Question: I'm using

```
 x = System.Security.Principal.WindowsIdentity.GetCurrent().User.Value;

```

which outputs the users **windows identity**.
Here is an **example** of what the output looks like:

> 
> S-5-2-51-37518325-1542148351-9136563645-5621
> 
> 
> 

I'm currently sending this to a .txt file and reading it but as you can probably tell anyone with a bit of experience can access the text file so how would I scramble these values to the point they are no longer able get traced back to their original numbers.

Answer: 
This seems to run perfectly fine on TF 1.15:

```
import cv2
import numpy as np
import tensorflow as tf
from tensorflow.python import keras
print(tf.__version__)
model = keras.Sequential([
    keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation="relu", padding='same', input_shape=(150,150,1)),
    keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'),
    keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'),
    keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'),
    keras.layers.Flatten(),
    keras.layers.Dense(8, activation="softmax")
])
# Create random image
img = np.zeros([150,150,3], dtype=np.uint8)
img[:,:,0] = np.ones([150,150])*64
img[:,:,1] = np.ones([150,150])*128
img[:,:,2] = np.ones([150,150])*192

g_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
g_img = np.expand_dims(g_img, axis=0)
g_img = np.expand_dims(g_img, axis=-1) # (1, 150, 150, 1)
prediction = model.predict(g_img)
print(prediction.shape)

```

```
1.15.2
(1, 8)

```
in code you wrote, the input shape is (224,256,1) so change it to (150,150,1)
try this:

```
from tensorflow.python import keras
model = keras.Sequential([
    keras.layers.Conv2D(filters=32, kernel_size=(3,3), activation="relu", padding='same', input_shape=(150,150,1)),
    keras.layers.MaxPool2D(pool_size=(2,2), padding='same', data_format='channels_last'),

```

do the math to get the desired output.