Python实现的递归神经网络简单示例


            本文实例讲述了Python实现的递归神经网络。分享给大家供大家参考，具体如下：





# Recurrent Neural Networks

import copy, numpy as np

np.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([range(largest_number)],dtype=np.uint8).T,axis=1)

for i in range(largest_number):

  int2binary[i] = binary[i]

# 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[a_int] # binary encoding

  b_int = np.random.randint(largest_number/2) # int version

  b = int2binary[b_int] # binary encoding

  # true answer

  c_int = a_int + b_int

  c = int2binary[c_int]

  # 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([[a[binary_dim - position - 1],b[binary_dim - position - 1]]])

    y = np.array([[c[binary_dim - position - 1]]]).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[0])

    # decode estimate so we can print(it out)

    d[binary_dim - position - 1] = np.round(layer_2[0][0])

    # 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([[a[position],b[position]]])

    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("------------")







运行输出：





Error:[ 3.45638663]

Pred:[0 0 0 0 0 0 0 1]

True:[0 1 0 0 0 1 0 1]

9 + 60 = 1

------------

Error:[ 3.63389116]

Pred:[1 1 1 1 1 1 1 1]

True:[0 0 1 1 1 1 1 1]

28 + 35 = 255

------------

Error:[ 3.91366595]

Pred:[0 1 0 0 1 0 0 0]

True:[1 0 1 0 0 0 0 0]

116 + 44 = 72

------------

Error:[ 3.72191702]

Pred:[1 1 0 1 1 1 1 1]

True:[0 1 0 0 1 1 0 1]

4 + 73 = 223

------------

Error:[ 3.5852713]

Pred:[0 0 0 0 1 0 0 0]

True:[0 1 0 1 0 0 1 0]

71 + 11 = 8

------------

Error:[ 2.53352328]

Pred:[1 0 1 0 0 0 1 0]

True:[1 1 0 0 0 0 1 0]

81 + 113 = 162

------------

Error:[ 0.57691441]

Pred:[0 1 0 1 0 0 0 1]

True:[0 1 0 1 0 0 0 1]

81 + 0 = 81

------------

Error:[ 1.42589952]

Pred:[1 0 0 0 0 0 0 1]

True:[1 0 0 0 0 0 0 1]

4 + 125 = 129

------------

Error:[ 0.47477457]

Pred:[0 0 1 1 1 0 0 0]

True:[0 0 1 1 1 0 0 0]

39 + 17 = 56

------------

Error:[ 0.21595037]

Pred:[0 0 0 0 1 1 1 0]

True:[0 0 0 0 1 1 1 0]

11 + 3 = 14

------------







英文原文：http://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

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