[NDArray] add clip op

example/notebooks/cifar-recipe.ipynb

@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -247,16 +247,16 @@
      "output_type": "stream",
      "text": [
       "INFO:root:Start training with [gpu(0)]\n",
-      "INFO:root:Batch [50]\tSpeed: 1091.84 samples/sec\n",
-      "INFO:root:Batch [100]\tSpeed: 1084.80 samples/sec\n",
-      "INFO:root:Batch [150]\tSpeed: 1084.55 samples/sec\n",
-      "INFO:root:Batch [200]\tSpeed: 1077.30 samples/sec\n",
-      "INFO:root:Batch [250]\tSpeed: 1074.73 samples/sec\n",
-      "INFO:root:Batch [300]\tSpeed: 1075.67 samples/sec\n",
-      "INFO:root:Batch [350]\tSpeed: 1067.09 samples/sec\n",
-      "INFO:root:Iteration[0] Train-accuracy=0.525695\n",
-      "INFO:root:Iteration[0] Time cost=47.012\n",
-      "INFO:root:Iteration[0] Validation-accuracy=0.660008\n"
+      "INFO:root:Batch [50]\tSpeed: 1003.50 samples/sec\n",
+      "INFO:root:Batch [100]\tSpeed: 976.31 samples/sec\n",
+      "INFO:root:Batch [150]\tSpeed: 975.57 samples/sec\n",
+      "INFO:root:Batch [200]\tSpeed: 964.21 samples/sec\n",
+      "INFO:root:Batch [250]\tSpeed: 963.53 samples/sec\n",
+      "INFO:root:Batch [300]\tSpeed: 963.95 samples/sec\n",
+      "INFO:root:Batch [350]\tSpeed: 963.71 samples/sec\n",
+      "INFO:root:Iteration[0] Train-accuracy=0.520520\n",
+      "INFO:root:Iteration[0] Time cost=52.424\n",
+      "INFO:root:Iteration[0] Validation-accuracy=0.652393\n"
      ]
     }
    ],
@@ -272,14 +272,14 @@
     "#           eval_data=test_dataiter,\n",
     "#           eval_metric=\"accuracy\",\n",
     "#           epoch_end_callback=mx.helper.Speedometer(batch_size),\n",
-    "#           iter_end_callback=mx.model.do_checkpoint(model_prefix))\n"
+    "#           iter_end_callback=mx.callback.do_checkpoint(model_prefix))\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "After only 1 epoch, our model is able to acheive about 66% accuracy on testset.\n",
+    "After only 1 epoch, our model is able to acheive about 65% accuracy on testset.\n",
     "We can save our model by calling either ```save``` or using ```pickle```.\n"
    ]
   },
@@ -348,7 +348,7 @@
      "output_type": "stream",
      "text": [
       "INFO:root:Finish predict...\n",
-      "INFO:root:final accuracy = 0.651000\n"
+      "INFO:root:final accuracy = 0.652600\n"
      ]
     }
    ],
@@ -385,33 +385,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "Symbol only support integer index to fetch i-th output",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-17-0e3d13f4a151>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0minternals\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msoftmax\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_internals\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mfea_symbol\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minternals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"global_avg_output\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m feature_extractor = mx.model.FeedForward(ctx=mx.gpu(), symbol=group, \n",
-      "\u001b[1;32m/home/bing/wtf/mxnet/python/mxnet/symbol.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m    156\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 158\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Symbol only support integer index to fetch i-th output'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    159\u001b[0m         \u001b[0mhandle\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSymbolHandle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    160\u001b[0m         check_call(_LIB.MXSymbolGetOutput(\n",
-      "\u001b[1;31mTypeError\u001b[0m: Symbol only support integer index to fetch i-th output"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(10000, 336, 1, 1)\n"
      ]
     }
    ],
    "source": [
-    "# predict internal featuremaps\n",
+    "# Predict internal featuremaps\n",
+    "# From a symbol, we are able to get all internals. Note it is still a symbol\n",
     "internals = softmax.get_internals()\n",
-    "\n",
+    "# We get get an internal symbol for the feature.\n",
+    "# By default, the symbol is named as \"symbol_name + _output\"\n",
+    "# in this case we'd like to get global_avg\" layer's output as feature, so its \"global_avg_output\"\n",
+    "# You may call ```internals.list_outputs()``` to find the target\n",
+    "# but we strongly suggests set a special name for special symbol \n",
     "fea_symbol = internals[\"global_avg_output\"]\n",
     "\n",
-    "feature_extractor = mx.model.FeedForward(ctx=mx.gpu(), symbol=group, \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",
+    "feature_extractor = mx.model.FeedForward(ctx=mx.gpu(), symbol=fea_symbol, \n",
     "                                         arg_params=model.arg_params, aux_params=model.aux_params,\n",
     "                                         allow_extra_params=True)\n",
+    "# Predict as normal\n",
     "global_pooling_feature = feature_extractor.predict(test_dataiter)\n",
     "print(global_pooling_feature.shape)"
    ]

python/mxnet/__init__.py

@@ -22,6 +22,8 @@
 from . import optimizer
 from . import model
 from . import initializer
+# use mx.init as short for mx.initializer
+from . import initializer as init
 from . import visualization
 # use viz as short for mx.ndarray
 from . import visualization as viz

python/mxnet/ndarray.py

@@ -243,6 +243,16 @@ def _slice(self, start, stop):
             self.handle, start, stop, ctypes.byref(handle)))
         return NDArray(handle=handle, writable=self.writable)
 
+    def clip(self, value):
+        """Clip NDArray to range [-value, value] and remove NaN
+
+        Parameters
+        ----------
+        value: float
+            cliped range
+        """
+        return NDArray._clip_scalar(self, float(value))
+
     def wait_to_read(self):
         """Block until all pending writes operations on current NDArray are finished.
 
@@ -636,6 +646,9 @@ def generic_ndarray_function(*args, **kwargs):
     ret_function.__name__ = func_name
     ret_function.__doc__ = doc_str
     return ret_function
+
+
+
 # pylint: enable=too-many-locals, invalid-name
 
 def _init_ndarray_module():

python/mxnet/optimizer.py

@@ -34,14 +34,19 @@ class SGD(Optimizer):
 
     rescale_grad : float, optional
         rescaling factor of gradient.
+
+    clip_gradient : float, optional
+        clip gradient in range [-clip_gradient, clip_gradient]
     """
     def __init__(self, learning_rate=0.01, momentum=0.0,
-                 wd=0.0001, rescale_grad=1, lr_scheduler=None):
+                 wd=0.0001, rescale_grad=1, clip_gradient=None,
+                 lr_scheduler=None):
         super(SGD, self).__init__()
         self.lr = learning_rate
         self.momentum = momentum
         self.wd = wd
         self.rescale_grad = rescale_grad
+        self.clip_gradient = clip_gradient
         self.lr_scheduler = lr_scheduler
         if lr_scheduler != None:
             self.lr_scheduler.base_lr = learning_rate
@@ -89,7 +94,11 @@ def update(self, index, weight, grad, state):
         if state:
             mom = state
             mom[:] *= self.momentum
-            mom[:] += -lr * (grad * self.rescale_grad + self.wd * weight)
+            if self.clip_gradient == None:
+                mom[:] += -lr * (grad * self.rescale_grad + self.wd * weight)
+            else:
+                mom[:] += -lr * (grad.clip(self.clip_gradient) * self.rescale_grad +
+                                 self.wd * weight)
             weight[:] += mom
         else:
             assert self.momentum == 0.0

src/ndarray/ndarray.cc

@@ -315,6 +315,7 @@ NDArray operator*(const NDArray &lhs, const real_t &rhs) {
 NDArray operator/(const NDArray &lhs, const real_t &rhs) {
   return ScalarOpRet<ndarray::Div, false>(lhs, rhs);
 }
+
 // Binary
 NDArray &NDArray::operator=(real_t scalar) {
   SetValueOp(scalar, this);
@@ -510,7 +511,7 @@ MXNET_REGISTER_NDARRAY_FUN(_plus_scalar).set_function(ScalarOp<ndarray::Plus, fa
 MXNET_REGISTER_NDARRAY_FUN(_minus_scalar).set_function(ScalarOp<ndarray::Minus, false>);
 MXNET_REGISTER_NDARRAY_FUN(_mul_scalar).set_function(ScalarOp<ndarray::Mul, false>);
 MXNET_REGISTER_NDARRAY_FUN(_div_scalar).set_function(ScalarOp<ndarray::Div, false>);
-
+MXNET_REGISTER_NDARRAY_FUN(_clip_scalar).set_function(ScalarOp<ndarray::Clip, false>);
 // register API function
 // scalar, reverse scalar
 MXNET_REGISTER_NDARRAY_FUN(_rminus_scalar).set_function(ScalarOp<ndarray::Minus, true>);

src/ndarray/ndarray_function-inl.h

@@ -100,11 +100,13 @@ DECL_SCALAR(DEVICE, Plus, EvalScalar_, true)
 DECL_SCALAR(DEVICE, Minus, EvalScalar_, true)
 DECL_SCALAR(DEVICE, Mul, EvalScalar_, true)
 DECL_SCALAR(DEVICE, Div, EvalScalar_, true)
+DECL_SCALAR(DEVICE, Clip, EvalScalar_, true)
 // for reverse seq
 DECL_SCALAR(DEVICE, Plus, EvalScalar_, false)
 DECL_SCALAR(DEVICE, Minus, EvalScalar_, false)
 DECL_SCALAR(DEVICE, Mul, EvalScalar_, false)
 DECL_SCALAR(DEVICE, Div, EvalScalar_, false)
+DECL_SCALAR(DEVICE, Clip, EvalScalar_, false)
 }  // namespace ndarray
 }  // namespace mxnet
 

src/ndarray/ndarray_function.h

@@ -37,6 +37,16 @@ struct Div : public BinaryBase {
   typedef mshadow::op::div mshadow_op;
 };
 
+struct Clip : public BinaryBase {
+  struct mshadow_op {
+    MSHADOW_XINLINE static real_t Map(real_t a, real_t b) {
+      if (isnan(a)) return 0.0f;
+      if (a < -b) return -b;
+      if (a > b) return b;
+      return a;
+    }
+  };
+};
 // type holder for random number generators
 struct UniformDistribution {};