Here's a sample Keras model with 5 inputs and 5 outputs:

```
from keras.models import Model
from keras.layers import Input, Dense

# Define input layer
inputs = Input(shape=(5,))

# Define hidden layer
hidden = Dense(10, activation='relu')(inputs)

# Define output layer
outputs = Dense(5, activation='softmax')(hidden)

# Define the model
model = Model(inputs=inputs, outputs=outputs)

# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
```

In this example, the input layer has 5 nodes, and the output layer has 5 nodes (with a softmax activation function). The hidden layer has 10 nodes and uses the relu activation function.

Note that the model is compiled with a categorical crossentropy loss function and the Adam optimizer. You may need to adjust the choice of loss function and optimizer depending on your specific use case.
Crea un modello Keras che accetta 5 input e ha cinque output