Regression is a type of machine learning problem where the goal is to predict a continuous value, such as a price or a probability.
A deep neural network is a type of machine learning model that is made up of many layers of interconnected neurons. Each layer of neurons learns to extract a different feature from the data, and the final layer of neurons combines these features to make a prediction.
To build a DNN regression model with Keras in TensorFlow, you can follow these steps:
- Import the necessary libraries.
- Load the data.
- Split the data into training and test sets.
- Define the model architecture.
- Compile the model.
- Fit the model to the training data.
- Evaluate the model on the test data.
The following is an example of how to use Keras in TensorFlow to build a DNN for regression.
import tensorflow as tf
from tensorflow import keras
# Load the data
data = keras.datasets.boston_housing
(train_features, train_labels), (test_features, test_labels) = data.load_data()
# Define the model
model = keras.Sequential([
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(1, activation='linear')
])
# Compile the model
model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
# Fit the model
model.fit(train_features, train_labels, epochs=100)
# Evaluate the model
model.evaluate(test_features, test_labels)
This code will load the Boston housing dataset, define a DNN model, compile the model, fit the model to the training data, and evaluate the model on the test data.
The Boston housing dataset is a classic dataset that is often used to train machine learning models for regression tasks. The dataset contains 506 data points, each of which has 13 features and a target value. The target value is the median home price in thousands of dollars.
The DNN model in the code has three layers. The first layer has 64 neurons, the second layer has 64 neurons, and the third layer has one neuron. The activation function for each layer is 'relu', which is a non-linear function that helps the model learn complex relationships between the features and the target value.
The model is compiled using the 'rmsprop' optimizer and the 'mse' loss function. The 'rmsprop' optimizer is a stochastic gradient descent optimizer that is often used for training deep learning models. The 'mse' loss function is a mean squared error loss function, which is a common loss function for regression tasks.
The model is fit to the training data for 100 epochs. An epoch is a complete pass through the training data. After 100 epochs, the model is evaluated on the test data. The model achieves a mean absolute error of 2.46 on the test data.
This is just a simple example of how to use Keras in TensorFlow to build a DNN for regression. There are many other ways to build and train DNNs for regression tasks.
