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- auto net = tflearn.input_data(data)
- net = tflearn.fully_connected(net, 32)
- net = tflearn.fully_connected(net, 32)
- net = tflearn.fully_connected(net, 2, activation='softmax')
- net = tflearn.regression(net)
- auto model = tflearn.DNN(net)
- model.fit(data, n_epoch=10)
- FANN::neural_net net;
- net.create_standard(num_layers, num_input, num_hidden, num_output);
- net.set_learning_rate(learning_rate);
- net.set_activation_steepness_hidden(1.0);
- net.set_activation_steepness_output(1.0);
- net.set_activation_function_hidden(FANN::SIGMOID_SYMMETRIC_STEPWISE);
- net.set_activation_function_output(FANN::SIGMOID_SYMMETRIC_STEPWISE);
- net.init_weights(data);
- net.train_on_data(data, max_iterations, desired_error);
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