adjusted Show, Attend and Tell model

src/test/resources/models/showAttendTell/Main.emadl

@@ -3,7 +3,7 @@ package showAttendTell;
 component Main{
     ports in Z(0:255)^{3, 224, 224} images,
           in Z(-oo:oo)^{64,2048} data,
-          out Z(0:25316)^{1} target[25];
+          out Z(0:37758)^{1} target[25];
 
     instance Show_attend_tell net;
 

src/test/resources/models/showAttendTell/Show_attend_tell.cnnt

@@ -4,7 +4,6 @@ configuration Show_attend_tell{
      context:cpu
      eval_metric:bleu
      loss:softmax_cross_entropy_ignore_indices{
-        sparse_label:true
         ignore_indices:2
      }
      use_teacher_forcing:true

src/test/resources/models/showAttendTell/Show_attend_tell.emadl

@@ -9,7 +9,7 @@ component Show_attend_tell{
 
       layer LSTM(units=512) decoder;
 
-      layer FullyConnected(units = 256) features;
+      layer FullyConnected(units = 256, flatten=false) features;
       layer FullyConnected(units = 1, flatten=false) attention;
 
       0 -> target[0];
@@ -30,7 +30,6 @@ component Show_attend_tell{
                Tanh() ->
                FullyConnected(units=1, flatten=false) ->
                Softmax(axis=0) ->
-               Dropout(p=0.25) ->
                attention
            |
                features.output

src/test/resources/models/showAttendTell/Show_attend_tell_images_as_input.emadl

@@ -11,15 +11,15 @@ component Show_attend_tell_images_as_input{
 
       layer LSTM(units=512) decoder;
 
-      layer FullyConnected(units = 256) features;
+      layer FullyConnected(units = 256, flatten=false) features;
       layer FullyConnected(units = 1, flatten=false) attention;
 
       0 -> target[0];
 
       images ->
-      Convolution(kernel=(7,7), channels=64, stride=(7,7), padding="valid") ->
-      Convolution(kernel=(4,4), channels=64, stride=(4,4), padding="valid") ->
-      GlobalPooling(pool_type="max") ->
+      Convolution(kernel=(7,7), channels=128, stride=(7,7), padding="valid") ->
+      Convolution(kernel=(4,4), channels=128, stride=(4,4), padding="valid") ->
+      Reshape(shape=(64, 128)) ->
       features;
 
        timed <t> GreedySearch(max_length=25){
@@ -36,7 +36,6 @@ component Show_attend_tell_images_as_input{
                Tanh() ->
                FullyConnected(units=1, flatten=false) ->
                Softmax(axis=0) ->
-               Dropout(p=0.25) ->
                attention
            |
                features.output

src/test/resources/target_code/gluon/CNNSupervisedTrainer_mnist_mnistClassifier_net.py

@@ -52,7 +52,6 @@ class SoftmaxCrossEntropyLossIgnoreIndices(gluon.loss.Loss):
         else:
             label = _reshape_like(F, label, pred)
             loss = -(pred * label).sum(axis=self._axis, keepdims=True)
-        #loss = _apply_weighting(F, loss, self._weight, sample_weight)
         # ignore some indices for loss, e.g. <pad> tokens in NLP applications
         for i in self._ignore_indices:
             loss = loss * mx.nd.logical_not(mx.nd.equal(mx.nd.argmax(pred, axis=1), mx.nd.ones_like(mx.nd.argmax(pred, axis=1))*i))
@@ -246,7 +245,7 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
         if loss == 'softmax_cross_entropy':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = mx.gluon.loss.SoftmaxCrossEntropyLoss(from_logits=fromLogits, sparse_label=sparseLabel)
-        if loss == 'softmax_cross_entropy_ignore_indices':
+        elif loss == 'softmax_cross_entropy_ignore_indices':
             fromLogits = loss_params['from_logits'] if 'from_logits' in loss_params else False
             loss_function = SoftmaxCrossEntropyLossIgnoreIndices(ignore_indices=ignore_indices, from_logits=fromLogits, sparse_label=sparseLabel)
         elif loss == 'sigmoid_binary_cross_entropy':
@@ -363,7 +362,12 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                             attention = mx.nd.squeeze(attention)
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+2)
-                            if dict[int(labels[l+1][0].asscalar())] == "<end>":
+                            if int(labels[l+1][0].asscalar()) > len(dict):
+                                ax.set_title("<unk>")
+                                img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
+                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
+                                break
+                            elif dict[int(labels[l+1][0].asscalar())] == "<end>":
                                 ax.set_title(".")
                                 img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                 ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
@@ -426,13 +430,18 @@ class CNNSupervisedTrainer_mnist_mnistClassifier_net:
                             attention = mx.nd.squeeze(attention)
                             attention_resized = np.resize(attention.asnumpy(), (8, 8))
                             ax = fig.add_subplot(max_length//3, max_length//4, l+2)
-                            if dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())] == "<end>":
+                            if int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar()) > len(dict):
+                                ax.set_title("<unk>")
+                                img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
+                                ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
+                                break
+                            elif dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())] == "<end>":
                                 ax.set_title(".")
                                 img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                 ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
                                 break
                             else:
-                                ax.set_title(dict[int(mx.nd.slice_axis(mx.nd.argmax(outputs[l+1], axis=1), axis=0, begin=0, end=1).asscalar())])
+                                ax.set_title(dict[int(mx.nd.slice_axis(outputs[l+1], axis=0, begin=0, end=1).squeeze().asscalar())])
                                 img = ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
                                 ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())