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luotao1reyoung
luotao1
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reyoung
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refine sparse momentum api and unittest (#126)
* refine sparse momentum api and unittest * fix unittests bug
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‎doc/algorithm/rnn/rnn.rst

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@@ -142,6 +142,7 @@ We also project the encoder vector to :code:`decoder_size` dimensional space, ge
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The decoder uses :code:`recurrent_group` to define the recurrent neural network. The step and output functions are defined in :code:`gru_decoder_with_attention`:
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.. code-block:: python
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group_inputs=[StaticInput(input=encoded_vector,is_seq=True),
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StaticInput(input=encoded_proj,is_seq=True)]
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trg_embedding = embedding_layer(

‎doc/ui/api/trainer_config_helpers/optimizers.rst

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:members: BaseSGDOptimizer
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:noindex:
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MomentumOptimizer
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=================
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.. automodule:: paddle.trainer_config_helpers.optimizers
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:members: MomentumOptimizer
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:noindex:
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AdamOptimizer
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=============
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.. automodule:: paddle.trainer_config_helpers.optimizers

‎paddle/trainer/tests/CMakeLists.txt

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@@ -47,7 +47,7 @@ add_test(NAME test_CompareTwoOpts
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COMMAND ${PROJ_ROOT}/paddle/.set_python_path.sh -d ${PROJ_ROOT}/python/
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${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoOpts
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--config_file_a=trainer/tests/sample_trainer_config_opt_a.conf --config_file_b=trainer/tests/sample_trainer_config_opt_b.conf
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--num_passes=1 --need_high_accuracy=1
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--num_passes=1 --need_high_accuracy=0
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WORKING_DIRECTORY ${PROJ_ROOT}/paddle/)
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################# test_CompareSparse ##################

‎paddle/trainer/tests/mnist.list

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trainer/tests/mnist_bin_part

‎paddle/trainer/tests/mnist_bin_part

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‎paddle/trainer/tests/sample_trainer_config_opt_a.conf

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# See the License for the specific language governing permissions and
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# limitations under the License.
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#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
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from paddle.trainer_config_helpers import *
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################################### Data Configuration ###################################
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TrainData(ProtoData(files = "train.list"))
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TrainData(ProtoData(files = "trainer/tests/mnist.list"))
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################################### Algorithm Configuration ###################################
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Settings(
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learning_rate_decay_a = 0.0,
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learning_rate_decay_b = 0.0,
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learning_rate = 1e-03,
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batch_size = 1000,
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algorithm = 'sgd',
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num_batches_per_send_parameter = 1,
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num_batches_per_get_parameter = 1,
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learning_method='sparse_momentum',
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)
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default_momentum(0.5)
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settings(batch_size = 1000,
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learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
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################################### Network Configuration ###################################
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Layer(type = "data", name = "input", size = 784)
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Layer(inputs = [Input("input", parameter_name = "_layer1.w")], name = "layer1", bias = Bias(parameter_name = "_layer1.bias"), active_type = "sigmoid", type = "fc", size = 800)
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Layer(inputs = [Input("layer1", parameter_name = "_layer2.w")], name = "layer2", bias = Bias(parameter_name = "_layer2.bias"), active_type = "sigmoid", type = "fc", size = 800)
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#Layer(inputs = [Input("layer2", parameter_name = "_layer_output.w", decay_rate = 0.02)], name = "output", bias = Bias(parameter_name = "_layer_output.bias"), active_type = "margin", type = "fc", size = 10)
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#Layer(inputs = [Input("layer2", parameter_name = "_layer_output.w", decay_rate = 0.02)], name = "output", bias = Bias(parameter_name = "_layer_output.bias"), type = "fc", size = 10)
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Layer(inputs = [Input("layer2", parameter_name = "_layer_output.w")], name = "output", bias = Bias(parameter_name = "_layer_output.bias"), active_type = "softmax", type = "fc", size = 10)
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Layer(type = "data", name = "label", size = 1)
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Layer(inputs = [Input("output"), Input("label")], type = "multi-class-cross-entropy", name = "cost")
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#Layer(inputs = [Input("output"), Input("label")], type = "huber", name = "cost")
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Evaluator(inputs=["output", "label"], type = "classification_error", name = "classification_error")
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Inputs("input", "label")
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Outputs("cost")
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data = data_layer(name ="input", size=784)
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fc1 = fc_layer(input=data, size=800,
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bias_attr=True,
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act=SigmoidActivation())
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fc2 = fc_layer(input=fc1, size=800,
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bias_attr=True,
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act=SigmoidActivation())
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output = fc_layer(input=[fc1, fc2], size=10,
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bias_attr=True,
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act=SoftmaxActivation())
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lbl = data_layer(name ="label", size=1)
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cost = classification_cost(input=output, label=lbl)
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outputs(cost)

‎paddle/trainer/tests/sample_trainer_config_opt_b.conf

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# See the License for the specific language governing permissions and
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# limitations under the License.
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#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
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from paddle.trainer_config_helpers import *
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################################### Data Configuration ###################################
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TrainData(ProtoData(files = "train.list"))
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TrainData(ProtoData(files = "trainer/tests/mnist.list"))
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################################### Algorithm Configuration ###################################
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Settings(
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learning_rate_decay_a = 0.0,
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learning_rate_decay_b = 0.0,
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learning_rate = 1e-03,
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batch_size = 1000,
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algorithm = 'sgd',
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num_batches_per_send_parameter = 1,
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num_batches_per_get_parameter = 1,
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learning_method='momentum',
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)
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default_momentum(0.5)
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settings(batch_size = 1000,
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learning_method = MomentumOptimizer(momentum=0.5, sparse=False))
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################################### Network Configuration ###################################
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Layer(type = "data", name = "input", size = 784)
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Layer(inputs = [Input("input", parameter_name = "_layer1.w")], name = "layer1", bias = Bias(parameter_name = "_layer1.bias"), active_type = "sigmoid", type = "fc", size = 800)
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Layer(inputs = [Input("layer1", parameter_name = "_layer2.w")], name = "layer2", bias = Bias(parameter_name = "_layer2.bias"), active_type = "sigmoid", type = "fc", size = 800)
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#Layer(inputs = [Input("layer2", parameter_name = "_layer_output.w", decay_rate = 0.02)], name = "output", bias = Bias(parameter_name = "_layer_output.bias"), active_type = "margin", type = "fc", size = 10)
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#Layer(inputs = [Input("layer2", parameter_name = "_layer_output.w", decay_rate = 0.02)], name = "output", bias = Bias(parameter_name = "_layer_output.bias"), type = "fc", size = 10)
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Layer(inputs = [Input("layer2", parameter_name = "_layer_output.w")], name = "output", bias = Bias(parameter_name = "_layer_output.bias"), active_type = "softmax", type = "fc", size = 10)
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Layer(type = "data", name = "label", size = 1)
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Layer(inputs = [Input("output"), Input("label")], type = "multi-class-cross-entropy", name = "cost")
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#Layer(inputs = [Input("output"), Input("label")], type = "huber", name = "cost")
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Evaluator(inputs=["output", "label"], type = "classification_error", name = "classification_error")
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Inputs("input", "label")
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Outputs("cost")
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data = data_layer(name ="input", size=784)
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fc1 = fc_layer(input=data, size=800,
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bias_attr=True,
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act=SigmoidActivation())
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fc2 = fc_layer(input=fc1, size=800,
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bias_attr=True,
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act=SigmoidActivation())
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output = fc_layer(input=[fc1, fc2], size=10,
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bias_attr=True,
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act=SoftmaxActivation())
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lbl = data_layer(name ="label", size=1)
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cost = classification_cost(input=output, label=lbl)
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outputs(cost)

‎python/paddle/trainer_config_helpers/optimizers.py

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class MomentumOptimizer(BaseSGDOptimizer):
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"""
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MomentumOptimizer.
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When sparse=True, the update scheme:
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.. math::
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\\alpha_t &= \\alpha_{t-1} / k \\\\
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\\beta_t &= \\beta_{t-1} / (1 + \\lambda \\gamma_t) \\\\
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u_t &= u_{t-1} - \\alpha_t \\gamma_t g_t \\\\
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v_t &= v_{t-1} + \\tau_{t-1} \\alpha_t \\gamma_t g_t \\\\
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\\tau_t &= \\tau_{t-1} + \\beta_t / \\alpha_t
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where :math:`k` is momentum, :math:`\\lambda` is decay rate,
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:math:`\\gamma_t` is learning rate at the t'th step.
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:param sparse: with sparse support or not.
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:type sparse: bool
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"""
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def extra_settings(self):
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default_momentum(self.momentum)
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def to_setting_kwargs(self):
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return {
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'learning_method': 'momentum'
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}
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if self.sparse:
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return {
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'learning_method': 'sparse_momentum'
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}
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else:
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return {
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'learning_method': 'momentum'
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}
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def __init__(self, momentum=None):
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def __init__(self, momentum=None, sparse=False):
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self.momentum = momentum
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self.sparse = sparse
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class AdamOptimizer(BaseSGDOptimizer):

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