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Now let's use the trained model to make some predictions on unlabeled examples . that is, on examples that contain features but not a label. There are several categories of neural networks and this program uses a dense, or fully-connected neural network. Machine learning provides many algorithms to classify flowers statistically. If you feed enough representative examples into the right machine learning model type, the program will figure out the relationships for you. A training loop feeds the dataset examples into the model to help it make better predictions. We need to select the kind of model to train. The first four fields are features. You can start to see some clusters by plotting a few features from the batch. We've trained a model and "proven" that it's good—but not perfect—at classifying Iris species. Our ambitions are more modest—we're going to classify Iris flowers based on the length and width measurements of their sepals and petals.

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Description: See the Datasets Quick Start guide for more information. Here, the fields hold float numbers representing flower measurements. Using the example's features, make a prediction and compare it with the label. The ideal number of hidden layers and neurons depends on the problem and the dataset. Setup program Configure imports and eager execution Import the required Python modules—including TensorFlow—and enable eager execution for this program. Use the model to make predictions about unknown data. Our ambitions are more modest—we're going to classify Iris flowers based on the length and width measurements of their sepals and petals. Variable 0 We'll use this to calculate a single optimization step. To convert these logits to a probability for each class, use the softmax function.. To simplify the model building step, create a function to repackage the features dictionary into a single array with shape.
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