Predict from pretrained example

example/README.md

@@ -2,6 +2,14 @@ MXNet Examples
 ==============
 This folder contains examples of MXNet.
 
+Notebooks
+--------
+* [composite symbol](composite_symbol.ipynb) gives you a demo of how to composite a symbolic Inception-BatchNorm Network
+* [cifar-10 recipe](cifar-recipe.ipynb) gives you a step by step demo of how to use MXNet
+* [cifar-100](cifar-100.ipynb) gives you a demo of how to train a 75.68% accuracy CIFAR-100 model
+* [predict with pretained model](predict-with-pretrained-model.ipynb) gives you a demo of use a pretrained Inception-BN Network
+
+
 Contents
 --------
 * [mnist](mnist) gives examples on training mnist.
@@ -13,4 +21,3 @@ Python Howto
 [Python Howto](python-howto) is a folder containing short snippet of code
 introducing a certain feature of mxnet.
 
-

example/notebooks/cifar-recipe.ipynb

@@ -410,9 +410,12 @@
     "fea_symbol = internals[\"global_avg_output\"]\n",
     "\n",
     "# Make a new model by using an internal symbol. We can reuse all parameters from model we trained before\n",
-    "# In this case, we must set ```allow_extra_params``` to True\n",
+    "# In this case, we must set ```allow_extra_params``` to True \n",
+    "# Because we don't need params of FullyConnected Layer\n",
+    "\n",
     "feature_extractor = mx.model.FeedForward(ctx=mx.gpu(), symbol=fea_symbol, \n",
-    "                                         arg_params=model.arg_params, aux_params=model.aux_params,\n",
+    "                                         arg_params=model.arg_params,\n",
+    "                                         aux_params=model.aux_params,\n",
     "                                         allow_extra_params=True)\n",
     "# Predict as normal\n",
     "global_pooling_feature = feature_extractor.predict(test_dataiter)\n",

python/mxnet/metric.py

@@ -52,6 +52,22 @@ def update(self, label, pred):
         self.sum_metric += numpy.sum(py == label)
         self.num_inst += label.size
 
+class LogLoss(EvalMetric):
+    """Calculate logloss"""
+    def __init__(self):
+        self.eps = 1e-15
+        super(LogLoss, self).__init__('logloss')
+
+    def update(self, label, pred):
+        # pylint: disable=invalid-name
+        pred = pred.asnumpy()
+        label = label.asnumpy().astype('int32')
+        for i in range(label.size):
+            p = pred[i][label[i]]
+            assert(numpy.isnan(p) == False)
+            p = max(min(p, 1 - self.eps), self.eps)
+            self.sum_metric += -numpy.log(p)
+        self.num_inst += label.size
 
 class CustomMetric(EvalMetric):
     """Custom evaluation metric that takes a NDArray function.
@@ -110,5 +126,7 @@ def create(metric):
         raise TypeError('metric should either be callable or str')
     if metric == 'acc' or metric == 'accuracy':
         return Accuracy()
+    elif metric == 'logloss':
+        return LogLoss()
     else:
         raise ValueError('Cannot find metric %s' % metric)

python/mxnet/model.py

@@ -511,7 +511,8 @@ def _init_predictor(self, input_shape):
 
         for name, value in list(zip(self.symbol.list_arguments(), pred_exec.arg_arrays)):
             if not self._is_data_arg(name):
-                assert name in self.arg_params
+                if not name in self.arg_params:
+                    raise ValueError("%s not exist in arg_params" % name)
                 self.arg_params[name].copyto(value)
         for name, value in list(zip(self.symbol.list_auxiliary_states(), pred_exec.aux_arrays)):
             assert name in self.aux_params