SEQUENTIAL MODEL.py

#SEQUENTIAL MODEL
import tensorflow as tf

# Setup plotting
import matplotlib.pyplot as plt

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)

import pandas as pd
concrete = pd.read_csv(r"C:\Users\alvar\OneDrive\Escritorio\MLAI Tools\NN\Data\concrete.csv")



#Input shape
input_shape = [8]

#Define a model with hidden layers
from tensorflow import keras
from keras import layers

model = keras.Sequential([
    layers.Dense(512, activation='relu', input_shape=input_shape),
    layers.Dense(512, activation='relu'),
    layers.Dense(512, activation='relu'),    
    layers.Dense(1),
])
#activation layers
model = keras.Sequential([
    layers.Dense(32, input_shape=[8]),
    layers.Activation('relu'),
    layers.Dense(32),
    layers.Activation('relu'),
    layers.Dense(1),
])
#Optional alternative to ReLu 
#Change 'relu' to 'elu', 'selu', 'swish'... or something else
activation_layer = layers.Activation('relu')

x = tf.linspace(-3.0, 3.0, 100)
y = activation_layer(x) # once created, a layer is callable just like a function

plt.figure(dpi=100)
plt.plot(x, y)
plt.xlim(-3, 3)
plt.xlabel("Input")
plt.ylabel("Output")
plt.show()