Snippet code for feed forward neural network

feed_forward_neural_network.py

import pandas as pd
import numpy as np
import random
from sklearn.datasets import make_regression

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# require for installation: !pip install -q git+https://github.com/tensorflow/docs
import tensorflow_docs as tfdocs
import tensorflow_docs.plots
import tensorflow_docs.modeling

import matplotlib.pyplot as plt
%matplotlib inline
np.set_printoptions(suppress=True)

SEED = 515

X, y, coef = make_regression(n_samples=10000, n_features=5, noise=12.3, 
                             bias=100, random_state=121, coef_ind=True)
model = keras.Sequential([
  layers.Dense(16, activation='relu', input_shape=[X.shape[1]]),
  layers.Dense(1)
])
optimizer = tf.keras.optimizers.RMSprop(0.001)
model.compile(loss='mse',
              optimizer=optimizer,
              metrics=['mae', 'mse'])

history = model.fit(
  X, y, epochs=500, validation_split=0.2, verbose=0,
  callbacks=[tfdocs.modeling.EpochDots()]
)

plotter = tfdocs.plots.HistoryPlotter(smoothing_std=2)
plt.figure(figsize=(6, 4))
plotter.plot({'Basic': history}, metric="mae")
plt.ylabel('MAE [y]')
plt.show()