def test_train(input_units, hidden_units, batch_size, nr_samples, rgen):
nn = FcClassifier(input_units, hidden_units)
nn.init_random()
x_in = rgen.randn(nr_samples, input_units)
y_in = rgen.randint(2, size=nr_samples)
cost_old = nn.evaluate(x_in.T, y_in)
nn.train(x_in,
y_in,
learning_rate=0.0005,
nr_epochs=1,
batch_size=batch_size)
cost_new = nn.evaluate(x_in.T, y_in)
assert cost_old > cost_new
def test_evaluate(input_units, hidden_units, nr_samples, rgen):
nn = FcClassifier(input_units, hidden_units)
nn.init_random()
parameters = nn.get_weights()
weights = list(w for w, _ in parameters)
biases = list(b for _, b in parameters)
x_in = rgen.randn(input_units, nr_samples)
y_in = rgen.randint(2, size=nr_samples)
cost = nn.evaluate(x_in, y_in)
y_ref = fcnn_predict(x_in, weights, biases, it.repeat(sigmoid))
cost_ref = cross_entropy(y_in, y_ref)
assert_almost_equal(cost, cost_ref)