## Issue

After training the model , evaluating prediction that is on dtype=float32.

```
y_pred = model.predict(x_test)
y_pred
array([[0.952564 ],
[0.40119413],
[0.8223132 ],
...,
[0.03289893],
[0.16677496],
[0.882395 ]], dtype=float32)
result = model.evaluate(np.asarray(x_test), np.asarray(y_test))
loss = result[0]
accuracy = result[1]
print(f"[+] Accuracy: {accuracy*100:.2f}%")
```

so , for this i have float32 but 1 for positive & 0 for neg, for this i have some issue , so i am trying to do, make it float32 to int32 & a loop that if value is greater than 0.5 then it will count as 1 & if less than 0.5 then count as 0 means neg.

the loop i tried :

```
y_pred = model.predict(x_test)
y_pred ( for i in range(len(y_pred)):
if y_pred[i][0] >= 0.5:
y_pred[i][0] = int(1)
else:
y_pred[i][0] = 0
print(y_pred[0]) )
```

error is : invalid syntax .

can anyone help to sort out this one ?

## Solution

Seems like you should use y_pred[0][i]. The array looks to be 2d with only one sub array, so [0][i] should be used. Also, if the code is exactly what is written, i think

```
y_pred ( for i in range(len(y_pred)):
```

should be

```
for i in range(len(y_pred)):
```

Answered By – Mercury

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