def test_ModelTest_CheckSaveLoad_Insufficient(self):
shape = Shape([2, 2])
values1 = [1, 2, 3, 4]
values2 = [5, 6, 7, 8]
tmp = tempfile.NamedTemporaryFile()
m1 = Model()
m2 = Model()
p1 = Parameter(shape, I.Constant(0))
p1.value += tF.raw_input(shape, values1)
p2 = Parameter(shape, I.Constant(0))
p2.value += tF.raw_input(shape, values2)
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.save(tmp.name)
m1 = Model()
m2 = Model()
p1 = Parameter()
m1.add("p", p1)
m1.add("sm", m2)
with self.assertRaises(RuntimeError):
m1.load(tmp.name)
def main():
with DefaultScopeDevice(CPUDevice()):
pw1 = Parameter("w1", [8, 2], I.XavierUniform())
pb1 = Parameter("b1", [8], I.Constant(0))
pw2 = Parameter("w2", [1, 8], I.XavierUniform())
pb2 = Parameter("b2", [], I.Constant(0))
trainer = T.SGD(0.1)
trainer.add_parameter(pw1)
trainer.add_parameter(pb1)
trainer.add_parameter(pw2)
trainer.add_parameter(pb2)
input_data = np.array(
[
[1, 1], # Sample 1
[1, -1], # Sample 2
[-1, 1], # Sample 3
[-1, -1], # Sample 4
],
dtype=np.float32)
output_data = np.array(
[
1, # Label 1
-1, # Label 2
-1, # Label 3
1, # Label 4
],
dtype=np.float32)
for i in range(100):
g = Graph()
with DefaultScopeGraph(g):
# Builds a computation graph.
#x = F.input(shape=Shape([2], 4), data=input_data)
x = F.input(data=input_data)
w1 = F.input(param=pw1)
b1 = F.input(param=pb1)
w2 = F.input(param=pw2)
b2 = F.input(param=pb2)
h = F.tanh(F.matmul(w1, x) + b1)
y = F.matmul(w2, h) + b2
# Calculates values.
y_val = g.forward(y).to_list()
print("epoch ", i, ":")
for j in range(4):
print(" [", j, "]: ", y_val[j])
#t = F.input(shape=Shape([], 4), data=output_data)
t = F.input(data=output_data)
diff = t - y
loss = F.batch.mean(diff * diff)
loss_val = g.forward(loss).to_list()[0]
print(" loss: ", loss_val)
trainer.reset_gradients()
g.backward(loss)
trainer.update()
def test_ModelTest_CheckSaveLoad_Same(self):
shape = Shape([2, 2])
values1 = [1, 2, 3, 4]
values2 = [5, 6, 7, 8]
tmp = tempfile.NamedTemporaryFile()
m1 = Model()
m2 = Model()
p1 = Parameter(shape, I.Constant(0))
p1.value += tF.raw_input(shape, values1)
p2 = Parameter(shape, I.Constant(0))
p2.value += tF.raw_input(shape, values2)
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.save(tmp.name)
m1 = Model()
m2 = Model()
p1 = Parameter()
p2 = Parameter()
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.load(tmp.name)
self.assertTrue(p1.valid())
self.assertTrue(p2.valid())
self.assertEqual(shape, p1.shape())
self.assertEqual(shape, p2.shape())
self.assertEqual(values1, p1.value.to_list())
self.assertEqual(values2, p2.value.to_list())
def __init__(self, in_size, out_size, trainer):
self.out_size_ = out_size
self.pw_ = Parameter([3 * out_size, in_size], I.Uniform(-0.1, 0.1))
self.pbf_ = Parameter([out_size], I.Constant(0))
self.pbr_ = Parameter([out_size], I.Constant(0))
trainer.add_parameter(self.pw_)
trainer.add_parameter(self.pbf_)
trainer.add_parameter(self.pbr_)
def init(self, src_vocab_size, trg_vocab_size, embed_size, hidden_size):
"""Creates a new AttentionalEncoderDecoder object."""
self.psrc_lookup.init([embed_size, src_vocab_size], I.XavierUniform())
self.ptrg_lookup.init([embed_size, trg_vocab_size], I.XavierUniform())
self.pwhj.init([embed_size, 2 * hidden_size], I.XavierUniform())
self.pbj.init([embed_size], I.Constant(0))
self.pwjy.init([trg_vocab_size, embed_size], I.XavierUniform())
self.pby.init([trg_vocab_size], I.Constant(0))
self.src_fw_lstm.init(embed_size, hidden_size)
self.src_bw_lstm.init(embed_size, hidden_size)
self.trg_lstm.init(2 * embed_size, hidden_size)
def test_model_load_save(self):
submodel = TestModel()
submodel.sp1 = Parameter([2, 4], I.Constant(0))
submodel.sp1.value = tF.input(np.array([[0, 1, 2, 3], [4, 5, 6, 7]]))
submodel.sp2 = Parameter([2, 4], I.Constant(0))
submodel.sp2.value = tF.input(np.array([[9, 8, 7, 6], [5, 4, 3, 2]]))
submodel.add("sp1", submodel.sp1)
submodel.add("sp2", submodel.sp2)
parentmodel = TestModel()
parentmodel.p1 = Parameter([4, 2], I.Constant(0))
parentmodel.p1.value = tF.input(
np.array([[0, 1], [2, 3], [4, 5], [6, 7]]))
parentmodel.p2 = Parameter([4, 2], I.Constant(0))
parentmodel.p2.value = tF.input(
np.array([[9, 8], [7, 6], [5, 4], [3, 2]]))
parentmodel.sub = submodel
parentmodel.add("p1", parentmodel.p1)
parentmodel.add("p2", parentmodel.p2)
parentmodel.add("sub", parentmodel.sub)
submodel_load = TestModel()
submodel_load.sp1 = Parameter()
submodel_load.sp2 = Parameter()
submodel_load.add("sp1", submodel_load.sp1)
submodel_load.add("sp2", submodel_load.sp2)
parentmodel_load = TestModel()
parentmodel_load.p1 = Parameter()
parentmodel_load.p2 = Parameter()
parentmodel_load.sub = submodel_load
parentmodel_load.add("p1", parentmodel_load.p1)
parentmodel_load.add("p2", parentmodel_load.p2)
parentmodel_load.add("sub", parentmodel_load.sub)
with tempfile.NamedTemporaryFile() as fp:
parentmodel.save(fp.name)
parentmodel_load.load(fp.name)
self.assertTrue(
(parentmodel_load.p1.value.to_ndarrays()[0] == np.array([[0, 1],
[2, 3],
[4, 5],
[6, 7]
])).all())
self.assertTrue(
(parentmodel_load.p2.value.to_ndarrays()[0] == np.array([[9, 8],
[7, 6],
[5, 4],
[3, 2]
])).all())
self.assertTrue(
(parentmodel_load.sub.sp1.value.to_ndarrays()[0] == np.array(
[[0, 1, 2, 3], [4, 5, 6, 7]])).all())
self.assertTrue(
(parentmodel_load.sub.sp2.value.to_ndarrays()[0] == np.array(
[[9, 8, 7, 6], [5, 4, 3, 2]])).all())
def init(self, src_vocab_size, trg_vocab_size, embed_size, hidden_size):
self.psrc_lookup_.init([embed_size, src_vocab_size], I.XavierUniform())
self.ptrg_lookup_.init([embed_size, trg_vocab_size], I.XavierUniform())
self.pwfbw_.init([2*hidden_size, hidden_size], I.XavierUniform())
self.pwhw_.init([hidden_size, hidden_size], I.XavierUniform())
self.pwwe_.init([hidden_size], I.XavierUniform())
self.pwhj_.init([embed_size, hidden_size], I.XavierUniform())
self.pbj_.init([embed_size], I.Constant(0))
self.pwjy_.init([trg_vocab_size, embed_size], I.XavierUniform())
self.pby_.init([trg_vocab_size], I.Constant(0))
self.src_fw_lstm_.init(embed_size, hidden_size)
self.src_bw_lstm_.init(embed_size, hidden_size)
self.trg_lstm_.init(embed_size+hidden_size*2, hidden_size)
def test_device_instance(self):
dev = Device.get_default()
self.assertIs(dev, self.device)
tensor = tF.raw_input([], [0])
dev = tensor.device()
self.assertIs(dev, self.device)
node = F.raw_input([], [0])
dev = node.device()
self.assertIs(dev, self.device)
my_device = Naive()
self.assertIsNot(my_device, self.device)
node = F.raw_input([], [0], device=my_device)
dev = node.device()
self.assertIs(dev, my_device)
dev = self.graph.get_device(node)
self.assertIs(dev, my_device)
param = Parameter([], I.Constant(1))
dev = param.device()
self.assertIs(dev, self.device)
def test_Parameter_argument(self):
# shape w/o data
p = Parameter(Shape([2, 3]))
self.assertEqual(p.shape(), Shape([2, 3]))
# shape w/ Initializer
p = Parameter(Shape([4, 3]), I.Constant(1))
self.assertEqual(p.shape(), Shape([4, 3]))
self.assertEqual(p.value.to_list(), [1] * 12)
# shape w/ list[float]
p = Parameter(Shape([4, 3]), self.list_data[:12])
self.assertEqual(p.shape(), Shape([4, 3]))
self.assertEqual(p.value.to_list(), self.list_data[:12])
# ndarray w/o shape
p = Parameter(init=self.ndarray_data[0])
self.assertEqual(p.shape(), Shape([4, 3]))
self.assertEqual(p.value.to_list(), self.list_data[:12])
# ndarray w/ shape
p = Parameter(Shape([2, 6]), init=self.ndarray_data[0])
self.assertEqual(p.shape(), Shape([2, 6]))
self.assertEqual(p.value.to_list(), self.list_data[:12])
# list[float] w/o shape
self.assertRaises(TypeError, lambda: Parameter(init=self.list_data[:12]))
def __init__(self, in_size, out_size, trainer):
self.out_size_ = out_size
self.pwxh_ = Parameter([4 * out_size, in_size], I.Uniform(-0.1, 0.1))
self.pwhh_ = Parameter([4 * out_size, out_size], I.Uniform(-0.1, 0.1))
self.pbh_ = Parameter([4 * out_size], I.Constant(0))
trainer.add_parameter(self.pwxh_)
trainer.add_parameter(self.pwhh_)
trainer.add_parameter(self.pbh_)
def init(self, src_vocab_size, trg_vocab_size, embed_size, hidden_size):
"""Creates a new EncoderDecoder object."""
self.psrc_lookup.init([embed_size, src_vocab_size], I.XavierUniform())
self.ptrg_lookup.init([embed_size, trg_vocab_size], I.XavierUniform())
self.pwhy.init([trg_vocab_size, hidden_size], I.XavierUniform())
self.pby.init([trg_vocab_size], I.Constant(0))
self.src_lstm.init(embed_size, hidden_size)
self.trg_lstm.init(embed_size, hidden_size)
def test_Parameter_argument(self):
# no argument
p = Parameter()
self.assertFalse(p.valid())
# shape w/ Initializer
p = Parameter(Shape([4, 3]), I.Constant(1))
self.assertEqual(p.shape(), Shape([4, 3]))
self.assertEqual(p.value.to_list(), [1] * 12)
def test_ModelTest_CheckSaveLoadWithStats(self):
shape = Shape([2, 2])
values1 = [1, 2, 3, 4]
values2 = [5, 6, 7, 8]
stats1 = [10, 20, 30, 40]
stats2 = [50, 60, 70, 80]
tmp = tempfile.NamedTemporaryFile()
m1 = Model()
m2 = Model()
p1 = Parameter(shape, I.Constant(0))
p1.value += tF.raw_input(shape, values1)
p2 = Parameter(shape, I.Constant(0))
p2.value += tF.raw_input(shape, values2)
p1.add_stats("a", shape)
p2.add_stats("b", shape)
p1.stats["a"].reset_by_vector(stats1);
p2.stats["b"].reset_by_vector(stats2);
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.save(tmp.name)
m1 = Model()
m2 = Model()
p1 = Parameter()
p2 = Parameter()
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.load(tmp.name)
self.assertTrue(p1.valid())
self.assertTrue(p2.valid())
self.assertEqual(shape, p1.shape())
self.assertEqual(shape, p2.shape())
self.assertEqual(values1, p1.value.to_list())
self.assertEqual(values2, p2.value.to_list())
self.assertTrue("a" in p1.stats)
self.assertTrue("b" in p2.stats)
self.assertEqual(stats1, p1.stats["a"].to_list())
self.assertEqual(stats2, p2.stats["b"].to_list())
def __init__(self, name, src_vocab_size, trg_vocab_size, embed_size,
hidden_size, dropout_rate):
self.name_ = name
self.embed_size_ = embed_size
self.dropout_rate_ = dropout_rate
self.psrc_lookup_ = Parameter([embed_size, src_vocab_size],
I.XavierUniform())
self.ptrg_lookup_ = Parameter([embed_size, trg_vocab_size],
I.XavierUniform())
self.pwhj_ = Parameter([embed_size, 2 * hidden_size],
I.XavierUniform())
self.pbj_ = Parameter([embed_size], I.Constant(0))
self.pwjy_ = Parameter([trg_vocab_size, embed_size], I.XavierUniform())
self.pby_ = Parameter([trg_vocab_size], I.Constant(0))
self.src_fw_lstm_ = LSTM(name + "_src_fw_lstm", embed_size,
hidden_size)
self.src_bw_lstm_ = LSTM(name + "_src_bw_lstm", embed_size,
hidden_size)
self.trg_lstm_ = LSTM(name + "_trg_lstm", embed_size * 2, hidden_size)
def train_func(trainer):
dev = D.Naive(12345)
Device.set_default(dev)
g = Graph()
Graph.set_default(g)
pw1 = Parameter([8, 2], I.XavierUniform())
pb1 = Parameter([8], I.Constant(0))
pw2 = Parameter([1, 8], I.XavierUniform())
pb2 = Parameter([1], I.Constant(0))
trainer.add_parameter(pw1)
trainer.add_parameter(pb1)
trainer.add_parameter(pw2)
trainer.add_parameter(pb2)
input_data = [1, 1, 1, -1, -1, 1, -1, -1]
output_data = [1, -1, -1, 1]
for i in range(10):
g.clear()
x = F.input(input_data, Shape([2], 4))
w1 = F.parameter(pw1)
b1 = F.parameter(pb1)
w2 = F.parameter(pw2)
b2 = F.parameter(pb2)
h = F.tanh(w1 @ x + b1)
y = w2 @ h + b2
t = F.input(output_data, Shape([], 4))
diff = t - y
loss = F.batch.mean(diff * diff)
trainer.reset_gradients()
loss.backward()
trainer.update()
return [
pw1.value.to_list(),
pb1.value.to_list(),
pw2.value.to_list(),
pb2.value.to_list()
]
def test_optimizer_add(self):
model = TestModel()
p = Parameter([5], I.Constant(0))
p.gradient = tF.raw_input([5], [1, 2, 3, 4, 5])
optimizer = O.Adam()
optimizer.set_weight_decay(1e-6)
optimizer.set_gradient_clipping(5)
optimizer.add(model)
optimizer.add(p)
self.assertEqual(p.gradient.to_list(), [1, 2, 3, 4, 5])
self.assertEqual(model.param.gradient.to_list(), [1, 2, 3, 4, 5])
optimizer.reset_gradients()
self.assertEqual(p.gradient.to_list(), [0, 0, 0, 0, 0])
self.assertEqual(model.param.gradient.to_list(), [0, 0, 0, 0, 0])
def test_tensor_instance(self):
param = Parameter([], I.Constant(1))
t_origin = param.gradient
t = param.gradient
self.assertIs(t, t_origin)
t = Tensor(t_origin)
self.assertEqual(t.to_list(), t.to_list())
self.assertIsNot(t, t_origin)
t = t_origin
t *= 2
self.assertIs(t, t_origin)
t = t * 2
self.assertIsNot(t, t_origin)
def __init__(self, in_size, out_size):
self.out_size = out_size
self.pw = Parameter([3 * out_size, in_size], I.Uniform(-0.1, 0.1))
self.pbf = Parameter([out_size], I.Constant(0))
self.pbr = Parameter([out_size], I.Constant(0))
self.add_all_parameters()
def __init__(self, name, in_size, out_size):
self.name_ = name
self.out_size_ = out_size
self.pwxh_ = Parameter([4 * out_size, in_size], I.XavierUniform())
self.pwhh_ = Parameter([4 * out_size, out_size], I.XavierUniform())
self.pbh_ = Parameter([4 * out_size], I.Constant(0))
def init(self, in_size, out_size):
"""Creates a new LSTM."""
self._pwxh.init([4 * out_size, in_size], I.XavierUniform())
self._pwhh.init([4 * out_size, out_size], I.XavierUniform())
self._pbh.init([4 * out_size], I.Constant(0))
def __init__(self, in_size, out_size):
self.out_size = out_size
self.pw = Parameter([3 * out_size, in_size], I.Uniform(-0.1, 0.1))
self.pbf = Parameter([out_size], I.Constant(0))
self.pbr = Parameter([out_size], I.Constant(0))
self.scan_attributes()
def main():
# Loads data
train_inputs = load_images("data/train-images-idx3-ubyte", NUM_TRAIN_SAMPLES)
train_labels = load_labels("data/train-labels-idx1-ubyte", NUM_TRAIN_SAMPLES)
test_inputs = load_images("data/t10k-images-idx3-ubyte", NUM_TEST_SAMPLES)
test_labels = load_labels("data/t10k-labels-idx1-ubyte", NUM_TEST_SAMPLES)
# Initializes 2 device objects which manage different GPUs.
dev0 = D.CUDA(0)
dev1 = D.CUDA(1)
# Parameters on GPU 0.
pw1 = Parameter([NUM_HIDDEN_UNITS, NUM_INPUT_UNITS], I.XavierUniform(), dev0)
pb1 = Parameter([NUM_HIDDEN_UNITS], I.Constant(0), dev0)
# Parameters on GPU 1.
pw2 = Parameter([NUM_OUTPUT_UNITS, NUM_HIDDEN_UNITS], I.XavierUniform(), dev1)
pb2 = Parameter([NUM_OUTPUT_UNITS], I.Constant(0), dev1)
trainer = T.SGD(.1)
trainer.add_parameter(pw1)
trainer.add_parameter(pb1)
trainer.add_parameter(pw2)
trainer.add_parameter(pb2)
def make_graph(inputs):
# We first store input values explicitly on GPU 0.
x = F.input(inputs, device=dev0)
w1 = F.parameter(pw1)
b1 = F.parameter(pb1)
w2 = F.parameter(pw2)
b2 = F.parameter(pb2)
# The hidden layer is calculated and implicitly stored on GPU 0.
h_on_gpu0 = F.relu(w1 @ x + b1)
# `copy()` transfers the hiddne layer to GPU 1.
h_on_gpu1 = F.copy(h_on_gpu0, dev1)
# The output layer is calculated and implicitly stored on GPU 1.
return w2 @ h_on_gpu1 + b2
ids = list(range(NUM_TRAIN_SAMPLES))
g = Graph()
Graph.set_default(g)
for epoch in range(MAX_EPOCH):
random.shuffle(ids)
# Training loop
for batch in range(NUM_TRAIN_BATCHES):
print("\rTraining... %d / %d" % (batch + 1, NUM_TRAIN_BATCHES), end="")
inputs = [train_inputs[ids[batch * BATCH_SIZE + i]] for i in range(BATCH_SIZE)]
labels = [train_labels[ids[batch * BATCH_SIZE + i]] for i in range(BATCH_SIZE)]
g.clear()
y = make_graph(inputs)
loss = F.softmax_cross_entropy(y, labels, 0)
avg_loss = F.batch.mean(loss)
trainer.reset_gradients()
avg_loss.backward()
trainer.update()
print()
match = 0
# Test loop
for batch in range(NUM_TEST_BATCHES):
print("\rTesting... %d / %d" % (batch + 1, NUM_TEST_BATCHES), end="")
inputs = [test_inputs[batch * BATCH_SIZE + i] for i in range(BATCH_SIZE)]
g.clear()
y = make_graph(inputs)
y_val = y.to_list()
for i in range(BATCH_SIZE):
maxval = -1e10
argmax = -1
for j in range(NUM_OUTPUT_UNITS):
v = y_val[j + i * NUM_OUTPUT_UNITS]
if (v > maxval):
maxval = v
argmax = j
if argmax == test_labels[i + batch * BATCH_SIZE]:
match += 1
accuracy = 100.0 * match / NUM_TEST_SAMPLES
print("\nepoch %d: accuracy: %.2f%%\n" % (epoch, accuracy))
def __init__(self):
self.param = Parameter([5], I.Constant(0))
self.param.gradient = tF.raw_input([5], [1, 2, 3, 4, 5])
self.scan_attributes()
def main():
dev = D.Naive() # or D.CUDA(gpuid)
Device.set_default(dev)
# Parameters
pw1 = Parameter([8, 2], I.XavierUniform())
pb1 = Parameter([8], I.Constant(0))
pw2 = Parameter([1, 8], I.XavierUniform())
pb2 = Parameter([], I.Constant(0))
# Optimizer
optimizer = O.SGD(0.1)
# Registers parameters.
optimizer.add_parameter(pw1)
optimizer.add_parameter(pb1)
optimizer.add_parameter(pw2)
optimizer.add_parameter(pb2)
# Training data
input_data = [
np.array([1, 1], dtype=np.float32), # Sample 1
np.array([1, -1], dtype=np.float32), # Sample 2
np.array([-1, 1], dtype=np.float32), # Sample 3
np.array([-1, -1], dtype=np.float32), # Sample 4
]
output_data = [
np.array([1], dtype=np.float32), # Label 1
np.array([-1], dtype=np.float32), # Label 2
np.array([-1], dtype=np.float32), # Label 3
np.array([1], dtype=np.float32), # Label 4
]
g = Graph()
Graph.set_default(g)
for i in range(10):
g.clear()
# Builds a computation graph.
x = F.input(input_data)
w1 = F.parameter(pw1)
b1 = F.parameter(pb1)
w2 = F.parameter(pw2)
b2 = F.parameter(pb2)
h = F.tanh(w1 @ x + b1)
y = w2 @ h + b2
# Obtains values.
y_val = y.to_list()
print("epoch ", i, ":")
for j in range(4):
print(" [", j, "]: ", y_val[j])
# Extends the computation graph to calculate loss values.
t = F.input(output_data)
diff = t - y
loss = F.batch.mean(diff * diff)
# Obtains the loss.
loss_val = loss.to_float()
print(" loss: ", loss_val)
# Updates parameters.
optimizer.reset_gradients()
loss.backward()
optimizer.update()
def primitiv_xor_test(self):
dev = D.Naive()
Device.set_default(dev)
g = Graph()
Graph.set_default(g)
input_data = [
np.array([[1], [1]]),
np.array([[-1], [1]]),
np.array([[-1], [-1]]),
np.array([[1], [-1]]),
]
label_data = [
np.array([1]),
np.array([-1]),
np.array([1]),
np.array([-1]),
]
N = 8
pw = Parameter([1, N], I.XavierUniform())
pb = Parameter([], I.Constant(0))
pu = Parameter([N, 2], I.XavierUniform())
pc = Parameter([N], I.Constant(0))
if os.path.isfile('output/xor/pw.data') and os.path.isfile(
'output/xor/pb.data') and os.path.isfile(
'output/xor/pu.data') and os.path.isfile(
'output/xor/pc.data'):
pw.load('output/xor/pw.data')
pb.load('output/xor/pb.data')
pu.load('output/xor/pu.data')
pc.load('output/xor/pc.data')
optimizer = O.SGD(0.01)
optimizer.add(pw, pb, pu, pc)
for epoch in range(1000):
print(epoch, end=' ')
g.clear()
x = F.input(input_data)
w = F.parameter(pw)
b = F.parameter(pb)
u = F.parameter(pu)
c = F.parameter(pc)
h = F.tanh(u @ x + c)
y = F.tanh(w @ h + b)
for val in y.to_list():
print('{:+.6f},'.format(val), end=' ')
loss = self.calc_loss(y, label_data)
print('loss={:.6f}'.format(loss.to_float()))
optimizer.reset_gradients()
loss.backward()
optimizer.update()
pw.save('output/xor/pw.data')
pb.save('output/xor/pb.data')
pu.save('output/xor/pu.data')
pc.save('output/xor/pc.data')
return y.to_list()
def main():
# Loads data
train_inputs = load_images("data/train-images-idx3-ubyte", NUM_TRAIN_SAMPLES)
train_labels = load_labels("data/train-labels-idx1-ubyte", NUM_TRAIN_SAMPLES)
test_inputs = load_images("data/t10k-images-idx3-ubyte", NUM_TEST_SAMPLES)
test_labels = load_labels("data/t10k-labels-idx1-ubyte", NUM_TEST_SAMPLES)
dev = D.Naive() # or D.CUDA(gpuid)
Device.set_default(dev)
pw1 = Parameter([NUM_HIDDEN_UNITS, NUM_INPUT_UNITS], I.XavierUniform())
pb1 = Parameter([NUM_HIDDEN_UNITS], I.Constant(0))
pw2 = Parameter([NUM_OUTPUT_UNITS, NUM_HIDDEN_UNITS], I.XavierUniform())
pb2 = Parameter([NUM_OUTPUT_UNITS], I.Constant(0))
optimizer = O.SGD(.5)
optimizer.add(pw1, pb1, pw2, pb2)
def make_graph(inputs, train):
x = F.input(inputs)
w1 = F.parameter(pw1)
b1 = F.parameter(pb1)
h = F.relu(w1 @ x + b1)
h = F.dropout(h, .5, train)
w2 = F.parameter(pw2)
b2 = F.parameter(pb2)
return w2 @ h + b2
ids = list(range(NUM_TRAIN_SAMPLES))
g = Graph()
Graph.set_default(g)
for epoch in range(MAX_EPOCH):
random.shuffle(ids)
# Training loop
for batch in range(NUM_TRAIN_BATCHES):
print("\rTraining... %d / %d" % (batch + 1, NUM_TRAIN_BATCHES), end="")
inputs = [train_inputs[ids[batch * BATCH_SIZE + i]] for i in range(BATCH_SIZE)]
labels = [train_labels[ids[batch * BATCH_SIZE + i]] for i in range(BATCH_SIZE)]
g.clear()
y = make_graph(inputs, True)
loss = F.softmax_cross_entropy(y, labels, 0)
avg_loss = F.batch.mean(loss)
optimizer.reset_gradients()
avg_loss.backward()
optimizer.update()
print()
match = 0
# Test loop
for batch in range(NUM_TEST_BATCHES):
print("\rTesting... %d / %d" % (batch + 1, NUM_TEST_BATCHES), end="")
inputs = [test_inputs[batch * BATCH_SIZE + i] for i in range(BATCH_SIZE)]
g.clear()
y = make_graph(inputs, False)
y_val = y.to_list()
for i in range(BATCH_SIZE):
maxval = -1e10
argmax = -1
for j in range(NUM_OUTPUT_UNITS):
v = y_val[j + i * NUM_OUTPUT_UNITS]
if (v > maxval):
maxval = v
argmax = j
if argmax == test_labels[i + batch * BATCH_SIZE]:
match += 1
accuracy = 100.0 * match / NUM_TEST_SAMPLES
print("\nepoch %d: accuracy: %.2f%%\n" % (epoch, accuracy))
def setUp(self):
self.dev = D.Naive()
Device.set_default(self.dev)
self.p = Parameter([8], I.Constant(0))
self.p.value.reset_by_vector([1, 2, 3, 4, 5, 6, 7, 8])
def main():
# Loads data
train_inputs = load_images("data/train-images-idx3-ubyte",
NUM_TRAIN_SAMPLES)
train_labels = load_labels("data/train-labels-idx1-ubyte",
NUM_TRAIN_SAMPLES)
test_inputs = load_images("data/t10k-images-idx3-ubyte", NUM_TEST_SAMPLES)
test_labels = load_labels("data/t10k-labels-idx1-ubyte", NUM_TEST_SAMPLES)
dev = D.CUDA(0)
Device.set_default(dev)
g = Graph()
Graph.set_default(g)
# Parameters of CNNs
# Shape: {kernel_height, kernel_width, in_channels, out_channels}
pw_cnn1 = Parameter(Shape([KERNEL_SIZE1, KERNEL_SIZE1, 1, NUM_CHANNELS1]),
I.XavierUniformConv2D())
pw_cnn2 = Parameter(
Shape([KERNEL_SIZE2, KERNEL_SIZE2, NUM_CHANNELS1, NUM_CHANNELS2]),
I.XavierUniformConv2D())
# Parameters of FC layers
pw_fc1 = Parameter(Shape([NUM_HIDDEN_UNITS, NUM_INPUT_UNITS]),
I.XavierUniform())
pw_fc2 = Parameter(Shape([NUM_OUTPUT_UNITS, NUM_HIDDEN_UNITS]),
I.XavierUniform())
pb_fc1 = Parameter(Shape([NUM_HIDDEN_UNITS]), I.Constant(0))
pb_fc2 = Parameter(Shape([NUM_OUTPUT_UNITS]), I.Constant(0))
# Optimizer
optimizer = O.SGD(.1)
optimizer.add(pw_cnn1, pw_cnn2, pw_fc1, pw_fc2, pb_fc1, pb_fc2)
# Helper lambda to construct the predictor network.
def make_graph(inputs, train):
# Input and parameters.
#x = F.input(Shape([IMAGE_HEIGHT, IMAGE_WIDTH], BATCH_SIZE), inputs)
x = F.input(inputs)
w_cnn1 = F.parameter(pw_cnn1)
w_cnn2 = F.parameter(pw_cnn2)
w_fc1 = F.parameter(pw_fc1)
w_fc2 = F.parameter(pw_fc2)
b_fc1 = F.parameter(pb_fc1)
b_fc2 = F.parameter(pb_fc2)
# CNNs
h_cnn1 = F.relu(F.conv2d(x, w_cnn1, PADDING1, PADDING1, 1, 1, 1, 1))
h_pool1 = F.max_pool2d(h_cnn1, 2, 2, 0, 0, 2, 2)
h_cnn2 = F.relu(
F.conv2d(h_pool1, w_cnn2, PADDING2, PADDING2, 1, 1, 1, 1))
h_pool2 = F.max_pool2d(h_cnn2, 2, 2, 0, 0, 2, 2)
# FC layers
x_fc = F.dropout(F.flatten(h_pool2), .5, train)
h_fc = F.dropout(F.relu(F.matmul(w_fc1, x_fc) + b_fc1), .5, train)
return F.matmul(w_fc2, h_fc) + b_fc2
# Batch randomizer
ids = list(range(NUM_TRAIN_SAMPLES))
for epoch in range(MAX_EPOCH):
# Shuffles sample IDs.
random.shuffle(ids)
# Training loop
for batch in range(NUM_TRAIN_BATCHES):
print("\rTraining... %d / %d" % (batch + 1, NUM_TRAIN_BATCHES),
end="")
# Makes a minibatch for training.
inputs = [
train_inputs[ids[batch * BATCH_SIZE + i]]
for i in range(BATCH_SIZE)
]
labels = [
train_labels[ids[batch * BATCH_SIZE + i]]
for i in range(BATCH_SIZE)
]
# Constructs the graph.
g.clear()
y = make_graph(inputs, True)
loss = F.softmax_cross_entropy(y, labels, 0)
avg_loss = F.batch.mean(loss)
# Dump computation graph at the first time.
# if epoch == 0 and batch == 0:
# print(g.dump("dot"))
# Implicit forward, backward, and updates parameters.
optimizer.reset_gradients()
avg_loss.backward()
optimizer.update()
print()
match = 0
# Test loop
for batch in range(NUM_TEST_BATCHES):
print("\rTesting... %d / %d" % (batch + 1, NUM_TEST_BATCHES),
end="")
# Makes a test minibatch.
inputs = [
test_inputs[batch * BATCH_SIZE + i] for i in range(BATCH_SIZE)
]
# Constructs the graph.
g.clear()
y = make_graph(inputs, False)
# Gets outputs, argmax, and compares them with the label.
y_val = y.to_list()
for i in range(BATCH_SIZE):
maxval = -1e10
argmax = -1
for j in range(NUM_OUTPUT_UNITS):
v = y_val[j + i * NUM_OUTPUT_UNITS]
if v > maxval:
maxval = v
argmax = j
if argmax == test_labels[i + batch * BATCH_SIZE]:
match += 1
accuracy = 100.0 * match / NUM_TEST_SAMPLES
print("epoch %d: accuracy: %.2f%%" % (epoch, accuracy))
return 0
def init(self, d_model):
self.pgain.init([1, d_model], I.Constant(1))
self.pbias.init([1, d_model], I.Constant(0))
