#Activation function

import numpy as np
import nnfs 
from nnfs.datasets import spiral_data

nnfs.init()

X = [[1, 2, 3, 2.5],
		[2.0, 5.0, -1.0, 2.0],
		[-1.5, 2.7, 3.3, -0.8]]

X, y = spiral_data(100, 3)
# inputs = [0, 2, -1, 3.3, -2.7, 1.1, 2.2, -100]
# output = []

'''
for i in inputs:
	output.append(max(0, i))
	# if i>0:
	# 	output.append(i)
	# elif i<=0:
	# 	output.append(0)

print(output)
'''

class Layer_Dense:
	def __init__(self, n_inputs, n_neurons):
		self.weights = 0.10 * np.random.randn(n_inputs, n_neurons)
		self.biases = np.zeros((1, n_neurons))
	def forward(self, inputs):
		self.output = np.dot(inputs, self.weights) + self.biases

class Activation_ReLU:
	def forward(self, inputs):
		self.output = np.maximum(0, inputs)

layer1 = Layer_Dense(2, 5)
# layer2 = Layer_Dense(5, 2)

activation1 = Activation_ReLU()

layer1.forward(X)
activation1.forward(layer1.output)

print(activation1.output)
# print(layer1.output)
# activation1.forward(layer1.output)
#print(layer1.output)
# layer2.forward(layer1.output)
# print(layer2.output)
