CODEWITHSUNDEEP
python-3.xtensorflowneural-networkkeras
Tanmay Bhatnagar
Tanmay Bhatnagar

Reputation: 2470

Decimal accuracy of output layer in keras

I am working on a project that predicts drug synergy values based on various input features related to them. The synergy values are floating point numbers and so I would like to set an accuracy range for my neural network. eg - Say the actual value is 1.342423 and my model predicts 1.30123 then the output this should be treated as correct ouput. In other words I would like to limit the amount of decimal places that are checked to compare the actual answer and the predicted answer. Neural Net :

model = Sequential()
act = 'relu'
model.add(Dense(430, input_shape=(3,)))
model.add(Activation(act))

model.add(Dense(256))
model.add(Activation(act))
model.add(Dropout(0.42))

model.add(Dense(148))
model.add(Activation(act))
model.add(Dropout(0.3))

model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])

Complete source code for learning and train/test data : https://github.com/tanmay-edgelord/Drug-Synergy-Models/blob/master Please ask for any additional details that are required (Using Keras with TensorFlow backend)

Upvotes: 1

Views: 3338

Answers (1)

Daniel Möller
Daniel Möller

Reputation: 86610

Create a custom metric:

import keras.backend as K

def myAccuracy(y_true, y_pred):
    diff = K.abs(y_true-y_pred) #absolute difference between correct and predicted values
    correct = K.less(diff,0.05) #tensor with 0 for false values and 1 for true values
    return K.mean(correct) #sum all 1's and divide by the total.

Then use it in the model compilation:

model.compile(metrics=[myAccuracy],....)

Upvotes: 4

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