Keras - Autoencoder accuracy stuck on zero

Question

I'm trying to detect fraud using autoencoder and Keras. I've written the following code as a Notebook:

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn.preprocessing import StandardScaler
from keras.layers import Input, Dense
from keras.models import Model
import matplotlib.pyplot as plt

data = pd.read_csv('../input/creditcard.csv')
data['normAmount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
data = data.drop(['Time','Amount'],axis=1)

data = data[data.Class != 1]
X = data.loc[:, data.columns != 'Class']

encodingDim = 7
inputShape = X.shape[1]
inputData = Input(shape=(inputShape,))

X = X.as_matrix()

encoded = Dense(encodingDim, activation='relu')(inputData)
decoded = Dense(inputShape, activation='sigmoid')(encoded)
autoencoder = Model(inputData, decoded)
encoder = Model(inputData, encoded)
encodedInput = Input(shape=(encodingDim,))
decoderLayer = autoencoder.layers[-1]
decoder = Model(encodedInput, decoderLayer(encodedInput))

autoencoder.summary()

autoencoder.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
history = autoencoder.fit(X, X,
                epochs=10,
                batch_size=256,
                validation_split=0.33)

print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()

I'm probably missing something, my accuracy is stuck on 0 and my test loss is lower than my train loss.

Any Insight would be appericiated

Keras - Autoencoder accuracy stuck on zero

1 Answers