def test_neuralnet_grad(nn):
f, grad = back_prop1(nn.data, nn.labels, nn.params, dd.dimensions)
emp_grad = empirical_grad(lambda params: back_prop1(data, labels, params, dimensions), params)
assert_close(grad, emp_grad)
# Set up fake data and parameters for the neural network
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], )
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)]
