## Analyzing the Iris dataset

Let's look at a feedforward example using the Iris dataset.

### Note

You can download the dataset from https://github.com/ml-resources/neuralnetwork-programming/blob/ed1/ch02/iris/iris.csv and the target labels from https://github.com/ml-resources/neuralnetwork-programming/blob/ed1/ch02/iris/target.csv.

In the Iris dataset, we will use 150 rows of data made up of 50 samples from each of three Iris species: Iris setosa, Iris virginica, and Iris versicolor.

Petal geometry compared from three iris species:**Iris Setosa**, **Iris Virginica**, and **Iris Versicolor**.

In the dataset, each row contains data for each flower sample: sepal length, sepal width, petal length, petal width, and flower species. Flower species are stored as integers, with 0 denoting Iris setosa, 1 denoting Iris versicolor, and 2 denoting Iris virginica.

First, we will create a `run()`

function that takes three parameters--hidden layer size `h_size`

, standard deviation for weights `stddev`

, and Step size of Stochastic Gradient...