TensorFlow 1.x Deep Learning Cookbook

How to do it...

Here is how we start with function approximation using MLP:

1. Import the modules needed--sklearn for datasets, preprocessing data, and splitting it into train and test; Pandas to understand the dataset; and matplotlib and seaborn to visualize:

import tensorflow as tf 
import tensorflow.contrib.layers as layers 
from sklearn import datasets 
import matplotlib.pyplot as plt 
from sklearn.model_selection  import train_test_split 
from sklearn.preprocessing import MinMaxScaler 
import pandas as pd 
import seaborn as sns 
%matplotlib inline

2. Load the dataset and create a Pandas dataframe to understand the data:

# Data boston = datasets.load_boston() df = pd.DataFrame(boston.data, columns=boston.feature_names) df['target'] = boston.target ...