def main():
"""
main func
"""
text, x, y, char2idx, idx2char = getty()
T = 100
config = {
'dim_hidden': 300,
'l': T,
'clip': 5,
'mu': 0.9,
'step_size': 0.001
}
#np.random.seed(42)
r = RNN(config)
r.accept([27])
ttb = r.sample('f', char2idx, idx2char)
r.fit(x[:T], y[:T], 100, char2idx, idx2char)
tta = r.sample('f', char2idx, idx2char)
print(ttb)
print(tta)
print(text[:T])
return
(data, label) = cifar()
N = 10000
data = np.array(data, dtype=float)[:N, ]
label = np.array(label)[:N, ]
data = normalize(data)
config = {
'input_shape': [3, 32, 32],
'mu': 0.9,
'step_size': 0.000001,
'step_decay': 0.95
}
nn = Net(config)
conv1 = Conv([3, 3], 6)
relu1 = Relu()
conv2 = Conv([3, 3], 32)
relu2 = Relu()
pool = MaxPool()
fc = FC([10])
nn.add(conv1)
nn.add(relu1)
nn.add(pool)
nn.add(fc)
print(nn)
nn.fit(data, label, 200)
def test_backward(self):
l = MaxPool()
l.accept([2, 4, 4])
x = np.arange(32).reshape(1, 32)
y = l.forward(x)
dy = np.array([[[[1, 1], [1, 1]], [[2, 2], [2, 2]]]])
dx = l.backward(dy)
self.assertTrue(
np.allclose(dx, [[
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
2, 0, 2, 0, 0, 0, 0, 0, 2, 0, 2
]]))
import cv2
'''
input_img = np.array([[0,0,0,0,0,0], [0,0,50,0,29,0],
[0,0,80,31,2,0], [0,33,90,0,75,0],
[0,0,9,0,95,0], [0,0,0,0,0,0]
])
'''
train_images = mnist.train_images()[:1000]
train_labels = mnist.train_labels()[:1000]
test_images = mnist.test_images()[:1000] # test image size: 28 x 28
test_labels = mnist.test_labels()[:1000]
conv = Conv(8) # 28 x 28 -> 26 x 26 x 8
pool = MaxPool() # 26 x 26 x 8 -> 13 x 13 x 8
softmax = Softmax(13*13*8,10) # 10 nodes for 10 digits 0 -> 9
def forward(images,labels):
# transform image from [0->255] to [-0.5->0.5]
out = conv.forward((images / 255)-0.5)
out = pool.forward(out)
out = softmax.forward(out)
loss = -np.log(out[labels])
if np.argmax(out) == labels:
acc = 1
else:
acc = 0
return out,loss,acc
def test_repr(self):
l = MaxPool()
def test_forward(self):
l = MaxPool()
l.accept([2, 4, 4])
x = np.arange(32).reshape(1, 2, 4, 4)
y = l.forward(x)
self.assertTrue(np.allclose(y, [5, 7, 13, 15, 21, 23, 29, 31]))
def test_accept(self):
l = MaxPool((2, 2))
self.assertTrue(l.accept([1, 10, 10]))
pass
def test_init(self):
l = MaxPool((2, 2))
pass