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| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2016-08-22 10:34:24 -0800 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2016-08-22 11:47:07 -0700 |
| commit | b543d891faf7283b3a7342aa89ecb8ff9d44629a (patch) | |
| tree | 7b952d7a31828eb11cafe0f3f0dc83573fc234f2 | |
| parent | 8ea90fa68f15c8a91db7705750ed7a53f687fa1a (diff) | |
Make summary names for linear models unique
Change: 130962334
| -rw-r--r-- | tensorflow/contrib/learn/python/learn/models.py | 20 |
1 files changed, 10 insertions, 10 deletions
diff --git a/tensorflow/contrib/learn/python/learn/models.py b/tensorflow/contrib/learn/python/learn/models.py index 82329ce5eb..7a866b30cb 100644 --- a/tensorflow/contrib/learn/python/learn/models.py +++ b/tensorflow/contrib/learn/python/learn/models.py @@ -81,8 +81,9 @@ def linear_regression(x, y, init_mean=None, init_stddev=1.0): uniform_unit_scaling_initialzer will be used. """ with vs.variable_scope('linear_regression'): - logging_ops.histogram_summary('linear_regression.x', x) - logging_ops.histogram_summary('linear_regression.y', y) + scope_name = vs.get_variable_scope().name + logging_ops.histogram_summary('%s.x' % scope_name, x) + logging_ops.histogram_summary('%s.y' % scope_name, y) dtype = x.dtype.base_dtype y_shape = y.get_shape() if len(y_shape) == 1: @@ -103,8 +104,8 @@ def linear_regression(x, y, init_mean=None, init_stddev=1.0): initializer=init_ops.random_normal_initializer( init_mean, init_stddev, dtype=dtype), dtype=dtype) - logging_ops.histogram_summary('linear_regression.weights', weights) - logging_ops.histogram_summary('linear_regression.bias', bias) + logging_ops.histogram_summary('%s.weights' % scope_name, weights) + logging_ops.histogram_summary('%s.bias' % scope_name, bias) return losses_ops.mean_squared_error_regressor(x, y, weights, bias) @@ -139,8 +140,9 @@ def logistic_regression(x, uniform_unit_scaling_initialzer will be used. """ with vs.variable_scope('logistic_regression'): - logging_ops.histogram_summary('%s.x' % vs.get_variable_scope().name, x) - logging_ops.histogram_summary('%s.y' % vs.get_variable_scope().name, y) + scope_name = vs.get_variable_scope().name + logging_ops.histogram_summary('%s.x' % scope_name, x) + logging_ops.histogram_summary('%s.y' % scope_name, y) dtype = x.dtype.base_dtype # Set up the requested initialization. if init_mean is None: @@ -157,10 +159,8 @@ def logistic_regression(x, initializer=init_ops.random_normal_initializer( init_mean, init_stddev, dtype=dtype), dtype=dtype) - logging_ops.histogram_summary('%s.weights' % vs.get_variable_scope().name, - weights) - logging_ops.histogram_summary('%s.bias' % vs.get_variable_scope().name, - bias) + logging_ops.histogram_summary('%s.weights' % scope_name, weights) + logging_ops.histogram_summary('%s.bias' % scope_name, bias) # If no class weight provided, try to retrieve one from pre-defined # tensor name in the graph. if not class_weight: |