Modifications to TF scripts

Author: mandar2812

scripts/cifar.sc

@@ -29,7 +29,7 @@
     tf.learn.ReLU("Layer_0/ReLU", 0.1f) >>
     tf.learn.MaxPool("Layer_0/MaxPool", Seq(1, 2, 2, 1), 1, 1, SamePadding) >>
     tf.learn.Conv2D("Layer_1/Conv2D", Shape(2, 2, 16, 32), 1, 1, SamePadding) >>
-    tf.learn.AddBias(variableScope = "Bias_1") >>
+    tf.learn.AddBias("Bias_1") >>
     tf.learn.ReLU("Layer_1/ReLU", 0.1f) >>
     tf.learn.MaxPool("Layer_1/MaxPool", Seq(1, 2, 2, 1), 1, 1, SamePadding) >>
     tf.learn.Flatten("Layer_2/Flatten") >>

scripts/rnnPTB.sc

@@ -23,14 +23,12 @@
   object RNNOutputLayer extends tf.learn.Layer[LSTMTuple, Output]("RNNOutputLayer") {
     override val layerType: String = "RNNOutputLayer"
 
-    override protected def forward(
-      input: LSTMTuple, mode: tf.learn.Mode): tf.learn.LayerInstance[LSTMTuple, Output] = {
+    override protected def _forward(input: LSTMTuple, mode: tf.learn.Mode): Output = {
       val weights = tf.variable("OutputWeights", dataType, Shape(numHidden, vocabularySize))
       val bias = tf.variable("OutputBias", dataType, Shape(vocabularySize))
       val output = tf.linear(tf.reshape(input.output, Shape(-1, numHidden)), weights.value, bias.value)
       // We reshape the output logits to feed into the sequence loss layer
-      val reshapedOutput = tf.reshape(output, Shape(batchSize, numSteps, vocabularySize))
-      tf.learn.LayerInstance(input, reshapedOutput, trainableVariables = Set(weights, bias))
+      tf.reshape(output, Shape(batchSize, numSteps, vocabularySize))
     }
   }