num_iterations=0,
large_input_bool=False):
# Training a Neural Network to classify the data
layers = Layers(data.D, data.K)
if len(num_neurons) == 0:
num_neurons = 10 # default to a single hidden layer with 10 neurons
for hidden_set in num_neurons:
layers.add_layer(num_neurons)
if num_iterations > 0:
neural_network = NeuralNetwork(data, layers, num_iterations)
else:
neural_network = NeuralNetwork(data, layers)
neural_network.train(large_input_bool)
accuracy = neural_network.evaluate()
print accuracy
if __name__ == '__main__':
toy_2d_data = Data()
toy_2d_data.construct_toy_data()
toy_2d_data.preprocess() # No actual need for preprocessing here.
'''
# Visualizing the data
X, y = toy_2d_data.X, toy_2d_data.y
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.show()
'''
# classifyWithSoftmax(toy_2d_data)
classifyWithNeuralNetwork(toy_2d_data, num_neurons=100)
