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November 25, 2018 15:11
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Dropout Regularization in Deep Neural Networks
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from pandas import read_csv, DataFrame | |
from numpy.random import seed | |
from sklearn.preprocessing import scale | |
from sklearn.model_selection import train_test_split | |
from sklearn.metrics import roc_auc_score | |
from keras.models import Sequential | |
from keras.constraints import maxnorm | |
from keras.optimizers import SGD | |
from keras.layers import Dense, Dropout | |
from multiprocessing import Pool, cpu_count | |
from itertools import product | |
from parmap import starmap | |
df = read_csv("credit_count.txt") | |
Y = df[df.CARDHLDR == 1].DEFAULT | |
X = df[df.CARDHLDR == 1][['AGE', 'ADEPCNT', 'MAJORDRG', 'MINORDRG', 'INCOME', 'OWNRENT', 'SELFEMPL']] | |
sX = scale(X) | |
ncol = sX.shape[1] | |
x_train, x_test, y_train, y_test = train_test_split(sX, Y, train_size = 0.5, random_state = seed(2017)) | |
def tune_dropout(rate1, rate2): | |
net = Sequential() | |
## DROPOUT AT THE INPUT LAYER | |
net.add(Dropout(rate1, input_shape = (ncol,))) | |
## DROPOUT AT THE 1ST HIDDEN LAYER | |
net.add(Dense(ncol, init = 'normal', activation = 'relu', W_constraint = maxnorm(4))) | |
net.add(Dropout(rate2)) | |
## DROPOUT AT THE 2ND HIDDER LAYER | |
net.add(Dense(ncol, init = 'normal', activation = 'relu', W_constraint = maxnorm(4))) | |
net.add(Dropout(rate2)) | |
net.add(Dense(1, init = 'normal', activation = 'sigmoid')) | |
sgd = SGD(lr = 0.1, momentum = 0.9, decay = 0, nesterov = False) | |
net.compile(loss='binary_crossentropy', optimizer = sgd, metrics = ['accuracy']) | |
net.fit(x_train, y_train, batch_size = 200, nb_epoch = 50, verbose = 0) | |
print rate1, rate2, "{:6.4f}".format(roc_auc_score(y_test, net.predict(x_test))) | |
input_dp = [0.1, 0.2, 0.3] | |
hidden_dp = [0.2, 0.3, 0.4, 0.5] | |
parms = [i for i in product(input_dp, hidden_dp)] | |
seed(2017) | |
starmap(tune_dropout, parms, pool = Pool(processes = cpu_count())) |
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