def test():
data = np.random.rand(1000, 1)
target = data ** 2
net = am.mlp_network([1, 50, 1], 'tanh', 'identity')
print('\nNet created.\n')
am.fit_adaptive_gradient_descent(net, data, target, 0.1, 10, 5)
print('\nFinished.\n')
return net
def test_poke_inputs(self, shape=(40, 3, 2)):
# Stage 1: Poke data
neural_network = mlp_network(shape, 'tanh', 'identity')
sample_data = np.random.rand(shape[0])
neural_network.poke_inputs(sample_data)
# Stage 2: Verify previous operation
input_layer = neural_network.layers[0]
observed_data = np.asarray([neuron.output for neuron in input_layer])
self.assertTrue((observed_data == sample_data).all)
def test_poke_inputs(self, shape=(40, 3, 2)):
# Stage 1: Poke data
neural_network = mlp_network(shape, 'tanh', 'identity')
sample_data = np.random.rand(shape[0])
neural_network.poke_inputs(sample_data)
# Stage 2: Verify previous operation
input_layer = neural_network.layers[0]
observed_data = np.asarray([neuron.output for neuron in input_layer])
self.assertTrue((observed_data == sample_data).all)
def test_pick_outputs(self, shape=(4, 6, 78)):
# Stage 1: Write data in last layer neuron outputs
neural_network = mlp_network(shape, 'tanh', 'identity')
sample_data = np.random.rand(shape[-1])
last_layer = neural_network.layers[-1]
for neuron, output_value in zip(last_layer, sample_data):
neuron.output = output_value
# Stage 2: Check pick_outputs
observed_data = np.asarray(neural_network.pick_outputs())
self.assertTrue((observed_data == sample_data).all)
def test_make_neuron_fit_strategy_neuron_in_hidden_layer(
self, hidden_layer_size=20):
neural_network = mlp_network([3, hidden_layer_size, 2], 'default',
'default')
factory = AdaptiveGradientDescentMaterialsFactory()
for hidden_neuron in neural_network.layers[1]:
fit_strategy = factory.make_neuron_fit_strategy(hidden_neuron)
self.assertTrue(
isinstance(fit_strategy,
AdaptiveGradientDescentHiddenNeuronFitStrategy))
def test_make_neuron_fit_strategy_neuron_in_output_layer(
self, output_layer_size=20):
neural_network = mlp_network([3, 3, output_layer_size], 'default',
'default')
factory = AdaptiveGradientDescentMaterialsFactory()
for output_neuron in neural_network.layers[-1]:
fit_strategy = factory.make_neuron_fit_strategy(output_neuron)
self.assertTrue(
isinstance(fit_strategy,
AdaptiveGradientDescentOutputNeuronFitStrategy))
def test_pick_outputs(self, shape=(4, 6, 78)):
# Stage 1: Write data in last layer neuron outputs
neural_network = mlp_network(shape, 'tanh', 'identity')
sample_data = np.random.rand(shape[-1])
last_layer = neural_network.layers[-1]
for neuron, output_value in zip(last_layer, sample_data):
neuron.output = output_value
# Stage 2: Check pick_outputs
observed_data = np.asarray(neural_network.pick_outputs())
self.assertTrue((observed_data == sample_data).all)
def test_init(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
self.assertTrue(
isinstance(neural_network.factory,
AdaptiveGradientDescentMaterialsFactory))
self.assertTrue(
isinstance(neural_network.predict_strategy,
MlpNetworkPredictStrategy))
self.assertTrue(
isinstance(neural_network.fit_strategy,
AdaptiveNetworkFitStrategy))
def test_poke_inputs(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
data = None
self.assertRaises(NotImplementedError, Network.poke_inputs,
neural_network, data)
def test_call(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
self.assertRaises(NotImplementedError, Network.__call__,
neural_network)
def test_call(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
test_neuron = neural_network.layers[1][1]
self.assertEqual(test_neuron(), test_neuron.predict_strategy.predict())
def test_shape(self, shape=(40, 52, 1, 7, 4)):
neural_network = mlp_network(shape, 'tanh', 'identity')
self.assertEqual(neural_network.shape, list(shape))
def test_init(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
self.assertTrue(isinstance(neural_network.factory, AdaptiveGradientDescentMaterialsFactory))
self.assertTrue(isinstance(neural_network.predict_strategy, MlpNetworkPredictStrategy))
self.assertTrue(isinstance(neural_network.fit_strategy, AdaptiveNetworkFitStrategy))
def test_call(self, input_size=100, shape=(4, 3, 2)):
neural_network = mlp_network(shape, 'tanh', 'identity')
input_data = np.random.rand(input_size, shape[0])
self.assertTrue((neural_network(input_data) ==
neural_network.predict_strategy(input_data)).all)
def test_call(self, input_size=100, shape=(4, 3, 2)):
neural_network = mlp_network(shape, 'tanh', 'identity')
input_data = np.random.rand(input_size, shape[0])
self.assertTrue((neural_network(input_data) == neural_network.predict_strategy(input_data)).all)
def test_make_neuron_fit_strategy_neuron_in_output_layer(self, output_layer_size=20):
neural_network = mlp_network([3, 3, output_layer_size], 'default', 'default')
factory = AdaptiveGradientDescentMaterialsFactory()
for output_neuron in neural_network.layers[-1]:
fit_strategy = factory.make_neuron_fit_strategy(output_neuron)
self.assertTrue(isinstance(fit_strategy, AdaptiveGradientDescentOutputNeuronFitStrategy))
def test_shape(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
self.assertRaises(NotImplementedError, Network.shape.__get__, neural_network)
def test_pick_outputs(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
self.assertRaises(NotImplementedError, Network.pick_outputs, neural_network)
def test_poke_inputs(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
data = None
self.assertRaises(NotImplementedError, Network.poke_inputs, neural_network, data)
def test_call(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
self.assertRaises(NotImplementedError, Network.__call__, neural_network)
def test_shape(self, shape=(40, 52, 1, 7, 4)):
neural_network = mlp_network(shape, 'tanh', 'identity')
self.assertEqual(neural_network.shape, list(shape))
def test_pick_outputs(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
self.assertRaises(NotImplementedError, Network.pick_outputs,
neural_network)
def test_call(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
test_neuron = neural_network.layers[1][1]
self.assertEqual(test_neuron(), test_neuron.predict_strategy.predict())
def test_make_neuron_fit_strategy_neuron_in_hidden_layer(self, hidden_layer_size=20):
neural_network = mlp_network([3, hidden_layer_size, 2], 'default', 'default')
factory = AdaptiveGradientDescentMaterialsFactory()
for hidden_neuron in neural_network.layers[1]:
fit_strategy = factory.make_neuron_fit_strategy(hidden_neuron)
self.assertTrue(isinstance(fit_strategy, AdaptiveGradientDescentHiddenNeuronFitStrategy))
def test_shape(self):
neural_network = mlp_network([4, 3, 2], 'tanh', 'identity')
self.assertRaises(NotImplementedError, Network.shape.__get__,
neural_network)