Integrate wilrich/logregacc into master
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Project Philly
Коммит
9541f38ae3
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@ -66,7 +66,7 @@ def train_eval_logistic_regression_from_file(criterion_name=None,
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def test_logistic_regression_from_file(device_id):
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result = train_eval_logistic_regression_from_file('crit_node', 'eval_node', device_id)
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TOLERANCE_ABSOLUTE = 1E-02
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TOLERANCE_ABSOLUTE = 1E-06
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assert np.allclose(result['perplexity'], 1.55153792, atol=TOLERANCE_ABSOLUTE)
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assert np.allclose(result['crit_node'], 0.43924664, atol=TOLERANCE_ABSOLUTE)
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assert np.allclose(result['eval_node'], 3.26340137, atol=TOLERANCE_ABSOLUTE)
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@ -16,28 +16,47 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..', '..'))
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import numpy as np
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import cntk as C
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train_N = 1000
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test_N = 500
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# Mapping 2 numbers to 3 classes
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feature_dim = 2
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num_classes = 3
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def synthetic_data(N, feature_dim, num_classes):
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# Create synthetic data using NumPy.
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Y = np.random.randint(size=(N, 1), low=0, high=num_classes)
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# Make sure that the data is separable
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X = (np.random.randn(N, feature_dim)+3) * (Y+1)
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# converting class 0 into the vector "1 0 0",
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# class 1 into vector "0 1 0", ...
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class_ind = [Y==class_number for class_number in range(num_classes)]
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Y = np.asarray(np.hstack(class_ind), dtype=int)
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return X, Y
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def train_eval_logistic_regression_with_numpy(criterion_name=None,
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eval_name=None, device_id=-1):
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# for repro and tests :-)
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np.random.seed(1)
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N = 500
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d = 250
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train_X, train_y = synthetic_data(train_N, feature_dim, num_classes)
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test_X, test_y = synthetic_data(test_N, feature_dim, num_classes)
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# create synthetic data using numpy
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X = np.random.randn(N, d)
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Y = np.random.randint(size=(N, 1), low=0, high=2)
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Y = np.hstack((Y, 1-Y))
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# set up the training data for CNTK
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x = C.input_numpy(X)
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y = C.input_numpy(Y)
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# Set up the training data for CNTK. Before writing the CNTK configuration,
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# the data will be attached to X.reader.batch and y.reader.batch and then
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# serialized.
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X = C.input_numpy(train_X)
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y = C.input_numpy(train_y)
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# define our network -- one weight tensor and a bias
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W = C.parameter(value=np.zeros(shape=(2, d)))
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b = C.parameter(value=np.zeros(shape=(2, 1)))
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out = C.times(W, x) + b
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W = C.parameter(value=np.zeros(shape=(num_classes, feature_dim)))
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b = C.parameter(value=np.zeros(shape=(num_classes, 1)))
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out = C.times(W, X) + b
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ce = C.cross_entropy_with_softmax(y, out)
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ce.tag = 'criterion'
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@ -47,14 +66,18 @@ def train_eval_logistic_regression_with_numpy(criterion_name=None,
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eval.tag = 'eval'
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eval.name = eval_name
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my_sgd = C.SGDParams(epoch_size=0, minibatch_size=25, learning_rates_per_mb=0.1, max_epochs=3)
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with C.LocalExecutionContext('logreg') as ctx:
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my_sgd = C.SGDParams(epoch_size=0, minibatch_size=25,
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learning_rates_per_mb=0.1, max_epochs=3)
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with C.LocalExecutionContext('logreg', clean_up=False) as ctx:
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ctx.device_id = device_id
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ctx.train(
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root_nodes=[ce,eval],
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training_params=my_sgd)
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# For testing, we attach the test data to the input nodes.
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X.reader.batch, y.reader.batch = test_X, test_y
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result = ctx.test(root_nodes=[ce,eval])
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return result
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@ -63,10 +86,11 @@ def test_logistic_regression_with_numpy(device_id):
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result = train_eval_logistic_regression_with_numpy('crit_node',
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'eval_node', device_id)
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TOLERANCE_ABSOLUTE = 1E-02
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assert np.allclose(result['perplexity'], 1.55057073, atol=TOLERANCE_ABSOLUTE)
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assert np.allclose(result['crit_node'], 0.43862308, atol=TOLERANCE_ABSOLUTE)
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assert np.allclose(result['eval_node'], 1.16664551, atol=TOLERANCE_ABSOLUTE)
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TOLERANCE_ABSOLUTE = 1E-06
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print(result)
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assert np.allclose(result['perplexity'], 2.33378225, atol=TOLERANCE_ABSOLUTE)
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assert np.allclose(result['crit_node'], 0.84749023, atol=TOLERANCE_ABSOLUTE)
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assert np.allclose(result['eval_node'], 2.69121655, atol=TOLERANCE_ABSOLUTE)
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if __name__ == "__main__":
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print(train_eval_logistic_regression_with_numpy())
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@ -24,9 +24,6 @@ class AbstractReader(with_metaclass(ABCMeta)):
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def __ne__(self, x): return x is not self
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def _to_aggregate_form():
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pass
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class UCIFastReader(AbstractReader):
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@ -133,16 +130,29 @@ class CNTKTextFormatReader(AbstractReader):
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if randomize is None:
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randomize = 'none'
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self.randomize = randomize.lower()
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assert self.randomize in ['auto', 'none']
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if not self.randomize in ['auto', 'none']:
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raise ValueError('parameter "randomize" can be only "auto", ' +
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'"none", or None. You gave: %s'%randomize)
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self.skip_sequence_ids = bool(skip_sequence_ids)
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self.max_errors = int(max_errors)
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assert self.max_errors >= 0
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if not self.max_errors >= 0:
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raise ValueError('parameter "max_errors" has to be an integer ' +
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'greater than or equal to 0. You gave: %s'%max_errors)
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self.trace_level = int(trace_level)
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assert self.trace_level in [0,1,2]
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if not self.trace_level in [0,1,2]:
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raise ValueError('parameter "trace_level" has to be an integer ' +
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'from [0, 1, 2]. You gave: %s'%str(trace_level))
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self.chunk_size_in_bytes = int(chunk_size_in_bytes)
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assert self.chunk_size_in_bytes > 0
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if not self.chunk_size_in_bytes > 0:
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raise ValueError('parameter "chunk_size_in_bytes" has to be an integer ' +
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'greater than zero. You gave: %s'%str(chunk_size_in_bytes))
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self.num_chunks_to_cache = int(num_chunks_to_cache)
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assert self.chunk_size_in_bytes >= 0
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if self.chunk_size_in_bytes < 0:
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raise ValueError('parameter "chunk_size_in_bytes" has to be an integer ' +
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'greater than or equal to zero. You gave: %s'\
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%str(self.chunk_size_in_bytes))
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def map(self, node_or_name, **kw):
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'''
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@ -211,10 +221,14 @@ class CNTKTextFormatReader(AbstractReader):
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'''
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if input_map.has_unmapped():
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if len(input_map.node_map) > 0:
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raise ValueError('you cannot have inputs initialized with '+
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'NumPy arrays together with inputs that are ' +
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' initialized with a custom reader')
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input_map._serialize_unmapped_nodes(
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input_map.unmapped_nodes, self.filename)
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for node_or_name, param_dict in input_map.node_map.items():
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if (isinstance(node_or_name, ComputationNode)):
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name = node_or_name.name
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@ -477,10 +491,16 @@ class InputMap(object):
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from .utils import get_temp_filename
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filename = get_temp_filename(get_context().directory)
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assert not self.node_map
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if len(self.node_map) > 0:
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raise ValueError('you cannot have inputs initialized with '+
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'NumPy arrays together with inputs that are ' +
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' initialized with a custom reader')
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self._serialize_unmapped_nodes(filename)
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r = CNTKTextFormatReader(filename)
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# All the data we got, was through NumPy. In this case, we assume
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# that all the required randomization has happened already.
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r = CNTKTextFormatReader(filename, randomize=None)
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return r._to_config_description(self)
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@ -509,8 +529,9 @@ class InputMap(object):
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sample_sizes = collections.defaultdict(list)
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used_aliases = set()
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for node in self.unmapped_nodes:
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assert node._is_input()
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assert isinstance(node.reader, LazyInputReader)
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is_lazy_input = isinstance(node.reader, LazyInputReader)
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if not (node._is_input() and is_lazy_input):
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raise ValueError('expected NumPy input, but got "%s"'%str(node))
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l = node.reader
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@ -553,10 +574,7 @@ class InputMap(object):
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value_shape = shapes_in_tensor.pop()
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l.shape = value_shape if value_shape else (1,)
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assert node not in self.node_map
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self.node_map[node] = {
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'alias': l.input_alias,
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}
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self.node_map[node] = { 'alias': l.input_alias }
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# make sure all inputs have same sample size
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if len(sample_sizes) != 1:
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