Running data through a network

Function Signature

def run(
    self,
    inputs: np.ndarray
) -> np.ndarray:

Parameters

• inputs (np.ndarray): The input data to run through the network. This should be a numpy array of shape (input_shape,).

Return Value

• output (np.ndarray): The output of the network after running the given input through it. This should be a numpy array of shape (output_shape,).

Description

The run function takes a single input and runs it through the network, returning the output of the network.

The inputs parameter should be a numpy array of shape (input_shape,), where input_shape is the shape of the input to the network.

The output parameter is a numpy array of shape (output_shape,), where output_shape is the shape of the output of the network.

Examples

Here's an example of how to use the run function:

from deeprai.models import FeedForward

model = FeedForward()
model.add_dense(784)
model.add_dense(128, activation='relu')
model.add_dense(64, activation='relu')
model.add_dense(10, activation='softmax')
model.config(loss='categorical cross entropy')

input_data = np.random.rand(784)
output_data = model.run(input_data)

This code creates a FeedForward model with a single dense layer of size 784, followed by two additional dense layers with ReLU activation functions, and a final dense layer with a softmax activation function. The config function sets the optimizer to gradient descent and the loss function to categorical cross entropy.

The run function takes a single input data of shape (784,) and returns the output of the network as a numpy array of shape (10,).