本文整理汇总了Python中q2_gradcheck.gradcheck_naive函数的典型用法代码示例。如果您正苦于以下问题：Python gradcheck_naive函数的具体用法？Python gradcheck_naive怎么用？Python gradcheck_naive使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了gradcheck_naive函数的10个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: sanity_check
def sanity_check():
"""
Run python q4_softmaxreg.py.
"""
random.seed(314159)
np.random.seed(265)
dataset = StanfordSentiment()
tokens = dataset.tokens()
nWords = len(tokens)
_, wordVectors0, _ = load_saved_params()
wordVectors = (wordVectors0[:nWords,:] + wordVectors0[nWords:,:])
dimVectors = wordVectors.shape[1]
dummy_weights = 0.1 * np.random.randn(dimVectors, 5)
dummy_features = np.zeros((10, dimVectors))
dummy_labels = np.zeros((10,), dtype=np.int32)
for i in xrange(10):
words, dummy_labels[i] = dataset.getRandomTrainSentence()
dummy_features[i, :] = getSentenceFeature(tokens, wordVectors, words)
print "==== Gradient check for softmax regression ===="
gradcheck_naive(lambda weights: softmaxRegression(dummy_features,
dummy_labels, weights, 1.0, nopredictions = True), dummy_weights)
print "\n=== Results ==="
print softmaxRegression(dummy_features, dummy_labels, dummy_weights, 1.0)
开发者ID:KyleGoyette，项目名称:cs224d，代码行数:27，代码来源:q4_softmaxreg.py
示例2: sanity_check
def sanity_check():
"""
Set up fake data and parameters for the neural network, and test using
gradcheck.
"""
print "Running sanity check..."
N = 20
############################################################################
# The following dimensions are Dx, H, Dy #
############################################################################
dimensions = [10, 5, 10]
data = np.random.randn(N, dimensions[0]) # each row will be a datum
labels = np.zeros((N, dimensions[2])) # lables are 1 only where it is True
for i in xrange(N):
labels[i,random.randint(0,dimensions[2]-1)] = 1
############################################################################
# All the params are packed here #
# I think this a bad way - You need to initialize using normal distribution#
# See the number of parameters in the q1.pdf #
############################################################################
params = np.random.randn((dimensions[0] + 1) * dimensions[1] + (
dimensions[1] + 1) * dimensions[2], )
gradcheck_naive(lambda params: forward_backward_prop(data, labels, params,
dimensions), params)
开发者ID:abhinavkashyap92，项目名称:cs224d，代码行数:27，代码来源:q2_neural.py
示例3: test_word2vec
def test_word2vec():
# Interface to the dataset for negative sampling
dataset = type('dummy', (), {})()
def dummySampleTokenIdx():
return random.randint(0, 4)
def getRandomContext(C):
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], [tokens[random.randint(0,4)] \
for i in xrange(2*C)]
dataset.sampleTokenIdx = dummySampleTokenIdx
dataset.getRandomContext = getRandomContext
random.seed(31415)
np.random.seed(9265)
dummy_vectors = normalizeRows(np.random.randn(10,3))
dummy_tokens = dict([("a",0), ("b",1), ("c",2),("d",3),("e",4)])
print "==== Gradient check for skip-gram ===="
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset, 5), dummy_vectors)
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset, 5, negSamplingCostAndGradient), dummy_vectors)
#print "\n==== Gradient check for CBOW ===="
#gradcheck_naive(lambda vec: word2vec_sgd_wrapper(cbow, dummy_tokens, vec, dataset, 5), dummy_vectors)
#gradcheck_naive(lambda vec: word2vec_sgd_wrapper(cbow, dummy_tokens, vec, dataset, 5, negSamplingCostAndGradient), dummy_vectors)
print "\n=== Results ==="
print skipgram("c", 3, ["a", "b", "e", "d", "b", "c"], dummy_tokens, dummy_vectors[:5,:], dummy_vectors[5:,:], dataset)
print skipgram("c", 1, ["a", "b"], dummy_tokens, dummy_vectors[:5,:], dummy_vectors[5:,:], dataset, negSamplingCostAndGradient)
开发者ID:situgongyuan，项目名称:DeepLearningForNLP，代码行数:27，代码来源:q3_word2vec.py
示例4: test_word2vec
def test_word2vec():
# Interface to the dataset for negative sampling
dataset = type('dummy', (), {})()
def dummySampleTokenIdx():
return random.randint(0, 4)
def getRandomContext(C):
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], [tokens[random.randint(0,4)] \
for i in xrange(2*C)]
def getContexts(C,sz=50):
contexts = []
for i in xrange(sz):
C1 = random.randint(1,C)
centerword, context = dataset.getRandomContext(C1)
contexts.append((C1, centerword, context))
return contexts
dataset.sampleTokenIdx = dummySampleTokenIdx
dataset.getRandomContext = getRandomContext
random.seed(31415)
np.random.seed(9265)
dummy_vectors = normalizeRows(np.random.randn(10,3))
dummy_tokens = dict([("a",0), ("b",1), ("c",2),("d",3),("e",4)])
def getNegSamples(contexts):
negsamples = []
for context in contexts:
samples = []
for contextWord in context[2]:
target = dummy_tokens[contextWord]
indices = [target]
for i in xrange(10):
k = dataset.sampleTokenIdx()
while k == target:
k = dataset.sampleTokenIdx()
indices.append(k)
samples.append(indices)
negsamples.append(samples)
return negsamples # negsamples: [samples],
# samples:[indices], indices:[rndSample1,..,rndSampleK]
dataset.contexts = getContexts(5)
print dataset.contexts
dataset.negsamples = getNegSamples(dataset.contexts)
print "==== Gradient check for skip-gram ===="
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset, 5), dummy_vectors)
gradcheck_naive(lambda vec: word2vec_sgd_wrapper(skipgram, dummy_tokens, vec, dataset, 5, negSamplingCostAndGradient), dummy_vectors)
#print "\n==== Gradient check for CBOW ===="
#gradcheck_naive(lambda vec: word2vec_sgd_wrapper(cbow, dummy_tokens, vec, dataset, 5), dummy_vectors)
#gradcheck_naive(lambda vec: word2vec_sgd_wrapper(cbow, dummy_tokens, vec, dataset, 5, negSamplingCostAndGradient), dummy_vectors)
print "\n=== Results ==="
print skipgram("c", 3, ["a", "b", "e", "d", "b", "c"], dummy_tokens, dummy_vectors[:5,:], dummy_vectors[5:,:], dataset, dataset.negsamples[0])
print skipgram("c", 1, ["a", "b"], dummy_tokens, dummy_vectors[:5,:], dummy_vectors[5:,:], dataset, dataset.negsamples[0], negSamplingCostAndGradient)
开发者ID:yysherlock，项目名称:cs224d，代码行数:58，代码来源:q3_word2vec_v1.py
示例5: my_test_word2vec
def my_test_word2vec():
# Interface to the dataset for negative sampling
dataset = type('dummy', (), {})()
def dummySampleTokenIdx():
return random.randint(0, 4)
def getRandomContext(C):
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], [tokens[random.randint(0,4)] \
for i in xrange(2*C)]
dataset.sampleTokenIdx = dummySampleTokenIdx
dataset.getRandomContext = getRandomContext
random.seed(31415)
np.random.seed(9265)
dummy_vectors = normalizeRows(np.random.randn(10,3))
dummy_tokens = dict([("a",0), ("b",1), ("c",2),("d",3),("e",4)])
print "==== Gradient check for skip-gram ===="
# def word2vec_sgd_wrapper(word2vecModel, tokens, wordVectors, dataset, C, word2vecCostAndGradient = softmaxCostAndGradient):
# params
word2vecModel = skipgram
tokens = dummy_tokens
wordVectors = dummy_vectors
C=5
word2vecCostAndGradient = softmaxCostAndGradient
batchsize = 50
cost = 0.0
grad = np.zeros(wordVectors.shape)
N = wordVectors.shape[0]
inputVectors = wordVectors[:N/2,:]
outputVectors = wordVectors[N/2:,:]
for i in xrange(batchsize):
C1 = random.randint(1,C)
centerword, context = dataset.getRandomContext(C1)
if word2vecModel == skipgram:
denom = 1
else:
denom = 1
def fInVec(inVec):
c,gin,gout = word2vecModel(centerword, C1, context, tokens, inVec, outputVectors, dataset, word2vecCostAndGradient)
return (c,gin)
def fOutVec(outVec):
c,gin,gout = word2vecModel(centerword, C1, context, tokens, inputVectors, outVec, dataset, word2vecCostAndGradient)
return (c,gout)
c, gin, gout = word2vecModel(centerword, C1, context, tokens, inputVectors, outputVectors, dataset, word2vecCostAndGradient)
# print gin
# cost += c / batchsize / denom
# grad[:N/2, :] += gin / batchsize / denom
# grad[N/2:, :] += gout / batchsize / denom
gradcheck_naive(fInVec, gin)
gradcheck_naive(fOutVec, gout)
开发者ID:scinart，项目名称:cs224d，代码行数:57，代码来源:q3_word2vec.py
示例6: sanity_check
def sanity_check():
"""
Run python q4_softmaxreg.py.
"""
random.seed(314159)
np.random.seed(265)
dataset = StanfordSentiment()
tokens = dataset.tokens()
nWords = len(tokens)
_, wordVectors0, _ = load_saved_params()
N = wordVectors0.shape[0]//2
#assert N == nWords
wordVectors = (wordVectors0[:N,:] + wordVectors0[N:,:])
dimVectors = wordVectors.shape[1]
dummy_weights = 0.1 * np.random.randn(dimVectors, 5)
dummy_features = np.zeros((10, dimVectors))
dummy_labels = np.zeros((10,), dtype=np.int32)
for i in range(10):
words, dummy_labels[i] = dataset.getRandomTrainSentence()
dummy_features[i, :] = getSentenceFeature(tokens, wordVectors, words)
print("==== Gradient check for softmax regression ====")
gradcheck_naive(lambda weights: softmaxRegression(dummy_features,
dummy_labels, weights, 1.0, nopredictions = True), dummy_weights)
print("\n=== Results ===")
print(softmaxRegression(dummy_features, dummy_labels, dummy_weights, 1.0))
dummy_weights = 0.1 * np.random.randn(40, 10) + 1.0
dummy_features = np.random.randn(2000, 40)
dummy_labels = np.argmax(np.random.randn(2000, 10), axis=1)
print(-np.log(0.1))#expected correct classification (random) = 1 in 10;
#cost then becomes -np.log(0.1)
print(softmaxRegression(dummy_features, dummy_labels, dummy_weights, 0.0)[0])
dummy_weights = 0.1 * np.random.randn(40, 80) + 1.0
dummy_features = np.random.randn(2000, 40)
dummy_labels = np.argmax(np.random.randn(2000, 80), axis=1)
print(-np.log(1./80))#expected correct classification (random) = 1 in 80;
#cost then becomes -np.log(1./80)
print(softmaxRegression(dummy_features, dummy_labels, dummy_weights, 0.0)[0])
dummy_weights = 0.1 * np.random.randn(40, 1000) + 1.0
dummy_features = np.random.randn(40000, 40)
dummy_labels = np.argmax(np.random.randn(40000, 1000), axis=1)
print(-np.log(1./1000))#expected correct classification (random) = 1 in 80;
#cost then becomes -np.log(1./80)
print(softmaxRegression(dummy_features, dummy_labels, dummy_weights, 0.0)[0])
print(np.exp(-softmaxRegression(dummy_features, dummy_labels, dummy_weights, 0.0)[0]))
开发者ID:kingtaurus，项目名称:cs224d，代码行数:54，代码来源:q4_softmaxreg.py
示例7: your_sanity_checks
def your_sanity_checks():
"""
Use this space add any additional sanity checks by running:
python q2_neural.py
This function will not be called by the autograder, nor will
your additional tests be graded.
"""
print "Running your sanity checks..."
### YOUR CODE HERE
input = np.array([[0.5, 0.3, -1.5, 0.0, -0.2]])
labels = np.array([[0.2, 0.2, 0.2, 0.2, 0.2]])
dimensions = [5, 3, 5]
params = np.random.randn((dimensions[0] + 1) * dimensions[1] + (dimensions[1] + 1) * dimensions[2], )
gradcheck_naive(lambda params: forward_backward_prop(input, labels, params, dimensions), params)
开发者ID:ZhangBanger，项目名称:cs224d，代码行数:16，代码来源:q2_neural.py
示例8: sanity_check
def sanity_check():
"""
Set up fake data and parameters for the neural network, and test using
gradcheck.
"""
print "Running sanity check..."
N = 20
dimensions = [10, 5, 10]
data = np.random.randn(N, dimensions[0]) # each row will be a datum
labels = np.zeros((N, dimensions[2]))
for i in xrange(N):
labels[i,random.randint(0,dimensions[2]-1)] = 1
params = np.random.randn((dimensions[0] + 1) * dimensions[1] + (dimensions[1] + 1) * dimensions[2], )
gradcheck_naive(lambda params: forward_backward_prop(data, labels, params,dimensions), params)
开发者ID:cdelichy92，项目名称:DeepLearning-NLP，代码行数:17，代码来源:q2_neural.py
示例9: sanity_check
def sanity_check():
"""
Set up fake data and parameters for the neural network, and test using
gradcheck.
"""
print("Running sanity check...")
N = 300
dimensions = [10, 5, 10]
data = np.random.randn(N, dimensions[0]) # each row will be a datum
labels = np.zeros((N, dimensions[2]))
for i in range(N):
labels[i,random.randint(0,dimensions[2]-1)] = 1
params = np.random.randn((dimensions[0] + 1) * dimensions[1] + (
dimensions[1] + 1) * dimensions[2], )
#cost, _ = forward_backward_prop(data, labels, params, dimensions)
# # expect to get 1 in 10 correct
#print(np.exp(-cost))
# #cost is roughly correct
gradcheck_naive(lambda params: forward_backward_prop(data, labels, params,
dimensions), params)
开发者ID:kingtaurus，项目名称:cs224d，代码行数:24，代码来源:q2_neural.py
示例10: test_word2vec
def test_word2vec():
# Interface to the dataset for negative sampling
dataset = type('dummy', (), {})()
def dummySampleTokenIdx():
return random.randint(0, 4)
def getRandomContext(C):
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], [tokens[random.randint(0,4)] \
for i in xrange(2*C)]
dataset.sampleTokenIdx = dummySampleTokenIdx
dataset.getRandomContext = getRandomContext
random.seed(31415)
np.random.seed(9265)
dummy_vectors = normalizeRows(np.random.randn(10,3))
dummy_tokens = dict([("a",0), ("b",1), ("c",2),("d",3),("e",4)])
print "==== Gradient check for skip-gram ===="
C1 = random.randint(1,5)
centerword, context = dataset.getRandomContext(C1)
negsamples = []
for contextWord in context:
target = dummy_tokens[contextWord]
indices = [target]
for i in xrange(10):
k = dataset.sampleTokenIdx()
while k == target:
k = dataset.sampleTokenIdx()
indices.append(k)
negsamples.append(indices)
dataset.negsamples = negsamples
gradcheck_naive(lambda vec: my_sgd_wrapper(skipgram, dummy_tokens, vec, dataset, C1, centerword, context), dummy_vectors)
开发者ID:yysherlock，项目名称:cs224d，代码行数:36，代码来源:q3_word2vec_test.py
注：本文中的q2_gradcheck.gradcheck_naive函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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