class DenseTest(unittest.TestCase):
# When a setUp() method is defined, the test runner will run that method
# prior to each test. Likewise, if a tearDown() method is defined, the
# test runner will invoke that method after each test.
def setUp(self):
self.N = 2 # number of inputs
self.D = 3 # input dimension
self.H = 4 # output/hidden dimension
self.layer = Dense() # defines the layer we are testing
def tearDown(self):
pass
def test_one_plus_one(self):
self.assertEqual(1 + 1, 2)
def test_one_plus_two(self):
self.assertEqual(1 + 2, 3)
def test_forward(self):
# `numpy.linspace(start, stop, num=50...)` returns an array of evenly
# spaced numbers over a specified interval.
# `num=` Number of samples to generate. Default is 50. Must be non-negative.
x = np.linspace(-1, 1, num=self.N * self.D).reshape(self.N, self.D)
w = np.linspace(-0.5, 0.5, num=self.D * self.H).reshape(self.D, self.H)
b = np.linspace(-0.5, 0.5, num=self.H)
output = self.layer.forward(x, w, b)
expected_output = np.dot(x, w) + b
np.testing.assert_array_almost_equal(expected_output, output, decimal=9)
# TODO: line 28-30 why those ranges?
random.shuffle(index_list)
for i in range(image_number // batch_size):
train_image = np.zeros((784, batch_size))
train_label = np.zeros((10, batch_size))
for j in range(batch_size):
index = index_list[i * batch_size + j]
inputx = (img[index] - 128) / 256.0
inputx = inputx.reshape((784, 1))
label = np.zeros([10, 1])
label[labels[index]] = 1
train_image[:, j:j + 1] = inputx
train_label[:, j:j + 1] = label
dense.forward(train_image)
sigmoid.forward(dense.end)
loss.forward(sigmoid.end)
loss.backward(train_label)
sigmoid.backward(loss.grad)
dense.backward(sigmoid.grad)
print("--------------")
count = 0
for i in range(10000):
inputx = (test_imgs[i] - 128) / 256.0
inputx = inputx.reshape((784, 1))
dense.forward(inputx)
sigmoid.forward(dense.end)
loss.forward(sigmoid.end)
from softmax import Softmax
from data_loader import get_images_and_labels, get_test_images_and_labels
if __name__ == '__main__':
dense = Dense(10, 784)
dense.load_model("./model/w.npy", "./model/b.npy")
dense1 = Dense(10, 100)
sigmoid = Sigmoid()
loss = Softmax()
img, labels = get_images_and_labels()
test_imgs, test_label = get_test_images_and_labels()
train_label = np.zeros([10, 1])
train_label[labels[0]] = 1
inputx = (img[0] - 128) / 256.0
count = 0
for i in range(10000):
inputx = (test_imgs[i] - 128) / 256.0
inputx = inputx.reshape((784, 1))
dense.forward(inputx)
sigmoid.forward(dense.end)
loss.forward(sigmoid.end)
if (loss.end.argmax() == test_label[i]):
count += 1
print("epoch = ", " ", count / 10000)