tensorflow evalutaion and earlystopping gives infinity overflow error

user_14349010 0
Viewed 2359

I a model as seen in the code below, but when trying to evaluate it or using earlystopping on it it gives me the following error:

    numdigits = int(np.log10(self.target)) + 1
OverflowError: cannot convert float infinity to integer

I must state that without using .EarlyStopping or model.evaluate everything works well.

I know that np.log10(0) gives -inf so that could be a potential cause, but why is there a 0 there in the first place and how can it be prevented? How can this problem be fixed?

NOTES

this is the code I use:

import tensorflow as tf
from tensorflow import keras

TRAIN_PERCENT = 0.9

model = keras.Sequential([
    keras.layers.Dense(128, input_shape=(100,), activation='relu'),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(100)
])

earlystop_callback = keras.callbacks.EarlyStopping(min_delta=0.0001, patience=1
                                                   , monitor='accuracy'
                                                   )

optimizer = keras.optimizers.Adam(lr=0.01)
model.compile(optimizer=optimizer, loss="mse", metrics=['accuracy'])

X_set, Y_set = some_get_data_function()
sep = int(len(X_set)/TRAIN_PERCENT)
X_train, Y_train = X_set[:sep], Y_set[:sep]
X_test, Y_test = X_set[sep:], Y_set[sep:]

model.fit(X_train, Y_train, batch_size=16, epochs=5, callbacks=[earlystop_callback])
ev = model.evaluate(X_test, Y_test)
print(ev)

X,Y sets are np arrays. X is an array of arrays of 100 integers between 0 and 10. Y is an array of arrays of 100 integers, all of them are either 0 or 1.

keras tensorflow overflow numpy python
2 Answers

Well it's hard to tell exactly as I can't run code without some_get_data_function() realization but recently I've got same error when mistakenly passed EMPTY array to model.evaluate. Taking into account that @meTchaikovsky comment solved your issue it's certainly due to messed up input arrays.

user_38583340

in my case, the data was small - so doing something like

cached_train = train.shuffle(100_000).batch(8192).cache()
cached_test = test.batch(4096).cache()

would have resulted in an empty cached_test ! this does not affect model.fit

so always check the length of the data using cached_test.__len__() to make sure you are not passing an empty data to the function.

user_7028460
Related