作为一个较大项目的一部分，我正在尝试构建一个神经网络，该神经网络是使用两个独立的训练集针对一个输入训练序列建立的。换句话说，我有一个输入数组，我想构建两个独立的突触进行训练。
最简单的说，它看起来像这样：
x = [[0,0,0]，[0,1,1]，[0,1,0] ...] y = [[0,1]，[0,0]，[1 ，0] ...] z = [[0,0,0,0,1]，[1,0,0,0,0]，[1,1,0,1,1] ...]
其中y和z被训练为针对x的独立突触。
我一直无法修改我的代码以使其正常工作，我知道它不可能太复杂，但是我已经坚持了一天多，需要一些帮助。
下面是我似乎无法使用多个维度的代码。
# Import Necessary Modules for Function
import numpy as np
import time
# Computer a non-linear Sigmoid curve (__--)
def sigmoid(x):
sigmoidOutput = 1/(1+np.exp(-x))
return sigmoidOutput
# Convert the output of the signmoid function to its derivative
def sigmoidDerivative(sigmoidOutput):
return sigmoidOutput*(1-sigmoidOutput)
def cleanSentence(sentence):
# Tokenize the words within the input sentence
sentenceWords = nltk.word_tokenize(sentence)
# Stem the words within the tokenized setence
sentenceWords = [stemmer.stem(userInput.lower()) for userInput in sentenceWords]
return sentenceWords
# Return a binary bag of words [0 or 1] to evaluate whether or not a word exists within
# a sentence.
def bagWordCheck(sentence, userInput, show_details=False):
# Tokenize the sentence
sentenceWords = cleanSentence(sentence)
# Create a word bag using the training-data from the user-input
wordBag = [0]*len(userInput)
for sWord in sentenceWords:
for i,w in enumerate(userInput):
if w == sWord:
wordBag[i] = 1
if show_details:
print ("found in bag: %s" % w)
return(np.array(wordBag))
# Evaluate the user's input
def think(sentence, showDetails=False):
x = bagWordCheck(sentence.lower(), userInput, showDetails)
if showDetails:
print ("sentence:", sentence, "\n bagWordCheck:", x)
# input layer is our bag of words
l0 = x
# matrix multiplication of input and hidden layer
l1 = sigmoid(np.dot(l0, synapse0))
# output layer
l2 = sigmoid(np.dot(l1, synapse2))
return l2
# ANN and Gradient Descent code from https://iamtrask.github.io//2015/07/27/python-network-part2/
def train(X, y, hidden_neurons=10, alpha=1, epochs=50000, dropout=False, dropout_percent=0.5):
print ("Training with %s neurons, alpha:%s, dropout:%s %s" % (hidden_neurons, str(alpha), dropout, dropout_percent if dropout else '') )
print ("Input matrix: %sx%s Output matrix: %sx%s" % (len(X),len(X[0]),1, len(conversationTypes)) )
np.random.seed(1)
lastMeanError = 1
# randomly initialize our weights with mean 0
synapse0 = 2*np.random.random((len(X[0]), hidden_neurons)) - 1
synapse2 = 2*np.random.random((hidden_neurons, len(conversations))) - 1
#synapse2 = 2*np.random.random((hidden_neurons, len(conversationTypes))) - 1
#synapse2 = 2*np.random.random((hidden_neurons, len(conversationSubjects))) - 1
prev_synapse0_weight_update = np.zeros_like(synapse0)
prev_synapse2_weight_update = np.zeros_like(synapse2)
synapse0_direction_count = np.zeros_like(synapse0)
synapse2_direction_count = np.zeros_like(synapse2)
for j in iter(range(epochs+1)):
# Feed forward through layers 0, 1, and 2
layer_0 = X
layer_1 = sigmoid(np.dot(layer_0, synapse0))
if(dropout):
layer_1 *= np.random.binomial([np.ones((len(X),hidden_neurons))],1-dropout_percent)[0] * (1.0/(1-dropout_percent))
layer_2 = sigmoid(np.dot(layer_1, synapse2))
# how much did we miss the target value?
layer_2_error = y - layer_2
if (j% 10000) == 0 and j > 5000:
# if this 10k iteration's error is greater than the last iteration, break out
if np.mean(np.abs(layer_2_error)) < lastMeanError:
print ("delta after "+str(j)+" iterations:" + str(np.mean(np.abs(layer_2_error))) )
lastMeanError = np.mean(np.abs(layer_2_error))
else:
print ("break:", np.mean(np.abs(layer_2_error)), ">", lastMeanError )
break
# in what direction is the target value?
# were we really sure? if so, don't change too much.
layer_2_delta = layer_2_error * sigmoidDerivative(layer_2)
# how much did each l1 value contribute to the l2 error (according to the weights)?
layer_1_error = layer_2_delta.dot(synapse2.T)
# in what direction is the target l1?
# were we really sure? if so, don't change too much.
layer_1_delta = layer_1_error * sigmoidDerivative(layer_1)
synapse2_weight_update = (layer_1.T.dot(layer_2_delta))
synapse0_weight_update = (layer_0.T.dot(layer_1_delta))
if(j > 0):
synapse0_direction_count += np.abs(((synapse0_weight_update > 0)+0) - ((prev_synapse0_weight_update > 0) + 0))
synapse2_direction_count += np.abs(((synapse2_weight_update > 0)+0) - ((prev_synapse2_weight_update > 0) + 0))
synapse2 += alpha * synapse2_weight_update
synapse0 += alpha * synapse0_weight_update
prev_synapse0_weight_update = synapse0_weight_update
prev_synapse2_weight_update = synapse2_weight_update
now = datetime.datetime.now()
# persist synapses
synapse = {'synapse0': synapse0.tolist(), 'synapse2': synapse2.tolist(),
'datetime': now.strftime("%Y-%m-%d %H:%M"),
'userInput': userInput,
'conversations' : conversations,
'conversationTypes': conversationTypes,
'conversationSubjects' : conversationSubjects
}
synapse_file = "synapses.json"
with open(synapse_file, 'w') as outfile:
json.dump(synapse, outfile, indent=4, sort_keys=True)
print ("saved synapses to:", synapse_file)
X = np.array(trainingSet)
y = np.array(completeConversations)
y1 = np.array(completeTypes)
y2 = np.array(completeSubjects)
start_time = time.time()
train(X, y, hidden_neurons=5, alpha=0.1, epochs=30000, dropout=False, dropout_percent=0.2)
#train(X, y1, hidden_neurons=5, alpha=0.1, epochs=30000, dropout=False, dropout_percent=0.2)
#train(X, y2, hidden_neurons=5, alpha=0.1, epochs=30000, dropout=False, dropout_percent=0.2)
elapsed_time = time.time() - start_time
print ("processing time:", elapsed_time, "seconds")
# probability threshold
ERROR_THRESHOLD = 0.75
# load our calculated synapse values
synapse_file = 'synapses.json'
with open(synapse_file) as data_file:
synapse = json.load(data_file)
synapse0 = np.asarray(synapse['synapse0'])
synapse2 = np.asarray(synapse['synapse2'])
def classify(sentence, showDetails=False):
results = think(sentence, showDetails)
results = [[i,r] for i,r in enumerate(results) if r>ERROR_THRESHOLD ]
results.sort(key=lambda x: x[1], reverse=True)
return_results =[[conversations[r[0]],r[1]] for r in results]
#return_results =[[conversationSubjects[r[0]],r[1]] for r in results]
#return_results =[[conversationTypes[r[0]],r[1]] for r in results]
return return_results
classify("charlotte explorer is not letting me ")
请帮助！