3/5/2023 0 Comments Tensorflow vstack![]() ![]() While this can make the model output more directly interpretable, this approach is discouraged as it's impossible to provide an exact and numerically stable loss calculation for all models when using a softmax output.ĭefine a loss function for training using losses.SparseCategoricalCrossentropy, which takes a vector of logits and a True index and returns a scalar loss for each example. Note: It is possible to bake the tf.nn.softmax function into the activation function for the last layer of the network. The tf.nn.softmax function converts these logits to probabilities for each class: tf.nn.softmax(predictions).numpy()Īrray(], ![]() predictions = model(x_train).numpy()Īrray(], Tf.(128, activation='relu'),įor each example, the model returns a vector of logits or log-odds scores, one for each class. X_train, x_test = x_train / 255.0, x_test / 255.0īuild a tf.keras.Sequential model by stacking layers. (x_train, y_train), (x_test, y_test) = mnist.load_data() Convert the sample data from integers to floating-point numbers: mnist = tf. Note: Make sure you have upgraded to the latest pip to install the TensorFlow 2 package if you are using your own development environment. If you are following along in your own development environment, rather than Colab, see the install guide for setting up TensorFlow for development. Print("TensorFlow version:", tf._version_) Import TensorFlow into your program to get started: import tensorflow as tf Run all the notebook code cells: Select Runtime > Run all.In Colab, connect to a Python runtime: At the top-right of the menu bar, select CONNECT.To follow this tutorial, run the notebook in Google Colab by clicking the button at the top of this page. Python programs are run directly in the browser-a great way to learn and use TensorFlow. This tutorial is a Google Colaboratory notebook. Build a neural network machine learning model that classifies images. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |