def test_normalize_rows():
assert normalize_rows(np.array([[3.0, 4.0],[1, 2]])), [[0.6, 0.8], [0.4472, 0.8944]]
cost, grad_pred, grad = f(*params, **kws)
return cost, grad
gradcheck_naive(lambda vec: g_func_wrapper1(softmax_cost_and_gradient, vec, 0, dummy_vectors[1:], dataset, parameters=parameters),
dummy_vectors[0])
gradcheck_naive(lambda vec: g_func_wrapper2(softmax_cost_and_gradient, dummy_vectors[0], 0, vec, dataset, parameters=parameters),
dummy_vectors[1:])
print "==== Gradient check for neg_sampling_max_cost_and_gradient ===="
print "test 1"
gradcheck_naive(lambda vec: g_func_wrapper1(neg_sampling_cost_and_gradient, vec, 0, dummy_vectors[1:], dataset, parameters=parameters),
dummy_vectors[0], verbose=False)
print "test 2"
gradcheck_naive(lambda vec: g_func_wrapper2(neg_sampling_cost_and_gradient, dummy_vectors[0], 0, vec, dataset, parameters=parameters),
dummy_vectors[1:])
parameters = AttrDict(
{
'context_size' : 1,
'sgd' : {'batch_size': 1},
'dataset' : {}
}
)
random.seed(31415)
np.random.seed(9265)
dummy_vectors = normalize_rows(np.random.randn(10,3))
dummy_tokens = dict([("a",0), ("b",1), ("c",2), ("d",3), ("e",4)])
def vectors():
return normalize_rows(np.random.randn(10, 3))
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], )
print "Dimensionality of parameter vector", params.shape
# Perform gradcheck on your neural network
print "=== Neural network gradient check 1==="
check_res = gradcheck_naive(lambda params: back_prop1(data, labels, params, dimensions), params)
print "=== Neural network gradient check 2==="
#check_res = gradcheck_naive(lambda params: back_prop2(data, labels, params, dimensions), params)
print "=== normalize rows ==="
print normalize_rows(np.array([[3.0, 4.0],[1, 2]])) # the result should be [[0.6, 0.8], [0.4472, 0.8944]]
# Interface to the dataset for negative sampling
dataset = type('dummy', (), {})()
def dummySampleTokenIdx():
return random.randint(0, 4)
def getRandomContext(C, parameters=None):
tokens = ["a", "b", "c", "d", "e"]
return tokens[random.randint(0,4)], [tokens[random.randint(0,4)] for i in xrange(2*C)]
dataset.sample_token_idx = dummySampleTokenIdx
dataset.get_context = getRandomContext
parameters = AttrDict(
{
'context_size' : 3,
