I’m a noob still but am running into a puzzling error. I am playing with the MNIST tutorial. Here is the network:

```
model = tf.keras.models.Sequential( [
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10)
] )
model.compile(
optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
)
```

After training, I want to pass a single data point through the network so I do this:

```
mnist = tfds.load('mnist', split='train', as_supervised=True)
data_iter = mnist.repeat().as_numpy_iterator()
image_in = next( data_iter )
model( np.squeeze( image_in ) )
```

However, it appears the sqeeze operation in the first layer isn’t doing it’s job according to this error:

```
ValueError: Exception encountered when calling layer 'sequential_2' (type Sequential).
Input 0 of layer "dense_4" is incompatible with the layer: expected axis -1 of input shape to have value 784, but received input with shape (28, 28)
```

Can someone help me understand what’s going on here and what the appropriate way is to pass a data point through the model is?

Your help is greatly appreciated.