Regression using a DNN and multiple inputs is a machine learning technique that uses a deep neural network (DNN) to predict a continuous value from multiple inputs. The DNN is trained on a dataset of input-output pairs, and it learns to map the input to the output.
To build a DNN for regression using multiple inputs, you can use the Keras library in TensorFlow. Keras provides a simple API that makes it easy to build and train DNNs.
The following code shows how to build a DNN for regression using multiple inputs:
import tensorflow as tf
from tensorflow import keras
# Define the model
model = keras.Sequential([
keras.layers.Dense(64, activation='relu', input_shape=(3,)),
keras.layers.Dense(1, activation='linear')
])
# Compile the model
model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
# Fit the model
model.fit(x_train, y_train, epochs=100)
# Evaluate the model
model.evaluate(x_test, y_test)
This code will define a DNN with two layers: a hidden layer with 64 neurons and an output layer with 1 neuron. The input shape for the model is 3, which means that the model expects 3 inputs. The activation function for the hidden layer is 'relu', and the activation function for the output layer is 'linear'.
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 code above is just a simple example of how to build a DNN for regression using multiple inputs. There are many other ways to build and train DNNs for regression tasks.
