"""
numpynet_common
Contains common code shared across different modules
@author: Chronocook (chronocook@gmail.com)
"""
import numpy as np
from numpynet.loggit import log
class Activation:
"""
A class to hold all of the activation functions, ensures all have derivatives
"""
function = None
available = ["tanh", "tanhpos", "sigmoid", "relu", "softplus", "leakyrelu"]
def __init__(self, choice="sigmoid"):
"""
:param choice: Which activation function you want, must be in self.available
"""
if choice not in self.available:
msg = "Choice of activation (" + choice + ") not available!"
log.out.error(msg)
raise ValueError(msg)
elif choice == "tanh":
self.function = self._tanh
elif choice == "tanhpos":
self.function = self._tanhpos
elif choice == "sigmoid":
self.function = self._sigmoid
elif choice == "softplus":
self.function = self._softplus
elif choice == "relu":
self.function = self._relu
elif choice == "leakyrelu":
self.function = self._leakyrelu
@staticmethod
def _tanh(x, deriv=False):
"""
Hyperbolic tangent activation
"""
if deriv:
return 1.0 - np.power(np.tanh(x), 2)
return np.tanh(x)
@staticmethod
def _tanhpos(x, deriv=False):
"""
Positive hyperbolic tangent activation
"""
if deriv:
return (1.0 - np.power(np.tanh(x), 2)) / 2.0
return (np.tanh(x) + 1.0) / 2.0
@staticmethod
def _sigmoid(x, deriv=False):
"""
The sigmoid function and its derivative
"""
y = 1.0 / (1.0 + np.exp(-x))
if deriv:
return y * (1.0 - y)
return y
@staticmethod
def _softplus(x, deriv=False):
"""
The soft-plus function and its derivative
"""
y = np.log(1.0 + (np.exp(x)))
if deriv:
return 1.0 / (1.0 + np.exp(-x))
return y
@staticmethod
def _relu(x, deriv=False):
"""
Rectified linear unit activation function
"""
if deriv:
return 1.0 * (x >= 0)
return np.maximum(x, 0)
@staticmethod
def _leakyrelu(x, deriv=False):
"""
Rectified linear unit activation function with small value for negatives
"""
if deriv:
return 1.0 * (x >= 0) - 0.01 * (x < 0)
return np.maximum(x, 0.01 * x)
def predict_2d_space(net, delta=0.05):
"""
Iterate predictions over a 2d space
:param net: (object) A NumpyNet model object
:param delta: space between predictions
:return: prediction_matrix: the actual predictions
axis_x and axis_y: the axes (useful for plotting)
"""
axis_x = np.arange(net.predict_space[0], net.predict_space[1] + delta, delta)
axis_y = np.arange(net.predict_space[2], net.predict_space[3] + delta, delta)
prediction_matrix = np.empty((len(axis_x), len(axis_y)))
for i, x in enumerate(axis_x):
for j, y in enumerate(axis_y):
test_prediction = np.array([x, y])
test_prediction = net.predict(test_prediction)
prediction_matrix[i, j] = test_prediction
return prediction_matrix, axis_x, axis_y
