Kaggle 1 - A single neuron

import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow as tf
import matplotlib.pyplot as plt

# Set Matplotlib defaults
plt.style.use('seaborn-whitegrid')
plt.rc('figure', autolayout=True)
plt.rc('axes', labelweight='bold', labelsize='large', titleweight='bold', titlesize=18, titlepad=10)

# 1. Read and print the dataset
import pandas as pd
red_wine = pd.read_csv('../input/dl-course-data/red-wine.csv')
print(red_wine.head(), end='\n\n')
print(red_wine.tail())

# 2. A single neuron
input_shape = [11]
model = keras.Sequential([ layers.Dense(units=1, input_shape=input_shape) ])

# 3. Print the weights and the bias
w, b = model.weights
print("Weights = \n{}\n\nBias = \n{}".format(w, b))

# 4. NOT TRAINING means: random weight and bias = 0
for i in range(3):
    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()