## Issue

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`

.

## Solution

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.

Answered By – Tamal Govinda das

Answer Checked By – Pedro (Easybugfix Volunteer)