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, y_train), (x_test, y_test) = mnist.load_data() Convert the sample data from integers to floating-point numbers: mnist = tf.
Jupyter notebook online open ipynb install#
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.