我正在尝试将神经网络(多层感知器)应用于我的数据。我收到此错误:ValueError:形状(3,)和(4,99)未对齐:3(dim 0)!= 4(dim 0)
我在这一行有错误: a = self.activation(np.dot(a, self.weights[l]))
如果有人可以帮助我,我会很高兴。谢谢。
nn_inputs: [[15, 0, 2.48489062802], [-35, 29, 1.15616438943], [-5, -1, 2.32958496377], [-48, 33, 0.706488511889], [-10, 2, 2.0951038628] 3、11、1.8423515073]]
nn_labels: [0, 1, 0, 1, 0, 1]
def tanh(x):
return np.tanh(x)
def tanh_deriv(x):
return 1.0 - np.tanh(x)**2
def logistic(x):
return 1/(1 + np.exp(-x))
def logistic_derivative(x):
return logistic(x)*(1-logistic(x))
class NeuralNetwork:
def __init__(self, layers, activation='tanh'):
"""
:param layers: A list containing the number of units in each layer.
Should be at least two values
:param activation: The activation function to be used. Can be
"logistic" or "tanh"
"""
if activation == 'logistic':
self.activation = logistic
self.activation_deriv = logistic_derivative
elif activation == 'tanh':
self.activation = tanh
self.activation_deriv = tanh_deriv
self.weights = []
for i in range(1, len(layers) - 1):
self.weights.append((2*np.random.random((layers[i - 1] + 1, layers[i]+ 1))-1)*0.25)
self.weights.append((2*np.random.random((layers[i] + 1, layers[i +
1]))-1)*0.25)
def fit(self, X, y, learning_rate=0.2, epochs=10000):
X = np.atleast_2d(X)
temp = np.ones([X.shape[0], X.shape[1]+1])
temp[:, 0:-1] = X # adding the bias unit to the input layer
X = temp
y = np.array(y)
for k in range(epochs):
i = np.random.randint(X.shape[0])
a = [X[i]]
for l in range(len(self.weights)):
a.append(self.activation(np.dot(a[l], self.weights[l])))
error = y[i] - a[-1]
deltas = [error * self.activation_deriv(a[-1])]
for l in range(len(a) - 2, 0, -1): # we need to begin at the second to last layer
deltas.append(deltas[-1].dot(self.weights[l].T)*self.activation_deriv(a[l]))
deltas.reverse()
for i in range(len(self.weights)):
layer = np.atleast_2d(a[i])
delta = np.atleast_2d(deltas[i])
self.weights[i] += learning_rate * layer.T.dot(delta)
def predict(self, x):
x = np.array(x)
temp = np.ones(x.shape[0]+1)
temp[0:-1] = x
a = temp
for l in range(0, len(self.weights)):
a = self.activation(np.dot(a, self.weights[l]))
return a
nn_inputs = map(list, zip(speed, occupancy, capacity))
nn_labels = labels
nn = NeuralNetwork([3,len(nn_inputs),1], 'tanh')
nn.fit(nn_inputs, nn_labels)
for i in [[0, 0], [0, 1], [1, 0], [1,1]]:
print(i,nn.predict(i))