我正在尝试拟合神经网络代码，以使其使用sklearn乳腺癌数据库。这是我尝试过的代码：
from numpy import exp, array, random, dot
class NeuronLayer():
def __init__(self, number_of_neurons, number_of_inputs_per_neuron):
self.synaptic_weights = 2 * random.random((number_of_inputs_per_neuron, number_of_neurons)) - 1
class NeuralNetwork():
def __init__(self, layer1, layer2):
self.layer1 = layer1
self.layer2 = layer2
# The Sigmoid function, which describes an S shaped curve.
# We pass the weighted sum of the inputs through this function to
# normalise them between 0 and 1.
def __sigmoid(self, x):
return 1 / (1 + exp(-x))
# The derivative of the Sigmoid function.
# This is the gradient of the Sigmoid curve.
# It indicates how confident we are about the existing weight.
def __sigmoid_derivative(self, x):
return x * (1 - x)
# We train the neural network through a process of trial and error.
# Adjusting the synaptic weights each time.
def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
for iteration in range(0, number_of_training_iterations):
# Pass the training set through our neural network
output_from_layer_1, output_from_layer_2 = self.think(training_set_inputs)
# Calculate the error for layer 2 (The difference between the desired output
# and the predicted output).
layer2_error = training_set_outputs - output_from_layer_2
layer2_delta = layer2_error * self.__sigmoid_derivative(output_from_layer_2)
# Calculate the error for layer 1 (By looking at the weights in layer 1,
# we can determine by how much layer 1 contributed to the error in layer 2).
layer1_error = layer2_delta.dot(self.layer2.synaptic_weights.T)
layer1_delta = layer1_error * self.__sigmoid_derivative(output_from_layer_1)
# Calculate how much to adjust the weights by
layer1_adjustment = training_set_inputs.T.dot(layer1_delta)
layer2_adjustment = output_from_layer_1.T.dot(layer2_delta)
# Adjust the weights.
self.layer1.synaptic_weights += layer1_adjustment
self.layer2.synaptic_weights += layer2_adjustment
# The neural network thinks.
def think(self, inputs):
output_from_layer1 = self.__sigmoid(dot(inputs, self.layer1.synaptic_weights))
output_from_layer2 = self.__sigmoid(dot(output_from_layer1, self.layer2.synaptic_weights))
return output_from_layer1, output_from_layer2
# The neural network prints its weights
def print_weights(self):
print (" Layer 1 (4 neurons, each with 3 inputs): ")
print (self.layer1.synaptic_weights)
print (" Layer 2 (1 neuron, with 4 inputs):")
print (self.layer2.synaptic_weights)
if __name__ == "__main__":
# Leer el dataset
db_breast_cancer = datasets.load_breast_cancer()
# Creamos los conjuntos de entrenamiento y test.
conjunto_de_datos = db_breast_cancer.data # Las características del conjunto de datos
target = db_breast_cancer.target # Los targets del conjunto de datos
tamanio_conjunto_test = 0.30 # Tamaño para el conjunto de test en %
numero_semilla = 7 # Semilla para generar aleatoriedad
rango_a_estudiar = range(0, 30) # Seleccionar un rango de ejecuciones del algoritmo
# Separar conjunto de datos en entrenamiento y test
training_set_inputs, \
test_set_inputs, \
training_set_outputs, \
test_set_outputs = \
model_selection.train_test_split(
conjunto_de_datos,
target,
test_size=tamanio_conjunto_test,
random_state=numero_semilla
)
#Seed the random number generator
random.seed(1)
# Create layer 1 (4 neurons, each with 30 inputs)
layer1 = NeuronLayer(4, 30)
# Create layer 2 (a single neuron with 4 inputs)
layer2 = NeuronLayer(1, 4)
# Combine the layers to create a neural network
neural_network = NeuralNetwork(layer1, layer2)
print ("Stage 1) Random starting synaptic weights: ")
neural_network.print_weights()
# The training set. We have 7 examples, each consisting of 3 input values
# and 1 output value.
#training_set_inputs = array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [0, 1, 0], [1, 0, 0], [1, 1, 1], [0, 0, 0]])
#training_set_outputs = array([[0, 1, 1, 1, 1, 0, 0]]).T
# Train the neural network using the training set.
# Do it 60,000 times and make small adjustments each time.
neural_network.train(training_set_inputs, training_set_outputs.T, 60000)
print ("Stage 2) New synaptic weights after training: ")
neural_network.print_weights()
# Test the neural network with a new situation.
print ("Stage 3) Considering a new situation [1, 1, 0] -> ?: ")
hidden_state, output = neural_network.think(test_set_input[0])
print (output)
但是我遇到下一个错误：
ValueError：形状（398,398）和（1,4）未对齐：398（dim 1）！= 1 （昏暗0）。
更明确地，错误是：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-9-335a2d34ac99> in <module>()
103 # Train the neural network using the training set.
104 # Do it 60,000 times and make small adjustments each time.
--> 105 neural_network.train(training_set_inputs, training_set_outputs.T, 60000)
106
107 print ("Stage 2) New synaptic weights after training: ")
<ipython-input-9-335a2d34ac99> in train(self, training_set_inputs, training_set_outputs, number_of_training_iterations)
38 # Calculate the error for layer 1 (By looking at the weights in layer 1,
39 # we can determine by how much layer 1 contributed to the error in layer 2).
---> 40 layer1_error = layer2_delta.dot(self.layer2.synaptic_weights.T)
41 layer1_delta = layer1_error * self.__sigmoid_derivative(output_from_layer_1)
42
ValueError: shapes (398,398) and (1,4) not aligned: 398 (dim 1) != 1 (dim 0)
任何想法我该如何解决？
谢谢。