奇怪的bug，是我的环境问题还是代码问题，有人能帮忙看一下吗，基础薄弱的菜鸟求助..
import copy, numpy as npnp.random.seed(0)
# compute sigmoid nonlinearity
def sigmoid(x):
output = 1/(1+np.exp(-x))
return output
# convert output of sigmoid function to its derivative
def sigmoid_output_to_derivative(output):
return output*(1-output)
# training dataset generation
int2binary = {}
binary_dim = 8
largest_number = pow(2,binary_dim)
binary = np.unpackbits(
np.array(,dtype=np.uint8).T,axis=1)
for i in range(largest_number):
int2binary = binary
# input variables
alpha = 0.1
input_dim = 2
hidden_dim = 16
output_dim = 1
# initialize neural network weights
synapse_0 = 2*np.random.random((input_dim,hidden_dim)) - 1
synapse_1 = 2*np.random.random((hidden_dim,output_dim)) - 1
synapse_h = 2*np.random.random((hidden_dim,hidden_dim)) - 1
synapse_0_update = np.zeros_like(synapse_0)
synapse_1_update = np.zeros_like(synapse_1)
synapse_h_update = np.zeros_like(synapse_h)
# training logic
for j in range(10000):
# generate a simple addition problem (a + b = c)
a_int = np.random.randint(largest_number/2) # int version
a = int2binary # binary encoding
b_int = np.random.randint(largest_number/2) # int version
b = int2binary # binary encoding
# true answer
c_int = a_int + b_int
c = int2binary
# where we'll store our best guess (binary encoded)
d = np.zeros_like(c)
overallError = 0
layer_2_deltas = list()
layer_1_values = list()
layer_1_values.append(np.zeros(hidden_dim))
# moving along the positions in the binary encoding
for position in range(binary_dim):
# generate input and output
X = np.array([,b]])
y = np.array([]]).T
# hidden layer (input ~+ prev_hidden)
layer_1 = sigmoid(np.dot(X,synapse_0) + np.dot(layer_1_values[-1],synapse_h))
# output layer (new binary representation)
layer_2 = sigmoid(np.dot(layer_1,synapse_1))
# did we miss?... if so by how much?
layer_2_error = y - layer_2
layer_2_deltas.append((layer_2_error)*sigmoid_output_to_derivative(layer_2))
overallError += np.abs(layer_2_error)
# decode estimate so we can print it out
d = np.round(layer_2)
# store hidden layer so we can use it in the next timestep
layer_1_values.append(copy.deepcopy(layer_1))
future_layer_1_delta = np.zeros(hidden_dim)
for position in range(binary_dim):
X = np.array([,b]])
layer_1 = layer_1_values[-position-1]
prev_layer_1 = layer_1_values[-position-2]
# error at output layer
layer_2_delta = layer_2_deltas[-position-1]
# error at hidden layer
layer_1_delta = (future_layer_1_delta.dot(synapse_h.T) + \
layer_2_delta.dot(synapse_1.T)) * sigmoid_output_to_derivative(layer_1)
# let's update all our weights so we can try again
synapse_1_update += np.atleast_2d(layer_1).T.dot(layer_2_delta)
synapse_h_update += np.atleast_2d(prev_layer_1).T.dot(layer_1_delta)
synapse_0_update += X.T.dot(layer_1_delta)
future_layer_1_delta = layer_1_delta
synapse_0 += synapse_0_update * alpha
synapse_1 += synapse_1_update * alpha
synapse_h += synapse_h_update * alpha
synapse_0_update *= 0
synapse_1_update *= 0
synapse_h_update *= 0
# print out progress
if(j % 1000 == 0):
print "Error:" + str(overallError)
print "Pred:" + str(d)
print "True:" + str(c)
out = 0
for index,x in enumerate(reversed(d)):
out += x*pow(2,index)
print str(a_int) + " + " + str(b_int) + " = " + str(out)
print "------------"
代码是用python2写的，你运行的是python3吧，把最下面几行的print语句后面都加上括号就好了。
如：
print ("Error:" + str(overallError))
print ("Pred:" + str(d))
print ("True:" + str(c))
out = 0
for index,x in enumerate(reversed(d)):
out += x*pow(2,index)
print (str(a_int) + " + " + str(b_int) + " = " + str(out))
print ("------------") 露转溪桥 发表于 2018-9-4 17:13
代码是用python2写的，你运行的是python3吧，把最下面几行的print语句后面都加上括号就好了。
如：
print ...
哦哦，之前一直用的是python2，今天刚改用python3，怪不得，谢谢{:5_106:}
页:
[1]