Question : acc: 81.97% but predict result always 0

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Hi, I can’t figure out what’s wrong, I just modified script from pima indian database.csv with my data and predict result always 0 (but accuracy and training is 81.97%)

data is attached (rename it to data.csv first)

data.txt

Thanks and I’m sorry if I made stupid mistake (very newbie about this ML) 😃

from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)

dataset = numpy.loadtxt("data.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:7]
Y = dataset[:,7]


# create model
model = Sequential()
model.add(Dense(12, input_dim=7, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Fit the model
print('Training')
model.fit(X, Y, epochs=20, batch_size=16,verbose=0)

# evaluate the model
scores = model.evaluate(X, Y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

#predictions = model.predict(XTEST)
predictions = model.predict(X)
# round predictions
rounded = [round(x[0]) for x in predictions]
print(rounded)

[tRosenflanz]

.fit method has class_weight argument which can help you rebalance the losses of data without changing the data class_weight={0:1,1:5} should work

[x4080]

Thanks @tRosenflanz and @JamesTrick

I will close this because I finally found out the problem 😃