# Preset Parameters "n_inputs" : image_length, # Number of input signals "n_outputs" : 1, # Number of output signals from the network "n_hidden_layers" : 1, # Number of hidden layers in the network (0 or 1 for now) "n_hiddens" : 100, # Number of nodes per hidden layer "activation_functions" : [ LReLU_function, sigmoid_function ], # Activation functions by layer # Optional parameters "weights_low" : -0.1, # Lower bound on initial weight range "weights_high" : 0.1, # Upper bound on initial weight range "save_trained_network" : False, # Save trained weights or not. "batch_size" : 1, # 1 for stochastic gradient descent, 0 for gradient descent } # Initialization network = NeuralNetwork( settings ) # Train network.train( fem_images, fem_scores, # Trainingset ERROR_LIMIT = 1e-3, # Acceptable error bounds learning_rate = 1e-5, # Learning Rate ) # Alter image network.alter_image( fem_images[0], # Image to alter fem_scores[0] # Label for initial backprop )
