def __init__(self, num_layers_array, num_rows, num_columns, data_set, name_of_data_set, learning_rate): self.num_layers_array = num_layers_array self.str_results = [] len_num_layers_array = len(num_layers_array) self.layers = [] #Go through each layer end_of_num_layers_array = False for num_nodes in range(len_num_layers_array): if (num_nodes == len_num_layers_array - 1): end_of_num_layers_array = True else: end_of_num_layers_array = False if num_nodes == 0: temp_layer = ln.layer_of_neurons(num_rows, num_columns, data_set, name_of_data_set, self.num_layers_array[num_nodes], end_of_num_layers_array, num_nodes, learning_rate) else: results_of_last_layer = temp_layer.results_of_run temp_layer = ln.layer_of_neurons(num_rows, 1, results_of_last_layer, name_of_data_set, self.num_layers_array[num_nodes], end_of_num_layers_array, num_nodes, learning_rate) self.layers.append(temp_layer)
def main():
# Load data files
nRows_iris = 150
nColumns_iris = 5
nRows_diabetes = 768
nColumns_diabetes = 9
iris = lf.load_file("iris.csv", nRows_iris, nColumns_iris)
diabetes = lf.load_file("diabetes.data", nRows_diabetes, nColumns_diabetes)
# Normalize data files
iris = norm.normalize_iris(iris)
diabetes = norm.normalize_diabetes(diabetes)
# Feed inputs into neurons
layer_of_iris = ln.layer_of_neurons(nRows_iris, nColumns_iris, iris, "Iris")
layer_of_iris.run_neurons()
layer_of_iris.print_results()
layer_of_diabetes = ln.layer_of_neurons(nRows_diabetes, nColumns_diabetes, diabetes, "Diabetes")
layer_of_diabetes.run_neurons()
layer_of_diabetes.print_results()
def __init__(self, num_layers_array, num_rows, num_columns, data_set, name_of_data_set, learning_rate):
self.num_layers_array = num_layers_array
self.str_results = []
len_num_layers_array = len(num_layers_array)
self.layers = []
# Go through each layer
end_of_num_layers_array = False
for num_nodes in range(len_num_layers_array):
if num_nodes == len_num_layers_array - 1:
end_of_num_layers_array = True
else:
end_of_num_layers_array = False
if num_nodes == 0:
temp_layer = ln.layer_of_neurons(
num_rows,
num_columns,
data_set,
name_of_data_set,
self.num_layers_array[num_nodes],
end_of_num_layers_array,
num_nodes,
learning_rate,
)
else:
results_of_last_layer = temp_layer.results_of_run
temp_layer = ln.layer_of_neurons(
num_rows,
1,
results_of_last_layer,
name_of_data_set,
self.num_layers_array[num_nodes],
end_of_num_layers_array,
num_nodes,
learning_rate,
)
self.layers.append(temp_layer)
def main():
# Load data files
nRows_iris = 150
nColumns_iris = 5
nRows_diabetes = 768
nColumns_diabetes = 9
iris = lf.load_file("iris.csv", nRows_iris, nColumns_iris)
diabetes = lf.load_file("diabetes.data", nRows_diabetes, nColumns_diabetes)
# Normalize data files
iris = norm.normalize_iris(iris)
diabetes = norm.normalize_diabetes(diabetes)
# Feed inputs into neurons
layer_of_iris = ln.layer_of_neurons(nRows_iris, nColumns_iris, iris,
"Iris")
layer_of_iris.run_neurons()
layer_of_iris.print_results()
layer_of_diabetes = ln.layer_of_neurons(nRows_diabetes, nColumns_diabetes,
diabetes, "Diabetes")
layer_of_diabetes.run_neurons()
layer_of_diabetes.print_results()
