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未验证 提交 7af5cb9b 编写于 作者: Z Zhou Wei 提交者： GitHub
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fix english doc of all lr_scheduler (#26619) 

* fix doc of lr_scheduler

* fix doc
上级 286eca2d
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Showing with 94 addition and 106 deletion
python/paddle/optimizer/lr_scheduler.py
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......@@ -153,7 +153,7 @@ class NoamLR(_LRScheduler):
warmup_steps(int): The number of warmup steps. A super parameter. It is a python int number
learning_rate (float): The initial learning rate. It is a python float number. Default: 1.0.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``NoamLR`` instance to schedule learning rate.
......@@ -168,14 +168,14 @@ class NoamLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.NoamLR(d_model=0.01, warmup_steps=100, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.NoamLR(d_model=0.01, warmup_steps=100, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
......@@ -185,14 +185,13 @@ class NoamLR(_LRScheduler):
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.NoamLR(d_model=0.01, warmup_steps=100, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.NoamLR(d_model=0.01, warmup_steps=100, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -204,7 +203,7 @@ class NoamLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -251,7 +250,7 @@ class PiecewiseLR(_LRScheduler):
values(list): A list of learning rate values that will be picked during different epoch boundaries.
The type of element in the list is python float.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``PiecewiseLR`` instance to schedule learning rate.
......@@ -267,14 +266,14 @@ class PiecewiseLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.PiecewiseLR(boundaries=[3, 6, 9], values=[0.1, 0.2, 0.3, 0.4], verbose=True)
scheduler = paddle.optimizer.lr_scheduler.PiecewiseLR(boundaries=[3, 6, 9], values=[0.1, 0.2, 0.3, 0.4], verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
......@@ -284,14 +283,13 @@ class PiecewiseLR(_LRScheduler):
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.PiecewiseLR(boundaries=[3, 6, 9], values=[0.1, 0.2, 0.3, 0.4], verbose=True)
scheduler = paddle.optimizer.lr_scheduler.PiecewiseLR(boundaries=[3, 6, 9], values=[0.1, 0.2, 0.3, 0.4], verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -303,7 +301,7 @@ class PiecewiseLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -336,7 +334,7 @@ class NaturalExpLR(_LRScheduler):
learning_rate (float): The initial learning rate. It is a python float number.
gamma (float, optional): A Ratio to update the learning rate. Default: 0.1.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``NaturalExpLR`` instance to schedule learning rate.
......@@ -352,14 +350,14 @@ class NaturalExpLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.NaturalExpLR(learning_rate=0.5, gamma=0.1, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.NaturalExpLR(learning_rate=0.5, gamma=0.1, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
......@@ -369,14 +367,13 @@ class NaturalExpLR(_LRScheduler):
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.NaturalExpLR(learning_rate=0.5, gamma=0.1, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.NaturalExpLR(learning_rate=0.5, gamma=0.1, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -388,7 +385,7 @@ class NaturalExpLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -416,7 +413,7 @@ class InverseTimeLR(_LRScheduler):
gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` .
It should be less than 1.0. Default: 0.1.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``InverseTimeLR`` instance to schedule learning rate.
......@@ -432,14 +429,14 @@ class InverseTimeLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.InverseTimeLR(learning_rate=0.5, gamma=0.1, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.InverseTimeLR(learning_rate=0.5, gamma=0.1, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
......@@ -449,14 +446,13 @@ class InverseTimeLR(_LRScheduler):
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.InverseTimeLR(learning_rate=0.5, gamma=0.1, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.InverseTimeLR(learning_rate=0.5, gamma=0.1, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -468,7 +464,7 @@ class InverseTimeLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -513,7 +509,7 @@ class PolynomialLR(_LRScheduler):
cycle(bool, optional): Whether the learning rate rises again. If True, then the learning rate will rise when it decrease
to ``end_lr`` . If False, the learning rate is monotone decreasing. Default: False.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``PolynomialLR`` instance to schedule learning rate.
......@@ -529,31 +525,30 @@ class PolynomialLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.PolynomialLR(learning_rate=0.5, decay_steps=20, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.PolynomialLR(learning_rate=0.5, decay_steps=20, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
# train on statich mode
# train on static mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.PolynomialLR(learning_rate=0.5, decay_steps=20, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.PolynomialLR(learning_rate=0.5, decay_steps=20, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -565,7 +560,7 @@ class PolynomialLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -629,7 +624,7 @@ class LinearLrWarmup(_LRScheduler):
start_lr (float): Initial learning rate of warm up.
end_lr (float): Final learning rate of warm up.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``LinearLrWarmup`` instance to schedule learning rate.
......@@ -653,25 +648,24 @@ class LinearLrWarmup(_LRScheduler):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
# train on statich mode
# train on static mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.LinearLrWarmup(
scheduler = paddle.optimizer.lr_scheduler.LinearLrWarmup(
learning_rate=0.5, warmup_steps=20, start_lr=0, end_lr=0.5, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -683,7 +677,7 @@ class LinearLrWarmup(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -733,10 +727,10 @@ class ExponentialLR(_LRScheduler):
Args:
learning_rate (float): The initial learning rate. It is a python float number.
gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` .
It should be less than 1.0. Default: 0.1.
gamma (float): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` .
It should be less than 1.0.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``ExponentialLR`` instance to schedule learning rate.
......@@ -752,31 +746,30 @@ class ExponentialLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.ExponentialLR(learning_rate=0.5, gamma=0.9, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.ExponentialLR(learning_rate=0.5, gamma=0.9, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
# train on statich mode
# train on static mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.ExponentialLR(learning_rate=0.5, gamma=0.9, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.ExponentialLR(learning_rate=0.5, gamma=0.9, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -788,7 +781,7 @@ class ExponentialLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -824,7 +817,7 @@ class MultiStepLR(_LRScheduler):
gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` .
It should be less than 1.0. Default: 0.1.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
......@@ -841,31 +834,30 @@ class MultiStepLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.MultiStepLR(learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.MultiStepLR(learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
# train on statich mode
# train on static mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.MultiStepLR(learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.MultiStepLR(learning_rate=0.5, milestones=[2, 4, 6], gamma=0.8, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -877,7 +869,7 @@ class MultiStepLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -934,7 +926,7 @@ class StepLR(_LRScheduler):
gamma (float, optional): The Ratio that the learning rate will be reduced. ``new_lr = origin_lr * gamma`` .
It should be less than 1.0. Default: 0.1.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``StepLR`` instance to schedule learning rate.
......@@ -951,31 +943,30 @@ class StepLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.StepLR(learning_rate=0.5, step_size=5, gamma=0.8, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.StepLR(learning_rate=0.5, step_size=5, gamma=0.8, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
# train on statich mode
# train on static mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.StepLR(learning_rate=0.5, step_size=5, gamma=0.8, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.StepLR(learning_rate=0.5, step_size=5, gamma=0.8, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -987,7 +978,7 @@ class StepLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -1032,7 +1023,7 @@ class LambdaLR(_LRScheduler):
learning_rate (float): The initial learning rate. It is a python float number.
lr_lambda (function): A function which computes a factor by ``epoch`` , and then multiply the initial learning rate by this factor.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``LambdaLR`` instance to schedule learning rate.
......@@ -1048,31 +1039,30 @@ class LambdaLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.LambdaLR(learning_rate=0.5, lr_lambda=lambda x:0.95**x, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.LambdaLR(learning_rate=0.5, lr_lambda=lambda x:0.95**x, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
# train on statich mode
# train on static mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.LambdaLR(learning_rate=0.5, lr_lambda=lambda x:0.95**x, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.LambdaLR(learning_rate=0.5, lr_lambda=lambda x:0.95**x, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -1084,7 +1074,7 @@ class LambdaLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......@@ -1130,8 +1120,8 @@ class ReduceLROnPlateau(_LRScheduler):
change of ``loss`` is ``threshold`` . Default: ``'rel'`` .
cooldown (int, optional): The number of epochs to wait before resuming normal operation. Default: 0.
min_lr (float, optional): The lower bound of the learning rate after reduction. Default: 0.
epsilon (float, optional): Minimal decay applied to lr. If the difference between new and old lr is smaller than eps, the update is
ignored. Default: 1e-8.
epsilon (float, optional): Minimal decay applied to lr. If the difference between new and old lr is smaller than epsilon,
the update is ignored. Default: 1e-8.
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False``.
......@@ -1149,31 +1139,30 @@ class ReduceLROnPlateau(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.ReduceLROnPlateau(learning_rate=1.0, factor=0.5, patience=5, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.ReduceLROnPlateau(learning_rate=1.0, factor=0.5, patience=5, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step(loss)
# train on statich mode
# train on static mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.ReduceLROnPlateau(learning_rate=1.0, factor=0.5, patience=5, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.ReduceLROnPlateau(learning_rate=1.0, factor=0.5, patience=5, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -1185,7 +1174,7 @@ class ReduceLROnPlateau(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step(out[0])
"""
......@@ -1351,7 +1340,7 @@ class CosineAnnealingLR(_LRScheduler):
T_max (int): Maximum number of iterations. It is half of the decay cycle of learning rate.
eta_min (float|int, optional): Minimum learning rate, that is :math:`\eta_{min}` . Default: 0.
last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
verbose (bool): If ``True``, prints a message to stdout for each update. Default: ``False`` .
verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
Returns:
``CosineAnnealingLR`` instance to schedule learning rate.
......@@ -1367,31 +1356,30 @@ class CosineAnnealingLR(_LRScheduler):
paddle.disable_static()
x = np.random.uniform(-1, 1, [10, 10]).astype("float32")
linear = paddle.nn.Linear(10, 10)
scheduler = paddle.optimizer.CosineAnnealingLR(learning_rate=0.5, T_max=10, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.CosineAnnealingLR(learning_rate=0.5, T_max=10, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler, parameter_list=linear.parameters())
for epoch in range(20):
for batch_id in range(2):
x = paddle.to_tensor(x)
out = linear(x)
loss = paddle.reduce_mean(out)
out.backward()
loss.backward()
sgd.minimize(loss)
linear.clear_gradients()
scheduler.step()
# train on statich mode
# train on static mode
paddle.enable_static()
main_prog = paddle.static.Program()
start_prog = paddle.static.Program()
with paddle.static.program_guard(main_prog, start_prog):
x = paddle.static.data(name='x', shape=[-1, 4, 5])
y = paddle.static.data(name='y', shape=[-1, 4, 5])
x = paddle.static.data(name='x', shape=[None, 4, 5])
y = paddle.static.data(name='y', shape=[None, 4, 5])
z = paddle.static.nn.fc(x, 100)
loss = paddle.mean(z)
scheduler = paddle.optimizer.CosineAnnealingLR(learning_rate=0.5, T_max=10, verbose=True)
scheduler = paddle.optimizer.lr_scheduler.CosineAnnealingLR(learning_rate=0.5, T_max=10, verbose=True)
sgd = paddle.optimizer.SGD(learning_rate=scheduler)
sgd.minimize(loss)
lr_var = sgd._global_learning_rate()
exe = paddle.static.Executor()
exe.run(start_prog)
......@@ -1403,7 +1391,7 @@ class CosineAnnealingLR(_LRScheduler):
'x': np.random.randn(3, 4, 5).astype('float32'),
'y': np.random.randn(3, 4, 5).astype('float32')
},
fetch_list=lr_var.name)
fetch_list=loss.name)
scheduler.step()
"""
......
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