Wine quality

The Red Wine Quality dataset consists of physiochemical measurements from about 1600 Portuguese red wines. Also included is a quality rating for each wine from blind taste-tests.

raise SystemExit("Stop right there!");

An exception has occurred, use %tb to see the full traceback.

SystemExit: Stop right there!

Importing libraries and packages

# System
import os

# Mathematical operations and data manipulation
import pandas as pd

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

# Plotting
import matplotlib.pyplot as plt

%matplotlib inline

# Suppress
# 0 = all messages are logged (default behavior)
# 1 = INFO messages are not printed
# 2 = INFO and WARNING messages are not printed
# 3 = INFO, WARNING, and ERROR messages are not printed
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"

Set paths

# Path to datasets directory
data_path = "./datasets"
# Path to assets directory (for saving results to)
assets_path = "./assets"

Loading dataset

red_wine = pd.read_csv(f"{data_path}/winequality-red.csv")
red_wine.head()

	fixed acidity	volatile acidity	citric acid	residual sugar	chlorides	free sulfur dioxide	total sulfur dioxide	density	pH	sulphates	alcohol	quality
0	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5
1	7.8	0.88	0.00	2.6	0.098	25.0	67.0	0.9968	3.20	0.68	9.8	5
2	7.8	0.76	0.04	2.3	0.092	15.0	54.0	0.9970	3.26	0.65	9.8	5
3	11.2	0.28	0.56	1.9	0.075	17.0	60.0	0.9980	3.16	0.58	9.8	6
4	7.4	0.70	0.00	1.9	0.076	11.0	34.0	0.9978	3.51	0.56	9.4	5

red_wine.shape

(1599, 12)

Training of the network

model = keras.Sequential([layers.Dense(units=1, input_shape=[11])])

w, b = model.weights
print("Weights\n{}\n\nBias\n{}".format(w, b))

# Plotting
plt.style.use("seaborn-whitegrid")

# Set Matplotlib defaults
plt.rc("figure", autolayout=True)
plt.rc(
    "axes",
    labelweight="bold",
    labelsize="large",
    titleweight="bold",
    titlesize=18,
    titlepad=10,
)

Weights
<tf.Variable 'dense/kernel:0' shape=(11, 1) dtype=float32, numpy=
array([[-0.23026964],
       [ 0.4494403 ],
       [-0.18951398],
       [ 0.19517463],
       [-0.45655268],
       [-0.08020645],
       [ 0.16509748],
       [-0.15993416],
       [ 0.13550514],
       [ 0.12772518],
       [-0.33042252]], dtype=float32)>

Bias
<tf.Variable 'dense/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>

# Plotting an untrained model
model = keras.Sequential(
    [
        layers.Dense(1, input_shape=[1]),
    ]
)

x = tf.linspace(-1.0, 1.0, 100)
y = model.predict(x)

plt.figure(dpi=100)
plt.plot(x, y, "k")
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.xlabel("Input: x")
plt.ylabel("Target y")
w, b = model.weights  # you could also use model.get_weights() here
plt.title("Weight: {:0.2f}\nBias: {:0.2f}".format(w[0][0], b[0]))
plt.show()

# Plotting
plt.style.use("seaborn-whitegrid")

# Set Matplotlib defaults
plt.rc("figure", autolayout=True)
plt.rc(
    "axes",
    labelweight="bold",
    labelsize="large",
    titleweight="bold",
    titlesize=18,
    titlepad=10,
)

# Plotting an untrained model
model = keras.Sequential(
    [
        layers.Dense(1, input_shape=[1]),
    ]
)

x = tf.linspace(-1.0, 1.0, 100)
y = model.predict(x)

plt.figure(dpi=100)
plt.plot(x, y, "k")
plt.xlim(-1, 1)
plt.ylim(-1, 1)
plt.xlabel("Input: x")
plt.ylabel("Target y")
w, b = model.weights  # you could also use model.get_weights() here
plt.title("Weight: {:0.2f}\nBias: {:0.2f}".format(w[0][0], b[0]))
plt.show()