def test_create_neural_network_object_with_dimensions_param(self):
test_dimensions = (2, 10, 13, 15)
neural_network = dendrites.NeuralNetwork(dimensions=test_dimensions)
self.assertEqual(test_dimensions, neural_network.dimensions)
def setUp(self):
self.test_inputs = [1, 0, 1]
self.test_outputs = [1, 0]
self.neural_network = dendrites.NeuralNetwork(dimensions=(3, 4, 2))
self.neural_network.add(input=self.test_inputs,
output=self.test_outputs)
self.neural_network.train()
def test_create_neural_network_object_with_inputs_outputs_params(self):
test_inputs = 10
test_outputs = 10
neural_network = dendrites.NeuralNetwork(inputs=test_inputs,
outputs=test_outputs)
self.assertEqual(test_inputs, neural_network.dimensions[0])
self.assertEqual(test_outputs, neural_network.dimensions[-1])
import dendrites import logging # Create the logger logger = logging.getLogger() logger.setLevel(logging.DEBUG) # Create the neural network with 2 layers, input and output layer. # The Input layer has 2 inputs, and the output layer has 3 outputs. MyNeuralNet = dendrites.NeuralNetwork(dimensions=(2, 3)) # Add a supervised dataset -- tell the network how to behave MyNeuralNet.add(input=[0, 1], output=[0, 1, 0]) # Train the network MyNeuralNet.train() # Run the network on the dataset we provided earlier, and see the results. logger.info(MyNeuralNet.run(input=[0, 1])) # Save the MyNeuralNet in a net.dat file, so we can use it later (without Training it again) MyNeuralNet.save(location="net.dat") # Delete MyNeuralNet from memory del (MyNeuralNet) # Now lets load the network from the net.dat file we created earlier. MyLoadedNeuralNet = dendrites.NeuralNetwork() MyLoadedNeuralNet.load(location="net.dat") # Lets run the network one more time on the dataset we provided earlier,
def test_create_neural_network_object(self):
neural_network = dendrites.NeuralNetwork()
self.assertIsNotNone(neural_network)
def setUp(self):
self.neural_network = dendrites.NeuralNetwork(dimensions=(3, 4, 2))
self.neural_network.add(input=[1, 1, 1], output=[1, 1])
def setUp(self):
self.neural_network = dendrites.NeuralNetwork(dimensions=(3, 4, 2))