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

1. from keras.models import Sequential - imports the Sequential model from the Keras library.
2. from keras.layers import Dense - imports the Dense layer from the Keras library.
3. model = Sequential() - creates a new Sequential model.
4. model.add(Dense(1, input_dim=3)) - adds a single Dense layer to the model with three inputs and one output.
5. 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.

Helpful links

• Keras Documentation
• Keras Dense Layer Documentation

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