Hello,

I have tried to reproduce the following code for a multiclass classification problem (3 classes) from here:

```
import tensorflow as tf
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
# Load the Iris dataset
iris = load_iris()
# Split the dataset into training and test sets
X_train, X_test, y_train, y_test = train_test_split(
iris.data, iris.target, test_size=0.2, random_state=42)
# Define the model
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation='relu', input_shape=(4,)),
tf.keras.layers.Dense(3, activation='softmax')
])
# Compile the model
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Train the model
history = model.fit(X_train, y_train, epochs=50, validation_split=0.2)
```

I noticed that when I change the line in model.compile with

```
metrics=['accuracy']
```

to

```
metrics=[ tf.keras.metrics.Precision()])
```

to get a precision metric instead of accuracy, the code gives an error about shapes:

```
ValueError: Shapes (32, 3) and (32, 1) are incompatible
```

Error also happens if I try to add precision as a metric after accuracy like so:

```
metrics=['accuracy', tf.keras.metrics.Precision()]
```

I have also tried tensorflow addons and again get an error:

```
import tensorflow_addons as tfa
metrics= [tfa.metrics.F1Score(average="macro",num_classes = 3,threshold=None,name='f1_score', dtype=None)]
ValueError: Dimension 0 in both shapes must be equal, but are 3 and 1. Shapes are [3] and [1].
```

How can I optimize on precision or f1-score from Tensorflow metrics (

Module: tf.keras.metrics | TensorFlow v2.16.1)?