class NeuralNetwork:
def init(self, layers, activation='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) #x trainning dataset y target
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): #loop times
i = np.random.randint(X.shape[0])
a = [X[i]] #trainning data
for l in range(len(self.weights)): #going forward network, for each layer
a.append(self.activation(np.dot(a[l], self.weights[l]))) #Computer the node value for each layer (O_i) using activation function
error = y[i] - a[-1] #Computer the error at the top layer
deltas = [error * self.activation_deriv(a[-1])] #For output layer, Err calculation (delta is updated error)
#Staring backprobagation
for l in range(len(a) - 2, 0, -1): # we need to begin at the second to last layer
#Compute the updated error (i,e, deltas) for each node going from top layer to input 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
