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class_nn_base.py
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class_nn_base.py
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# -*- coding: utf-8 -*-
import numpy as np
from keras.initializers import glorot_uniform, he_normal
###########################################################################################
# NN class #
###########################################################################################
class NN_base:
###############
# Constructor #
###############
def __init__(
self,
learning_rate,
output_activation,
loss_function,
weight_init,
num_epochs,
class_weights,
minibatch_size,
L2_lambda,
flag_batchnorm,
seed
):
# seed
self.seed = seed
# NN parameters
self.learning_rate = learning_rate
self.output_activation = output_activation
self.num_epochs = num_epochs
self.class_weights = class_weights
self.minibatch_size = minibatch_size
self.L2_lambda = L2_lambda
self.flag_batchnorm = flag_batchnorm
if weight_init == "glorot":
self.weight_init = glorot_uniform(seed=self.seed)
elif weight_init == "he":
self.weight_init = he_normal(seed=self.seed)
# loss function
self.loss_function = loss_function
# model to be defined in sub-classes
self.model = None
#############
# Auxiliary #
#############
def cast_classes(self, y_datasets):
return [y_data.astype('int') for y_data in y_datasets]
###########################################################################################
# API #
###########################################################################################
##############
# Prediction #
##############
def prediction(self, x):
y_hat = self.model.predict(x=x, verbose=0)
y_hat_class = np.around(y_hat)
return y_hat, y_hat_class
############
# Training #
############
def training(self, x, y):
self.model.fit(
x=x,
y=self.cast_classes([y]), # cast class to integer
epochs=self.num_epochs,
batch_size=self.minibatch_size,
class_weight=self.class_weights,
verbose=0 # 0: off, 1: full, 2: brief
)
########################
# Change class weights #
########################
def change_class_weights(self, weights):
if self.class_weights != weights:
self.class_weights = weights
##################
# Get num_epochs #
##################
def get_num_epochs(self):
return self.num_epochs
#####################
# Change num_epochs #
#####################
def change_num_epochs(self, n_epochs):
if self.num_epochs != n_epochs:
self.num_epochs = n_epochs
#########################
# Change minibatch size #
#########################
def change_minibatch_size(self, batch_size):
if self.minibatch_size != batch_size:
self.minibatch_size = batch_size