change examples for new symbolic calling conventions (nnvm, apache#146)

examples/char-lstm/lstm.jl

@@ -25,9 +25,9 @@ function lstm_cell(data::mx.SymbolicNode, prev_state::LSTMState, param::LSTMPara
     data = mx.Dropout(data, p=dropout)
   end
 
-  i2h = mx.FullyConnected(data=data, weight=param.i2h_W, bias=param.i2h_b,
+  i2h = mx.FullyConnected(data, weight=param.i2h_W, bias=param.i2h_b,
                           num_hidden=4num_hidden, name=symbol(name, "_i2h"))
-  h2h = mx.FullyConnected(data=prev_state.h, weight=param.h2h_W, bias=param.h2h_b,
+  h2h = mx.FullyConnected(prev_state.h, weight=param.h2h_W, bias=param.h2h_b,
                           num_hidden=4num_hidden, name=symbol(name, "_h2h"))
 
   gates = mx.SliceChannel(i2h + h2h, num_outputs=4, name=symbol(name, "_gates"))
@@ -71,7 +71,7 @@ function LSTM(n_layer::Int, seq_len::Int, dim_hidden::Int, dim_embed::Int, n_cla
   for t = 1:seq_len
     data   = mx.Variable(symbol(name, "_data_$t"))
     label  = mx.Variable(symbol(name, "_label_$t"))
-    hidden = mx.FullyConnected(data=data, weight=embed_W, num_hidden=dim_embed,
+    hidden = mx.FullyConnected(data, weight=embed_W, num_hidden=dim_embed,
                                no_bias=true, name=symbol(name, "_embed_$t"))
 
     # stack LSTM cells
@@ -88,7 +88,7 @@ function LSTM(n_layer::Int, seq_len::Int, dim_hidden::Int, dim_embed::Int, n_cla
     if dropout > 0
       hidden = mx.Dropout(hidden, p=dropout)
     end
-    pred = mx.FullyConnected(data=hidden, weight=pred_W, bias=pred_b, num_hidden=n_class,
+    pred = mx.FullyConnected(hidden, weight=pred_W, bias=pred_b, num_hidden=n_class,
                              name=symbol(name, "_pred_$t"))
     smax = mx.SoftmaxOutput(pred, label, name=symbol(name, "_softmax_$t"))
     push!(outputs, smax)

examples/cifar10/cifar10.jl

@@ -5,9 +5,9 @@ using MXNet
 
 # basic Conv + BN + ReLU factory
 function conv_factory(data, num_filter, kernel; stride=(1,1), pad=(0,0), act_type=:relu)
-  conv = mx.Convolution(data=data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad)
-  bn   = mx.BatchNorm(data=conv)
-  act  = mx.Activation(data=bn, act_type=act_type)
+  conv = mx.Convolution(data, num_filter=num_filter, kernel=kernel, stride=stride, pad=pad)
+  bn   = mx.BatchNorm(conv)
+  act  = mx.Activation(bn, act_type=act_type)
   return act
 end
 
@@ -16,7 +16,7 @@ function downsample_factory(data, ch_3x3)
   # conv 3x3
   conv = conv_factory(data, ch_3x3, (3,3), stride=(2,2), pad=(1,1))
   # pool
-  pool = mx.Pooling(data=data, kernel=(3,3), stride=(2,2), pool_type=:max)
+  pool = mx.Pooling(data, kernel=(3,3), stride=(2,2), pool_type=:max)
   # concat
   concat = mx.Concat(conv, pool)
   return concat
@@ -48,10 +48,10 @@ in4d    = simple_factory(in4b, 48, 96)
 in4e    = downsample_factory(in4d, 96)
 in5a    = simple_factory(in4e, 176, 160)
 in5b    = simple_factory(in5a, 176, 160)
-pool    = mx.Pooling(data=in5b, pool_type=:avg, kernel=(7,7), name=:global_pool)
-flatten = mx.Flatten(data=pool, name=:flatten1)
-fc      = mx.FullyConnected(data=flatten, num_hidden=10, name=:fc1)
-softmax = mx.SoftmaxOutput(data=fc, name=:loss)
+pool    = mx.Pooling(in5b, pool_type=:avg, kernel=(7,7), name=:global_pool)
+flatten = mx.Flatten(pool, name=:flatten1)
+fc      = mx.FullyConnected(flatten, num_hidden=10, name=:fc1)
+softmax = mx.SoftmaxOutput(fc, name=:loss)
 
 
 #--------------------------------------------------------------------------------

examples/mnist/lenet.jl

@@ -7,25 +7,25 @@ using MXNet
 data = mx.Variable(:data)
 
 # first conv
-conv1 = @mx.chain mx.Convolution(data=data, kernel=(5,5), num_filter=20)  =>
+conv1 = @mx.chain mx.Convolution(data, kernel=(5,5), num_filter=20)  =>
                   mx.Activation(act_type=:tanh) =>
                   mx.Pooling(pool_type=:max, kernel=(2,2), stride=(2,2))
 
 # second conv
-conv2 = @mx.chain mx.Convolution(data=conv1, kernel=(5,5), num_filter=50) =>
+conv2 = @mx.chain mx.Convolution(conv1, kernel=(5,5), num_filter=50) =>
                   mx.Activation(act_type=:tanh) =>
                   mx.Pooling(pool_type=:max, kernel=(2,2), stride=(2,2))
 
 # first fully-connected
-fc1   = @mx.chain mx.Flatten(data=conv2) =>
+fc1   = @mx.chain mx.Flatten(conv2) =>
                   mx.FullyConnected(num_hidden=500) =>
                   mx.Activation(act_type=:tanh)
 
 # second fully-connected
-fc2   = mx.FullyConnected(data=fc1, num_hidden=10)
+fc2   = mx.FullyConnected(fc1, num_hidden=10)
 
 # softmax loss
-lenet = mx.SoftmaxOutput(data=fc2, name=:softmax)
+lenet = mx.SoftmaxOutput(fc2, name=:softmax)
 
 
 #--------------------------------------------------------------------------------

examples/mnist/mlp.jl

@@ -6,12 +6,12 @@ using MXNet
 
 #-- Option 1: explicit composition
 # data = mx.Variable(:data)
-# fc1  = mx.FullyConnected(data = data, name=:fc1, num_hidden=128)
-# act1 = mx.Activation(data = fc1, name=:relu1, act_type=:relu)
-# fc2  = mx.FullyConnected(data = act1, name=:fc2, num_hidden=64)
-# act2 = mx.Activation(data = fc2, name=:relu2, act_type=:relu)
-# fc3  = mx.FullyConnected(data = act2, name=:fc3, num_hidden=10)
-# mlp  = mx.SoftmaxOutput(data = fc3, name=:softmax)
+# fc1  = mx.FullyConnected(data, name=:fc1, num_hidden=128)
+# act1 = mx.Activation(fc1, name=:relu1, act_type=:relu)
+# fc2  = mx.FullyConnected(act1, name=:fc2, num_hidden=64)
+# act2 = mx.Activation(fc2, name=:relu2, act_type=:relu)
+# fc3  = mx.FullyConnected(act2, name=:fc3, num_hidden=10)
+# mlp  = mx.SoftmaxOutput(fc3, name=:softmax)
 
 #-- Option 2: using the mx.chain macro
 # mlp = @mx.chain mx.Variable(:data)             =>