def test_neuralNetwork_addLayer():
inputs = 6
outputs = 4
layers = 3
neurons = 87
activations = 'sigmoid'
nn = NeuralNetwork(inputs=inputs,
outputs=outputs,
layers=layers,
neurons=neurons,
activations=activations)
nn.addLayer()
assert nn.weights[-1].shape == (inputs, neurons)
assert nn.biases[-1].shape == (neurons, 1)
assert type(nn.act[-1]) is Activation
assert nn.act[-1].function == nn.act[-1]._sigmoid
new_neurons = 10
new_activations = 'relu'
nn.addLayer(neurons=new_neurons, activations=new_activations)
assert nn.weights[-1].shape == (neurons, new_neurons)
assert nn.biases[-1].shape == (new_neurons, 1)
assert type(nn.act[-1]) is Activation
assert nn.act[-1].function == nn.act[-1]._relu
nn_copy1 = copy.deepcopy(nn)
nn_copy2 = copy.deepcopy(nn)
nn_copy3 = copy.deepcopy(nn)
nn_copy4 = copy.deepcopy(nn)
nn_copy1.addOutputLayer()
assert nn_copy1.weights[-1].shape == (new_neurons, outputs)
assert nn_copy1.biases[-1].shape == (outputs, 1)
nn_copy2.addLayer(output=True)
assert nn_copy2.weights[-1].shape == (new_neurons, outputs)
assert nn_copy2.biases[-1].shape == (outputs, 1)
new_outputs = 24
with warnings.catch_warnings():
warnings.simplefilter("ignore")
nn_copy3.addOutputLayer(outputs=new_outputs)
assert nn_copy3.weights[-1].shape == (new_neurons, new_outputs)
assert nn_copy3.biases[-1].shape == (new_outputs, 1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
nn_copy4.addLayer(outputs=new_outputs, output=True)
assert nn_copy4.weights[-1].shape == (new_neurons, new_outputs)
assert nn_copy4.biases[-1].shape == (new_outputs, 1)
# Now that we constructed an entire network, check that the matrices line
# up so that the network can be evaluated
nn = copy.deepcopy(nn_copy1)
x = np.random.uniform(-1.0, 1.0, size=(inputs, 1))
assert nn(x).shape == (outputs, 1)
assert nn.network(x).shape == (outputs, 1)
nn = copy.deepcopy(nn_copy3)
assert nn(x).shape == (new_outputs, 1)
assert nn.network(x).shape == (new_outputs, 1)
