CODEWITHSUNDEEP
python-3.xdeep-learninglstmrecurrent-neural-network
Hadeer El-Zayat
Hadeer El-Zayat

Reputation: 291

how to build LSTM RNN network for binary classification?

I am trying to build a deep learning network for binary classification using LSTM based RNN.

Here is what I have tried using python 

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
import numpy as np

train = np.loadtxt("TrainDatasetFinal.txt", delimiter=",")
test = np.loadtxt("testDatasetFinal.txt", delimiter=",")

y_train = train[:,7]
y_test = test[:,7]

train_spec = train[:,6]
test_spec = test[:,6]


model = Sequential()
model.add(Embedding(8, 256, input_length=1))
model.add(LSTM(output_dim=128, activation='sigmoid',
               inner_activation='hard_sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='rmsprop')

model.fit(train_spec, y_train, batch_size=2000, nb_epoch=11)
score = model.evaluate(test_spec, y_test, batch_size=2000)

Here is a sample from the dataset

(Patient Number, time in millisecond, accelerometer x-axis,y-axis, z-axis,magnitude, spectrogram,label (0 or 1))

1,15,70,39,-970,947321,596768455815000,0
1,31,70,39,-970,947321,612882670787000,0
1,46,60,49,-960,927601,602179976392000,0
1,62,60,49,-960,927601,808020878060000,0
1,78,50,39,-960,925621,726154800929000,0

I believe that the my problem in those lines but I cannot recognize the error 

model.add(Embedding(8, 256, input_length=1))
model.add(LSTM(output_dim=128, activation='sigmoid',
               inner_activation='hard_sigmoid'))

and this is the error I have got

InvalidArgumentError (see above for traceback): indices[0,0] = -2147483648 is not in [0, 8)

Upvotes: 3

Views: 1138

Answers (1)

braaterAfrikaaner
braaterAfrikaaner

Reputation: 1082

Is the sample from your dataset provided above, the data you are trying to feed into the model? If so, there is a problem because your data is 2-dimensional, but for an RNN you need a 3-dimensional input tensor. You need a feature dimension, a batch size dimension and a time dimension. It looks like you are missing a proper time dimension. You should not have a column with 15, 31, 46,... (time in milliseconds) this should be shaped into its own dimension, so your input data looks like a "cube". Otherwise, you don't need a temporal model at all. Furthermore, you should standardize your input since your features have vastly different orders of magnitude. Moreover, the batch size of 2000 is almost certainly too large. Are you trying to express that your whole training set has 2000 samples? In this case, you may not have enough training data for the model you are building.

Upvotes: 1

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