CODEWITHSUNDEEP
pythonmachine-learningkeras
Tad. A
Tad. A

Reputation: 61

python - returning nan when trying to predict with Keras

For a school project, I'm trying to predict data using the keras framework, but it's returning 'nan' loss and values when I try to get predicted data.

Source code :

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=5)

# create model
model = Sequential()
model.add(Dense(950, input_shape=(425,), activation='relu'))
model.add(Dense(425, activation='relu'))
model.add(Dense(200, activation='relu'))
model.add(Dense(50, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

# Compile model
sgd = optimizers.SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer='sgd')

# Fit the model
model.fit(X_train, y_train, epochs=20, batch_size=1, verbose=1)

#evaluate the model
y_pred = model.predict(X_test)

score = model.evaluate(X_test, y_test,verbose=1)
print(score)

# calculate predictions
predictions = model.predict(X_pred)

Data :

X_train and X_test are (panda)dataframes of 5000 rows(nber of samples) * 425 columns (number of dimensions).

y_train and y_test look like : 

array([ 1.17899644,  1.46080518,  0.9662137 , ...,  2.40157461,
        0.53870386,  1.3192718 ])

Can you help me with that ?

Thank you for you help!

Upvotes: 5

Views: 20306

Answers (4)

Mohbat Tharani
Mohbat Tharani

Reputation: 560

  • Make sure your target output is in range (0, 1) as you have sigmoid in the last layer.

  • sigmoid has an output between zero and one so if the target output is not in this range then (a) change the activation function or (b) normalize outputs in the required range.

  • Make sure the purpose of this model is the regression.

  • After considering the above three points, play around with learning rate (decrease) and the optimiser (replace with any other).

Upvotes: 1

minh
minh

Reputation: 181

You initialized your SGD optimizer in variable sgd but you're not using it in compile

Upvotes: 0

S.P
S.P

Reputation: 1

Try changing your optimizer to 'Adam' instead of SGD

Upvotes: 0

Jodo
Jodo

Reputation: 4773

Usually, this means that something converges to infinity. As @desertnaut pointed out in the comment, reducing the learning rate might help.

But the root of the issue is your input data. What do these 425 data points mean? Are they from different sources, different features, different parameters? Finding outliners or normalizing the data, could help.

Your code looks fine otherwise.

Upvotes: 3

Related Questions