From Training to Prediction: TensorFlow Models for Decision Making | CodeSignal Learn

From Training to Prediction: TensorFlow Models for Decision Making | CodeSignal Learn

Introduction

Congratulations on successfully building and training your Neural Network model in TensorFlow. Now comes the exciting part—using your trained model to make predictions. In this lesson, we will guide you through the process of creating new inputs for your model, making predictions using the predict() method, and interpreting the model's output. By the end of this lesson, you'll have the skills to apply your model to real-world data and make insightful predictions. Let's dive in and see how we can use our model to predict whether a student will pass or fail based on their study and sleep hours!

Quick Refresh on Model Training

To ensure we're on the same page, let's quickly recap the model we built and trained in previous lessons: Python import numpy as np import tensorflow as tf # Example data: hours studied, hours slept X = np.array([[4, 6], [5, 7], [2, 8], [1, 3], [3, 4], [0, 5], [1, 1], [2, 4], [3, 5], [5, 5], [0, 4], [4, 4],]) # Labels: 1 if passed, 0 if failed y = np.array([[1], [1], [1], [0], [0], [0], [0], [0], [1], [1], [0], [1]]) # Define the model with 2 inputs and 1 output model = tf.keras.Sequential([tf.keras.layers.Input(shape=(2,)), tf.keras.layers.Dense(5, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid')]) # Compile the model model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Train the model model.fit(X, y, epochs=10) In this example, we defined a neural network model that takes in two features—hours studied and hours slept—and outputs a probability for passing or failing. We compiled the model using the Adam optimizer, binary cross-entropy as the loss function, and accuracy as a metric. Finally training the model for 10 epochs.

Creating New Inputs for our Model

When we have successfully trained a machine learning model, our ultimate goal is usually to make predictions or decisions based on new, unseen data. In our student pass/fail prediction scenario, suppose we have a new student who has studied for 3 hours and slept for 6 hours on the day before an exam. We would like to use our model to predict whether this student will pass or fail. To do this, we must create a new input in the same format that our model has been trained on. You'll recall that our model was trained on arrays with the structure [hours studied, hours slept] . Here's how we can create a new input for our model using numpy : Python new_input = np.array([[3, 6]]) In this code, np.array() is a function from the numpy library that creates an array using the input data provided. The input is [3, 6], corresponding to the two features (hours studied and hours slept) that our model requires to make a prediction. Alternatively, the input could also be a tensor, created using TensorFlow's tf.constant() : Python new_input = tf.constant([[3, 6]]) Both approaches will correctly format the new input data needed for prediction.

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Predicting Outcomes with New Inputs

Now that we have a new input for our model, we can use it to make a prediction. For this, we use the predict() method provided by TensorFlow : prediction = model.predict(new_input) print(prediction) The output of the above code will be: [[0.75]] When run, the predict() method applies the model to our new input and returns a prediction. It's important to note that the prediction is in the form of a probability output by the model's final sigmoid activation function. This probability gives us insight into the model's certainty about its prediction, where a value closer to 0 indicates a stronger prediction of the 'fail' class and a value closer to 1 indicates a stronger prediction of the 'pass' class.

Converting Predicted Probabilities into Labels

Finally, we want to convert our raw model output into a class label so that we get a clear prediction: 'pass' or 'fail'. As mentioned above, this is done by applying a threshold to the outputted probabilities. Here, we use a threshold of 0.5 (since we're dealing with a binary classification task): print("Prediction (0=fail, 1=pass):", (prediction > 0.5).astype(int)[0][0]) The output will be: Prediction (0=fail, 1=pass): 1 This output demonstrates that the model predicts the student will pass, as the predicted probability of passing (0.75) is greater than the 0.5 threshold. This simplistic example illustrates how a model's probability output is translated into a more understandable class label.