def loss(self, trg_batch, train):
losses = []
for i in range(len(trg_batch)-1):
y = self.decode_step(trg_batch[i], train)
loss = F.softmax_cross_entropy(y, trg_batch[i+1], 0)
losses.append(loss)
return F.batch.mean(F.sum(losses))
def loss(self, trg_batch, train):
"""Calculates loss values."""
losses = []
for i in range(len(trg_batch) - 1):
y = self.decode_step(trg_batch[i], train)
losses.append(F.softmax_cross_entropy(y, trg_batch[i + 1], 0))
return F.batch.mean(F.sum(losses))
def loss(self, outputs, inputs):
losses = [
F.softmax_cross_entropy(outputs[i], inputs[i + 1], 0)
for i in range(len(outputs))
]
return F.batch.mean(F.sum(losses))
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 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 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)
optimizer = O.SGD(.1)
optimizer.add(pw1, pb1, pw2, 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)
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)
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))