<gh_stars>1-10
"""This script will tell simple neural network
training with forward pass method.
Assume we want to train and predict number of 7,8,9,10 into linear function f(x) = 3x + 2"""

import numpy as np

def forwardPass(inputs, weight, bias):
	w_sum = np.dot(inputs, weight) + bias
	
	# Linear Activation f(x) = x
	act = w_sum

	return act

# Pre-Trained Weights & Biases after Training
W = np.array([[2.99999928]])
b = np.array([1.99999976])

# Initialize Input Data
inputs = np.array([[7], [8], [9], [10]])

# Output of Output Layer
o_out = forwardPass(inputs, W, b)

print("Output Layer Output (Linear)")
print("============================")
print(o_out, "\n")

"""
The output will be
Output Layer Output (Linear)
============================
(array([[ 22.99999472],
       [ 25.999994  ],
       [ 28.99999328],
       [ 31.99999256]]), '\n')

The true math is 23,26,29,32 There was minimum error in calculation
"""

"""This script will tell simple neural network
training with forward pass method.
Assume we want to train and predict number of 7,8,9,10 into linear function f(x) = 3x + 2"""

import numpy as np

def forwardPass(inputs, weight, bias):
	w_sum = np.dot(inputs, weight) + bias
	
	# Linear Activation f(x) = x
	act = w_sum

	return act

# Pre-Trained Weights & Biases after Training
W = np.array([[2.99999928]])
b = np.array([1.99999976])

# Initialize Input Data
inputs = np.array([[7], [8], [9], [10]])

# Output of Output Layer
o_out = forwardPass(inputs, W, b)

print("Output Layer Output (Linear)")
print("============================")
print(o_out, "\n")

"""
The output will be
Output Layer Output (Linear)
============================
(array([[ 22.99999472],
       [ 25.999994  ],
       [ 28.99999328],
       [ 31.99999256]]), '\n')

The true math is 23,26,29,32 There was minimum error in calculation
"""
This script will tell simple neural network training with forward pass method. Assume we want to train and predict number of 7,8,9,10 into linear function f(x) = 3x + 2 # Linear Activation f(x) = x # Pre-Trained Weights & Biases after Training # Initialize Input Data # Output of Output Layer The output will be Output Layer Output (Linear) ============================ (array([[ 22.99999472],        [ 25.999994  ],        [ 28.99999328],        [ 31.99999256]]), '\n')  The true math is 23,26,29,32 There was minimum error in calculation