Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Refine fluid vgg16 #53

Merged
merged 1 commit into from
Jan 15, 2018
Merged

Refine fluid vgg16 #53

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 7 additions & 4 deletions fluid/mnist.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -149,10 +149,13 @@ def run_benchmark(model, args):
y_data = y_data.reshape([len(y_data), 1])

start = time.time()
outs = exe.run(fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics)
outs = exe.run(
fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics
) # The accuracy is the accumulation of batches, but not the current batch.

end = time.time()
loss = np.array(outs[0])
acc = np.array(outs[1])
Expand Down
50 changes: 28 additions & 22 deletions fluid/vgg16.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -81,27 +81,35 @@ def main():
else:
data_shape = [224, 224, 3]

# Input data
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')

# Train program
net = vgg16_bn_drop(images)
predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)

optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
opts = optimizer.minimize(avg_cost)

# Evaluator
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)

# inference program
inference_program = fluid.default_main_program().clone()
with fluid.program_guard(inference_program):
test_accuracy = fluid.evaluator.Accuracy(
input=predict, label=label, main_program=inference_program)
test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
inference_program = fluid.io.get_inference_program(
test_target, main_program=inference_program)
test_target = accuracy.metrics + accuracy.states
inference_program = fluid.io.get_inference_program(test_target)

# Optimization
optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
opts = optimizer.minimize(avg_cost)

# Initialize executor
place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
exe = fluid.Executor(place)

# Parameter initialization
exe.run(fluid.default_startup_program())

# data reader
train_reader = paddle.batch(
Expand All @@ -117,7 +125,7 @@ def main():

# test
def test(exe):
test_accuracy.reset(exe)
accuracy.reset(exe)
for batch_id, data in enumerate(test_reader()):
img_data = np.array(map(lambda x: x[0].reshape(data_shape),
data)).astype("float32")
Expand All @@ -126,15 +134,9 @@ def test(exe):

exe.run(inference_program,
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + test_accuracy.metrics)

return test_accuracy.eval(exe)
"label": y_data})

place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
exe = fluid.Executor(place)

exe.run(fluid.default_startup_program())
return accuracy.eval(exe)

iters = 0
for pass_id in range(args.num_passes):
Expand All @@ -154,14 +156,18 @@ def test(exe):
fetch_list=[avg_cost] + accuracy.metrics)
iters += 1
num_samples += len(data)
print("Pass = %d, Iters = %d, Loss = %f, Accuracy = %f" %
(pass_id, iters, loss, acc))
pass_elapsed = time.time() - start_time
print(
"Pass = %d, Iters = %d, Loss = %f, Accuracy = %f" %
(pass_id, iters, loss, acc)
) # The accuracy is the accumulation of batches, but not the current batch.

pass_elapsed = time.time() - start_time
pass_train_acc = accuracy.eval(exe)
pass_test_acc = test(exe)
print(
"Pass = %d, Training performance = %f imgs/s, Test accuracy = %f\n"
% (pass_id, num_samples / pass_elapsed, pass_test_acc))
"Pass = %d, Training performance = %f imgs/s, Train accuracy = %f, Test accuracy = %f\n"
% (pass_id, num_samples / pass_elapsed, pass_train_acc,
pass_test_acc))


def print_arguments():
Expand Down