nn.py

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflowjs as tfjs

url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'
column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',
                'Acceleration', 'Model Year', 'Origin']

raw_dataset = pd.read_csv(url, names=column_names,
                          na_values='?', comment='\t',
                          sep=' ', skipinitialspace=True)

dataset = raw_dataset.copy()
dataset.tail()

dataset.isna().sum()
dataset = dataset.dropna()

dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})
dataset = pd.get_dummies(dataset, prefix='', prefix_sep='')
dataset.tail()

train_dataset = dataset.sample(frac=0.8, random_state=0)
test_dataset = dataset.drop(train_dataset.index)

train_features = train_dataset.copy()
test_features = test_dataset.copy()

train_labels = train_features.pop('MPG')
test_labels = test_features.pop('MPG')

normalizer = preprocessing.Normalization()
normalizer.adapt(np.array(train_features))

first = np.array(train_features[:1])

with np.printoptions(precision=2, suppress=True):
  print('First example:', first)
  print()
  print('Normalized:', normalizer(first).numpy())



horsepower_model = keras.Sequential([

tf.keras.layers.LayerNormalization(
    axis=-1, dtype='float64', epsilon=0.001, input_shape=[1]
),

    layers.Dense(2, activation='relu'),

    layers.Dense(1)
])


horsepower_model.compile(
    optimizer=tf.optimizers.Adam(learning_rate=0.1),
    loss='mean_absolute_error')


history = horsepower_model.fit(
    train_features['Horsepower'], train_labels,
    epochs=100,
    # suppress logging
    verbose=1,
    # Calculate validation results on 20% of the training data
    validation_split = 0.2)