remove reader requirement from test and predict actions and update mnist example
This commit is contained in:
jeanfad
Willi Richert
Родитель
fefbbf9e64
Коммит
6b1438edc7
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@ -103,12 +103,6 @@ class AbstractContext(object, metaclass=ABCMeta):
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import shutil
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shutil.rmtree(self.directory)
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def to_config(self):
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'''
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Generates the CNTK configuration for the root node.
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'''
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return self.root_node.to_config()
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def _generate_train_config(self, optimizer, reader, override_existing):
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'''
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Generates the configuration file for the train action.
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@ -128,7 +122,7 @@ class AbstractContext(object, metaclass=ABCMeta):
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tmpl = open(CNTK_TRAIN_TEMPLATE_PATH, "r").read()
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model_filename = os.path.join(model_dir, self.name)
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description, has_inputs, readers = self.to_config()
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description, has_inputs, readers = self.root_node.to_config()
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if reader:
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readers.append(reader)
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@ -147,10 +141,18 @@ class AbstractContext(object, metaclass=ABCMeta):
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'''
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tmpl = open(CNTK_TEST_TEMPLATE_PATH, "r").read()
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model_filename = os.path.join(self.directory, 'Models', self.name)
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# if no reader is passed generate the reader from the network
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if reader:
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reader_config = reader.generate_config()
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else:
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description, has_inputs, readers = self.root_node.to_config()
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reader_config = '\n'.join(r.generate_config() for r in readers)
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tmpl_dict = {
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'DevideId': self.device_id,
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'ModelPath': model_filename,
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'Reader': reader.generate_config(),
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'Reader': reader_config,
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}
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return tmpl % tmpl_dict
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@ -162,11 +164,19 @@ class AbstractContext(object, metaclass=ABCMeta):
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tmpl = open(CNTK_PREDICT_TEMPLATE_PATH, "r").read()
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model_filename = os.path.join(self.directory, 'Models', self.name)
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output_filename_base = os.path.join(self.directory, 'Outputs', self.name)
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# if no reader is passed generate the reader from the network
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if reader:
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reader_config = reader.generate_config()
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else:
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description, has_inputs, readers = self.root_node.to_config()
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reader_config = '\n'.join(r.generate_config() for r in readers)
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tmpl_dict = {
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'DevideId': self.device_id,
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'ModelPath': model_filename,
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'PredictOutputFile': output_filename_base,
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'Reader': reader.generate_config(),
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'Reader': reader_config,
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}
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return tmpl % tmpl_dict
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@ -204,7 +214,7 @@ class AbstractContext(object, metaclass=ABCMeta):
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return tmpl % tmpl_dict
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@abstractmethod
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def train(self, reader=None):
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def train(self, optimizer, reader=None, override_existing = True):
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'''
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Abstract method for the action train.
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:param reader: the reader to use for this action. Alternatively, you
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@ -288,7 +298,7 @@ class Context(AbstractContext):
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:param override_existing: if the folder exists already override it
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'''
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config_content = self._generate_train_config(optimizer, reader, override_existing)
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output = self._call_cntk(CNTK_TRAIN_CONFIG_FILENAME, config_content)
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return self._call_cntk(CNTK_TRAIN_CONFIG_FILENAME, config_content)
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def test(self, reader=None):
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'''
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@ -297,7 +307,7 @@ class Context(AbstractContext):
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can attach a reader directly to the input node.
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'''
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config_content = self._generate_test_config(reader)
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output = self._call_cntk(CNTK_TEST_CONFIG_FILENAME, config_content)
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return self._call_cntk(CNTK_TEST_CONFIG_FILENAME, config_content)
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def predict(self, reader=None):
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'''
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@ -308,7 +318,7 @@ class Context(AbstractContext):
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Returns the predicted output
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'''
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config_content = self._generate_predict_config(reader)
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output = self._call_cntk(CNTK_PREDICT_CONFIG_FILENAME, config_content)
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return self._call_cntk(CNTK_PREDICT_CONFIG_FILENAME, config_content)
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'''
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Regular expression to parse the shape information of the nodes out of
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@ -29,13 +29,16 @@ if (__name__ == "__main__"):
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hidden_dim=200
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training_filename=os.path.join("Data", "Train-28x28.txt")
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test_filename=os.path.join("Data", "Test-28x28.txt")
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features = Input(feat_dim, var_name='features')
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features.attach_uci_fast_reader(training_filename, 1)
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feat_scale = Constant(0.00390625)
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feats_scaled = Scale(feat_scale, features)
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labels = Input(label_dim, tag='label', var_name='labels')
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labels.attach_uci_fast_reader(training_filename, 0, True, 1, os.path.join("Data", "labelsmap.txt"))
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h1 = dnn_sigmoid_layer(feat_dim, hidden_dim, feats_scaled, 1)
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out = dnn_layer(hidden_dim, label_dim, h1, 1)
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@ -44,20 +47,16 @@ if (__name__ == "__main__"):
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ec = CrossEntropyWithSoftmax(labels, out)
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ec.tag = 'criterion'
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# Build the reader
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r = UCIFastReader(filename=training_filename)
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# Add the input node to the reader
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r.add_input(features, 1, feat_dim)
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r.add_input(labels, 0, 1, label_dim, os.path.join("Data", "labelsmap.txt"))
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# Build the optimizer (settings are scaled down)
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my_sgd = SGD(epoch_size = 600, minibatch_size = 32, learning_ratesPerMB = 0.1, max_epochs = 5, momentum_per_mb = 0)
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# Create a context or re-use if already there
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with Context('mnist_one_layer', root_node= ec, clean_up=False) as ctx:
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# CNTK actions
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ctx.train(my_sgd, r)
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r["FileName"] = os.path.join("Data", "Test-28x28.txt")
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ctx.test(r)
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ctx.predict(r)
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#ctx.train(my_sgd)
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features.attach_uci_fast_reader(test_filename, 1)
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labels.attach_uci_fast_reader(test_filename, 0, True, 1, os.path.join("Data", "labelsmap.txt"))
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ctx.predict()
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ctx.test()
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ctx.predict()
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@ -147,15 +147,15 @@ class ComputationNode(object):
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inputs_param = [p_value]
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input_nodes_vars = []
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for p_value in inputs_param:
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if p_value in unrolled_nodes:
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for pv in inputs_param:
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if pv in unrolled_nodes:
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# we have seen this node already, so just retrieve its
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# name
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child_var = unrolled_nodes[p_value]
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child_var = unrolled_nodes[pv]
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else:
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child_var, node_counter, child_desc = p_value._to_config_recursively(
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child_var, node_counter, child_desc = pv._to_config_recursively(
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desc, unrolled_nodes, inputs, readers, node_counter)
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unrolled_nodes[p_value] = child_var
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unrolled_nodes[pv] = child_var
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input_nodes_vars.append(child_var)
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param_variable_names.append(_tuple_to_cntk_shape(input_nodes_vars))
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@ -164,7 +164,7 @@ class ComputationNode(object):
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self._param_to_brainscript(p_name, p_value))
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if self.reader:
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readers.append(self.reader)
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readers.add(self.reader)
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if self._is_input():
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inputs.add(self)
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@ -188,7 +188,7 @@ class ComputationNode(object):
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'''
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unrolled_nodes = {}
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inputs=set()
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readers=[]
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readers=set()
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var_name, node_counter, desc = self._to_config_recursively(
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desc=[],
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unrolled_nodes=unrolled_nodes,
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@ -198,13 +198,14 @@ class ComputationNode(object):
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return var_name, node_counter, desc, len(inputs)>0, readers
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def _dedupe_readers(self, readers):
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import copy
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readers_map = {}
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for r in readers:
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filename = r['FileName']
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if filename in readers_map:
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readers_map[filename].inputs_def.extend(r.inputs_def)
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else:
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readers_map[filename] = r
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readers_map[filename] = copy.deepcopy(r)
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return [r for r in readers_map.values()]
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