Ejemplo n.º 1
0
        network,
        training_one, 
        method = "Newton-CG",
        ERROR_LIMIT = 1e-4
    )

# Train the network using Scaled Conjugate Gradient
scaled_conjugate_gradient(
        network,
        training_one, 
        ERROR_LIMIT = 1e-4
    )

# Train the network using resilient backpropagation
resilient_backpropagation(
        network,
        training_one,          # specify the training set
        ERROR_LIMIT     = 1e-3, # define an acceptable error limit
        #max_iterations = (),   # continues until the error limit is reach if this argument is skipped
        
        # optional parameters
        weight_step_max = 50., 
        weight_step_min = 0., 
        start_step = 0.5, 
        learn_max = 1.2, 
        learn_min = 0.5
    )


network.print_test( training_one )
Ejemplo n.º 2
0
    training_one,  # specify the training set
    ERROR_LIMIT=0.001,  # define an acceptable error limit 
    #max_iterations  = 100, # continues until the error limit is reach if this argument is skipped

    # optional parameters
    learning_rate=0.03,  # learning rate
    momentum_factor=0.4,  # momentum
)

# Train the network using SciPy
scipyoptimize(network, training_one, method="Newton-CG", ERROR_LIMIT=1e-4)

# Train the network using Scaled Conjugate Gradient
scaled_conjugate_gradient(network, training_one, ERROR_LIMIT=1e-4)

# Train the network using resilient backpropagation
resilient_backpropagation(
    network,
    training_one,  # specify the training set
    ERROR_LIMIT=1e-3,  # define an acceptable error limit
    #max_iterations = (),   # continues until the error limit is reach if this argument is skipped

    # optional parameters
    weight_step_max=50.,
    weight_step_min=0.,
    start_step=0.5,
    learn_max=1.2,
    learn_min=0.5)

network.print_test(lst)
Ejemplo n.º 3
0
        network,
        training_one, 
        method = "Newton-CG",
        ERROR_LIMIT = 1e-4
    )

# Train the network using Scaled Conjugate Gradient
scaled_conjugate_gradient(
        network,
        training_one, 
        ERROR_LIMIT = 1e-4
    )

# Train the network using resilient backpropagation
resilient_backpropagation(
        network,
        training_one,          # specify the training set
        ERROR_LIMIT     = 1e-3, # define an acceptable error limit
        #max_iterations = (),   # continues until the error limit is reach if this argument is skipped
        
        # optional parameters
        weight_step_max = 50., 
        weight_step_min = 0., 
        start_step = 0.5, 
        learn_max = 1.2, 
        learn_min = 0.5
    )


network.print_test( lst)
Ejemplo n.º 4
0
)

# Train the network using SciPy
#network.scipyoptimize(
#                training_one,
#                method = "Newton-CG",
#                ERROR_LIMIT = 1e-4
#            )

## Train the network using Scaled Conjugate Gradient
#network.scg(
#                training_one,
#                ERROR_LIMIT = 1e-4
#            )

# Train the network using resilient backpropagation
#network.resilient_backpropagation(
#                training_one,          # specify the training set
#                ERROR_LIMIT     = 1e-3, # define an acceptable error limit
#                #max_iterations = (),   # continues until the error limit is reach if this argument is skipped
#
#                # optional parameters
#                weight_step_max = 50.,
#                weight_step_min = 0.,
#                start_step = 0.5,
#                learn_max = 1.2,
#                learn_min = 0.5
#            )

network.print_test(training_one)
Ejemplo n.º 5
0
def main(file_path):
    input_length = 0

    training_one = []
    with open(file_path, mode="r") as fh:
        temp_data = []
        readed_data = csv.reader(fh, delimiter=',')
        for row in readed_data:
            input_length = len(row) - 2
            name = row[0]
            for j in range(1, len(row) - 1):
                temp_data.append(float(row[j]))

            temp_data = normalize(temp_data)
            target = bitfield(int(row[len(row) - 1]))
            temp_instance = Instance(temp_data, target, name)
            temp_data = []
            training_one.append(temp_instance)

    settings = {
        # Required settings
        "cost_function":
        sum_squared_error,
        "n_inputs":
        input_length,  # Number of network input signals
        "layers": [(26, sigmoid_function), (15, sigmoid_function),
                   (10, sigmoid_function), (3, sigmoid_function)],
        # [ (number_of_neurons, activation_function) ]
        # The last pair in you list describes the number of output signals

        # Optional settings
        "weights_low":
        -1,  # Lower bound on initial weight range
        "weights_high":
        0,  # Upper bound on initial weight range
        "save_trained_network":
        False,  # Whether to write the trained weights to disk
        "input_layer_dropout":
        0.0,  # dropout fraction of the input layer
        "hidden_layer_dropout":
        0.0,  # dropout fraction in all hidden layers
    }

    # initialize the neural network
    network = NeuralNet(settings)

    # load a stored network configuration
    # network = NeuralNet.load_from_file( "trained_configuration.pkl" )

    # Train the network using backpropagation
    backpropagation(
        network,
        training_one,  # specify the training set
        ERROR_LIMIT=1e-3,  # define an acceptable error limit
        #max_iterations  = 100, # continues until the error limit is reach if this argument is skipped

        # optional parameters
        learning_rate=0.01,  # learning rate
        momentum_factor=0.9,  # momentum
        max_iterations=100000)
    """
    # Train the network using SciPy
    scipyoptimize(
            network,
            training_one,
            method = "Newton-CG",
            ERROR_LIMIT = 1e-4
        )

    # Train the network using Scaled Conjugate Gradient
    scaled_conjugate_gradient(
            network,
            training_one,
            ERROR_LIMIT = 1e-4
        )

    # Train the network using resilient backpropagation
    resilient_backpropagation(
            network,
            training_one,          # specify the training set
            ERROR_LIMIT     = 1e-3, # define an acceptable error limit
            #max_iterations = (),   # continues until the error limit is reach if this argument is skipped

            # optional parameters
            weight_step_max = 50.,
            weight_step_min = 0.,
            start_step = 0.5,
            learn_max = 1.2,
            learn_min = 0.5
        )

    """
    # есть метод save_to_file в neuralnet. надо попробовать его использовать
    return network.print_test(training_one)