def test_softmax_cost_gradient(self):
vocabulary_size = 5
vector_size = 2
input = np.random.randint(0, vocabulary_size)
output = np.random.randint(0, vocabulary_size)
def softmax_cost_gradient_wrapper(parameters):
cost, gradient = softmax_cost_gradient(parameters, input, output)
return cost, gradient
initial_parameters = np.random.normal(size=(2, vocabulary_size, vector_size))
result = gradient_check(softmax_cost_gradient_wrapper, initial_parameters)
self.assertEqual([], result)
def test_skip_gram(self):
vocabulary_size = 5
vector_size = 2
context_size = 3
data_size = 4
inputs = np.random.randint(0, vocabulary_size, size=data_size)
outputs = np.random.randint(0, vocabulary_size, size=(data_size, context_size))
initial_parameters = np.random.normal(size=(2, vocabulary_size, vector_size))
# Create cost and gradient function for supervised SGD and check its gradient
cost_gradient = bind_cost_gradient(skip_gram_cost_gradient, inputs, outputs, sampler=batch_sampler)
result = gradient_check(cost_gradient, initial_parameters)
self.assertEqual([], result)
final_parameters, cost_history = gradient_descent(cost_gradient, initial_parameters, 10)
def test_softmax_cost_gradient(self):
vocabulary_size = 5
vector_size = 2
input = np.random.randint(0, vocabulary_size)
output = np.random.randint(0, vocabulary_size)
def softmax_cost_gradient_wrapper(parameters):
cost, gradient = softmax_cost_gradient(parameters, input, output)
return cost, gradient
initial_parameters = np.random.normal(size=(2, vocabulary_size,
vector_size))
result = gradient_check(softmax_cost_gradient_wrapper,
initial_parameters)
self.assertEqual([], result)
def test_skip_gram(self):
vocabulary_size = 5
vector_size = 2
context_size = 3
data_size = 4
inputs = np.random.randint(0, vocabulary_size, size=data_size)
outputs = np.random.randint(0,
vocabulary_size,
size=(data_size, context_size))
initial_parameters = np.random.normal(size=(2, vocabulary_size,
vector_size))
# Create cost and gradient function for supervised SGD and check its gradient
cost_gradient = bind_cost_gradient(skip_gram_cost_gradient,
inputs,
outputs,
sampler=batch_sampler)
result = gradient_check(cost_gradient, initial_parameters)
self.assertEqual([], result)
final_parameters, cost_history = gradient_descent(
cost_gradient, initial_parameters, 10)
