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Just creating a function splitTestTrainFromLast in util.py and moving the shuffle and splitting of datasets there. #1

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Just creating a function splitTestTrainFromLast in util.py and moving the shuffle and splitting of datasets there. #1

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9 changes: 3 additions & 6 deletions ann.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,7 @@
import numpy as np
import matplotlib.pyplot as plt

from util import getData, softmax, cost2, y2indicator, error_rate, relu
from sklearn.utils import shuffle
from util import getData, softmax, cost2, y2indicator, error_rate, relu,splitTrainTestFromLast


class ANN(object):
Expand All @@ -11,10 +10,8 @@ def __init__(self, M):

# learning rate 10e-6 is too large
def fit(self, X, Y, learning_rate=10e-7, reg=10e-7, epochs=10000, show_fig=False):
X, Y = shuffle(X, Y)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
# Tvalid = y2indicator(Yvalid)
X, Y = X[:-1000], Y[:-1000]
Xvalid,Yvalid,X,Y = splitTrainTestFromLast(X,Y,1000)

N, D = X.shape
K = len(set(Y))
Expand Down Expand Up @@ -70,7 +67,7 @@ def score(self, X, Y):

def main():
X, Y = getData()

model = ANN(200)
model.fit(X, Y, reg=0, show_fig=True)
print model.score(X, Y)
Expand Down
6 changes: 5 additions & 1 deletion util.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import numpy as np
import pandas as pd

from sklearn.utils import shuffle

def init_weight_and_bias(M1, M2):
W = np.random.randn(M1, M2) / np.sqrt(M1 + M2)
Expand Down Expand Up @@ -121,3 +121,7 @@ def crossValidation(model, X, Y, K=5):
errors.append(err)
print "errors:", errors
return np.mean(errors)

def splitTrainTestFromLast(X,Y,N):
X,Y = shuffle(X,Y)
return (X[-N:],Y[-N:],X[:-N],Y[:-N])