# python-kerasHow can I use Python Keras to create a neural network with zero hidden layers?

Using Python Keras to create a neural network with zero hidden layers is possible by creating a model with a single layer that has the same number of neurons as the input and output layers. The following example code creates a model with three inputs and one output.

```
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(1, input_dim=3))
model.compile(optimizer='sgd', loss='mean_squared_error', metrics=['accuracy'])
```

## Code explanation

`from keras.models import Sequential`

- imports the Sequential model from the Keras library.`from keras.layers import Dense`

- imports the Dense layer from the Keras library.`model = Sequential()`

- creates a new Sequential model.`model.add(Dense(1, input_dim=3))`

- adds a single Dense layer to the model with three inputs and one output.`model.compile(optimizer='sgd', loss='mean_squared_error', metrics=['accuracy'])`

- compiles the model with the stochastic gradient descent optimizer, mean squared error loss function, and accuracy metric.

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