딥 러닝 기초 함수들

pydl_funcs.py

import numpy as np

def step(x):
    return np.array( x > 0, dtype=np.int )

def sigmoid(x):
    y = 1 / (1 + np.exp(-x))
    return y

def relu(x):
	return np.maximum(0, x)

def softmax(x):
	exp_x = np.exp(x)
	sum_exp_x = np.sum(exp_x)
	y = exp_x / sum_exp_x
	return y

def softmax_overflow(x):
	c = np.max(x)
	exp_x = np.exp(x-c)
	sum_exp_x = np.sum(exp_x)
	y = exp_x / sum_exp_x
	return y