CODEWITHSUNDEEP
pythonmachine-learningkeras
DrRed
DrRed

Reputation: 35

Keras producing model with no accuracy

I have the following code for training a model based off of some numbers:

from numpy import loadtxt
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from time import sleep
dataset = loadtxt("data.csv", delimiter=",")
X = dataset[:,0:2]
y = dataset[:,2]
model = Sequential()
model.add(Dense(196, input_dim=2, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y, epochs=600, batch_size=10)
_, accuracy = model.evaluate(X, y)
print('Accuracy: %.2f' % (accuracy*100))

For reference, here is some of the data it is being presented with:

433,866,1299,1732
421,842,1263,1684
443,886,1329,1772
142,284,426,568
437,874,1311,1748
455,910,1365,1820
172,344,516,688
219,438,657,876
101,202,303,404
289,578,867,1156
110,220,330,440
421,842,1263,1684
472,944,1416,1888
121,242,363,484
215,430,645,860
134,268,402,536
488,976,1464,1952
467,934,1401,1868
418,836,1254,1672
134,268,402,536
241,482,723,964
116,232,348,464
395,790,1185,1580
438,876,1314,1752
396,792,1188,1584
57,114,171,228
218,436,654,872
372,744,1116,1488
305,610,915,1220
462,924,1386,1848
455,910,1365,1820
42,84,126,168
347,694,1041,1388
394,788,1182,1576
184,368,552,736
302,604,906,1208
326,652,978,1304
333,666,999,1332
335,670,1005,1340
176,352,528,704
168,336,504,672
62,124,186,248
26,52,78,104
335,670,1005,1340

(The first three numbers should be inputs, and the last one an output) The Keras program keeps training but only warrants an accuracy of 0. What am I doing wrong?

Upvotes: 2

Views: 88

Answers (1)

perl
perl

Reputation: 9941

Like discussed in comments, this is a regression problem (not classification), so we can use, for example, mse (mean squared errors) as a loss function, and change activation of the last layer to linear:

X = dataset[:,0:3]
y = dataset[:,3]
model = Sequential()
model.add(Dense(196, input_dim=3, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mse', optimizer='adam')
model.fit(X, y, epochs=600, batch_size=10)

Upvotes: 2

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