3 How to Make a Neural Network: A Beginners Guide - By Patrick Cooper

# I got this off the youtube channel https://www.youtube.com/watch?v=swJFNjIjxIE. By Patrick Cooper
# but it seems that one Cannot register 2 metrics with the same name: /tensorflow/api/keras/optimizers...
# I must have not set up my workspace correctly, I am assuming. By Snowcatman

from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
import numpy as np

x = np.random.uniform(-10, 10, 100000)
y = (x**2+5)*2

print(x)

def build_model():
  model = keras.Sequential([
    layers.Dense(64, input_dim=1, activation='relu'),
    layers.Dense(128, activation='relu'),
    layers.Dense(128, activation='relu'),
    layers.Dense(64, activation='relu'),
    layers.Dense(1)
  ])

  # mean square error will work for our purposes here
  model.compile(loss='mean_squared_error', optimizer='adam')
  return model

model = build_model()

model.summary()

model.fit(x, y, epochs=12, batch_size=500)

eval_set_in = x[:3]
eval_set_out = y[:3]
print("Generate predictions for 3 samples using neural network model:")
print("First 3 Evaluation Inputs:", eval_set_in)
dl_predictions = model.predict(eval_set_in).flatten()
print("First 3 Actual Outputs:", eval_set_out)
print("First 3 Evaluation Outputs:", dl_predictions)
print("Error:", abs(eval_set_out - dl_predictions))

poly_line = np.linspace(-10, 10, num=20)

plt.title('Input (x) versus Output (y)')
plt.xlabel('Input Variable (x)')
plt.ylabel('Output Variable (y)')
plt.plot(poly_line, model.predict(poly_line), linewidth=8, color="blue",
         label="Approximated")
plt.plot(poly_line, (poly_line**2+5)*2, linewidth=4, color="lightblue",
         label="Actual")
plt.legend()
plt.show()