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.
Helpful links
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