that performs well.įor example, knowledge gained while learning to recognize lemons could apply when trying to recognize oranges. It focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. Transfer learning is a technique that trains a neural network on one problem and then applies the trained neural network to a different but related problem. ![]()
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