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CNNArch2Gluon
Commit cdd27689 authored by Sebastian N.'s avatar Sebastian N.
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Implemented parameters checkpoint_period, log_period and eval_train

parent 139f1e98
Pipeline #221320 passed with stages
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      Showing with 251 additions and 176 deletions
      src/main/resources/templates/gluon/CNNDataLoader.ftl
      View file @ cdd27689
      ......@@ -15,7 +15,7 @@ class ${tc.fileNameWithoutEnding}:
      def __init__(self):
      self._data_dir = "${tc.dataPath}/"
      def load_data(self, train_batch_size, test_batch_size):
      def load_data(self, batch_size):
      train_h5, test_h5 = self.load_h5_files()
      train_data = {}
      ......@@ -39,11 +39,7 @@ class ${tc.fileNameWithoutEnding}:
      train_iter = mx.io.NDArrayIter(data=train_data,
      label=train_label,
      batch_size=train_batch_size)
      train_test_iter = mx.io.NDArrayIter(data=train_data,
      label=train_label,
      batch_size=test_batch_size)
      batch_size=batch_size)
      test_iter = None
      ......@@ -64,9 +60,9 @@ class ${tc.fileNameWithoutEnding}:
      test_iter = mx.io.NDArrayIter(data=test_data,
      label=test_label,
      batch_size=test_batch_size)
      batch_size=batch_size)
      return train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images
      return train_iter, test_iter, data_mean, data_std, train_images, test_images
      def load_data_img(self, batch_size, img_size):
      train_h5, test_h5 = self.load_h5_files()
      ......
      src/main/resources/templates/gluon/CNNSupervisedTrainer.ftl
      View file @ cdd27689
      ......@@ -181,13 +181,15 @@ class ${tc.fileNameWithoutEnding}:
      num_epoch=10,
      eval_metric='acc',
      eval_metric_params={},
      eval_train=False,
      loss ='softmax_cross_entropy',
      loss_params={},
      optimizer='adam',
      optimizer_params=(('learning_rate', 0.001),),
      load_checkpoint=True,
      context='gpu',
      checkpoint_period=5,
      log_period=50,
      context='gpu',
      save_attention_image=False,
      use_teacher_forcing=False,
      normalize=True):
      ......@@ -213,10 +215,7 @@ class ${tc.fileNameWithoutEnding}:
      del optimizer_params['step_size']
      del optimizer_params['learning_rate_decay']
      train_batch_size = batch_size
      test_batch_size = batch_size
      train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(train_batch_size, test_batch_size)
      train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(batch_size)
      if normalize:
      self._net_creator.construct(context=mx_context, data_mean=data_mean, data_std=data_std)
      ......@@ -275,11 +274,11 @@ class ${tc.fileNameWithoutEnding}:
      else:
      logging.error("Invalid loss parameter.")
      speed_period = 50
      tic = None
      for epoch in range(begin_epoch, begin_epoch + num_epoch):
      loss_total = 0
      train_iter.reset()
      for batch_i, batch in enumerate(train_iter):
      with autograd.record():
      ......@@ -291,44 +290,51 @@ class ${tc.fileNameWithoutEnding}:
      loss.backward()
      loss_total += loss.sum().asscalar()
      for trainer in trainers:
      trainer.step(batch_size)
      if tic is None:
      tic = time.time()
      else:
      if batch_i % speed_period == 0:
      if batch_i % log_period == 0:
      try:
      speed = speed_period * batch_size / (time.time() - tic)
      speed = log_period * batch_size / (time.time() - tic)
      except ZeroDivisionError:
      speed = float("inf")
      logging.info("Epoch[%d] Batch[%d] Speed: %.2f samples/sec" % (epoch, batch_i, speed))
      loss_avg = loss_total / (batch_size * log_period)
      loss_total = 0
      logging.info("Epoch[%d] Batch[%d] Speed: %.2f samples/sec Loss: %.5f" % (epoch, batch_i, speed, loss_avg))
      tic = time.time()
      tic = None
      train_test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(train_test_iter):
      if True: <#-- Fix indentation -->
      if eval_train:
      train_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(train_iter):
      <#include "pythonExecuteTest.ftl">
      <#include "saveAttentionImageTrain.ftl">
      predictions = []
      for output_name in outputs:
      if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1:
      predictions.append(mx.nd.argmax(output_name, axis=1))
      else:
      predictions.append(output_name)
      predictions = []
      for output_name in outputs:
      if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1:
      predictions.append(mx.nd.argmax(output_name, axis=1))
      else:
      predictions.append(output_name)
      metric.update(preds=predictions, labels=labels)
      train_metric_score = metric.get()[1]
      metric.update(preds=predictions, labels=labels)
      train_metric_score = metric.get()[1]
      else:
      train_metric_score = 0
      test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      ......
      src/main/resources/templates/gluon/CNNTrainer.ftl
      View file @ cdd27689
      ......@@ -31,6 +31,12 @@ if __name__ == "__main__":
      <#if (config.loadCheckpoint)??>
      load_checkpoint=${config.loadCheckpoint?string("True","False")},
      </#if>
      <#if (config.checkpointPeriod)??>
      checkpoint_period=${config.checkpointPeriod},
      </#if>
      <#if (config.logPeriod)??>
      log_period=${config.logPeriod},
      </#if>
      <#if (config.context)??>
      context='${config.context}',
      </#if>
      ......@@ -51,6 +57,9 @@ if __name__ == "__main__":
      </#if>
      },
      </#if>
      <#if (config.evalTrain)??>
      eval_train=${config.evalTrain?string("True","False")},
      </#if>
      <#if (config.configuration.loss)??>
      loss='${config.lossName}',
      <#if (config.lossParams)??>
      ......
      src/main/resources/templates/gluon/pythonExecuteTest.ftl
      View file @ cdd27689
      ......@@ -6,19 +6,19 @@
      <#if tc.architectureOutputSymbols?size gt 1>
      <#assign outputName = tc.getNameWithoutIndex(tc.getName(tc.architectureOutputSymbols[0]))>
      ${outputName} = [mx.nd.zeros((test_batch_size, ${tc.join(tc.architectureOutputSymbols[0].ioDeclaration.type.dimensions, ", ")},), ctx=mx_context) for i in range(${tc.architectureOutputs?size?c})]
      ${outputName} = [mx.nd.zeros((batch_size, ${tc.join(tc.architectureOutputSymbols[0].ioDeclaration.type.dimensions, ", ")},), ctx=mx_context) for i in range(${tc.architectureOutputs?size?c})]
      <#else>
      <#list tc.architectureOutputSymbols as output>
      ${tc.getName(output)} = mx.nd.zeros((test_batch_size, ${tc.join(output.ioDeclaration.type.dimensions, ", ")},), ctx=mx_context)<#sep>,
      ${tc.getName(output)} = mx.nd.zeros((batch_size, ${tc.join(output.ioDeclaration.type.dimensions, ", ")},), ctx=mx_context)<#sep>,
      </#list>
      </#if>
      <#list tc.getLayerVariableMembers()?keys as member>
      ${member} = mx.nd.zeros((test_batch_size, ${tc.join(tc.cutDimensions(tc.getLayerVariableMembers()[member]), ", ")},), ctx=mx_context)
      ${member} = mx.nd.zeros((batch_size, ${tc.join(tc.cutDimensions(tc.getLayerVariableMembers()[member]), ", ")},), ctx=mx_context)
      </#list>
      <#list tc.architecture.constants as constant>
      ${tc.getName(constant)} = mx.nd.full((test_batch_size, 1,), ${constant.intValue?c}, ctx=mx_context)
      ${tc.getName(constant)} = mx.nd.full((batch_size, 1,), ${constant.intValue?c}, ctx=mx_context)
      </#list>
      nd.waitall()
      ......
      src/main/resources/templates/gluon/pythonExecuteTrain.ftl
      View file @ cdd27689
      ......@@ -6,19 +6,19 @@
      <#if tc.architectureOutputSymbols?size gt 1>
      <#assign outputName = tc.getNameWithoutIndex(tc.getName(tc.architectureOutputSymbols[0]))>
      ${outputName} = [mx.nd.zeros((train_batch_size, ${tc.join(tc.architectureOutputSymbols[0].ioDeclaration.type.dimensions, ", ")},), ctx=mx_context) for i in range(${tc.architectureOutputs?size?c})]
      ${outputName} = [mx.nd.zeros((batch_size, ${tc.join(tc.architectureOutputSymbols[0].ioDeclaration.type.dimensions, ", ")},), ctx=mx_context) for i in range(${tc.architectureOutputs?size?c})]
      <#else>
      <#list tc.architectureOutputSymbols as output>
      ${tc.getName(output)} = mx.nd.zeros((train_batch_size, ${tc.join(output.ioDeclaration.type.dimensions, ", ")},), ctx=mx_context)<#sep>,
      ${tc.getName(output)} = mx.nd.zeros((batch_size, ${tc.join(output.ioDeclaration.type.dimensions, ", ")},), ctx=mx_context)<#sep>,
      </#list>
      </#if>
      <#list tc.getLayerVariableMembers()?keys as member>
      ${member} = mx.nd.zeros((train_batch_size, ${tc.join(tc.cutDimensions(tc.getLayerVariableMembers()[member]), ", ")},), ctx=mx_context)
      ${member} = mx.nd.zeros((batch_size, ${tc.join(tc.cutDimensions(tc.getLayerVariableMembers()[member]), ", ")},), ctx=mx_context)
      </#list>
      <#list tc.architecture.constants as constant>
      ${tc.getName(constant)} = mx.nd.full((train_batch_size, 1,), ${constant.intValue?c}, ctx=mx_context)
      ${tc.getName(constant)} = mx.nd.full((batch_size, 1,), ${constant.intValue?c}, ctx=mx_context)
      </#list>
      nd.waitall()
      ......
      src/main/resources/templates/gluon/saveAttentionImageTest.ftl
      View file @ cdd27689
      if save_attention_image == "True":
      if not eval_train:
      import matplotlib
      matplotlib.use('Agg')
      import matplotlib.pyplot as plt
      logging.getLogger('matplotlib').setLevel(logging.ERROR)
      if(os.path.isfile('src/test/resources/training_data/Show_attend_tell/dict.pkl')):
      with open('src/test/resources/training_data/Show_attend_tell/dict.pkl', 'rb') as f:
      dict = pickle.load(f)
      plt.clf()
      fig = plt.figure(figsize=(15,15))
      max_length = len(labels)-1
      ax = fig.add_subplot(max_length//3, max_length//4, 1)
      ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(test_images[0+batch_size*(batch_i)].transpose(1,2,0))
      for l in range(max_length):
      attention = attentionList[l]
      ......@@ -15,14 +25,17 @@
      ax.set_title("<unk>")
      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))
      img = ax.imshow(test_images[0+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(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))
      img = ax.imshow(test_images[0+batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      plt.tight_layout()
      target_dir = 'target/attention_images'
      if not os.path.exists(target_dir):
      os.makedirs(target_dir)
      plt.savefig(target_dir + '/attention_test.png')
      plt.close()
      \ No newline at end of file
      src/main/resources/templates/gluon/saveAttentionImageTrain.ftl
      View file @ cdd27689
      ......@@ -4,16 +4,16 @@
      import matplotlib.pyplot as plt
      logging.getLogger('matplotlib').setLevel(logging.ERROR)
      plt.clf()
      fig = plt.figure(figsize=(15,15))
      max_length = len(labels)-1
      if(os.path.isfile('src/test/resources/training_data/Show_attend_tell/dict.pkl')):
      with open('src/test/resources/training_data/Show_attend_tell/dict.pkl', 'rb') as f:
      dict = pickle.load(f)
      plt.clf()
      fig = plt.figure(figsize=(15,15))
      max_length = len(labels)-1
      ax = fig.add_subplot(max_length//3, max_length//4, 1)
      ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(train_images[0+batch_size*(batch_i)].transpose(1,2,0))
      for l in range(max_length):
      attention = attentionList[l]
      ......@@ -24,12 +24,12 @@
      ax.set_title("<unk>")
      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))
      img = ax.imshow(train_images[0+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(labels[l+1][0].asscalar())])
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      img = ax.imshow(train_images[0+batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      plt.tight_layout()
      ......
      src/test/resources/target_code/CNNDataLoader_Alexnet.py
      View file @ cdd27689
      ......@@ -14,7 +14,7 @@ class CNNDataLoader_Alexnet:
      def __init__(self):
      self._data_dir = "data/Alexnet/"
      def load_data(self, train_batch_size, test_batch_size):
      def load_data(self, batch_size):
      train_h5, test_h5 = self.load_h5_files()
      train_data = {}
      ......@@ -38,11 +38,7 @@ class CNNDataLoader_Alexnet:
      train_iter = mx.io.NDArrayIter(data=train_data,
      label=train_label,
      batch_size=train_batch_size)
      train_test_iter = mx.io.NDArrayIter(data=train_data,
      label=train_label,
      batch_size=test_batch_size)
      batch_size=batch_size)
      test_iter = None
      ......@@ -63,9 +59,9 @@ class CNNDataLoader_Alexnet:
      test_iter = mx.io.NDArrayIter(data=test_data,
      label=test_label,
      batch_size=test_batch_size)
      batch_size=batch_size)
      return train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images
      return train_iter, test_iter, data_mean, data_std, train_images, test_images
      def load_data_img(self, batch_size, img_size):
      train_h5, test_h5 = self.load_h5_files()
      ......
      src/test/resources/target_code/CNNDataLoader_CifarClassifierNetwork.py
      View file @ cdd27689
      ......@@ -14,7 +14,7 @@ class CNNDataLoader_CifarClassifierNetwork:
      def __init__(self):
      self._data_dir = "data/CifarClassifierNetwork/"
      def load_data(self, train_batch_size, test_batch_size):
      def load_data(self, batch_size):
      train_h5, test_h5 = self.load_h5_files()
      train_data = {}
      ......@@ -38,11 +38,7 @@ class CNNDataLoader_CifarClassifierNetwork:
      train_iter = mx.io.NDArrayIter(data=train_data,
      label=train_label,
      batch_size=train_batch_size)
      train_test_iter = mx.io.NDArrayIter(data=train_data,
      label=train_label,
      batch_size=test_batch_size)
      batch_size=batch_size)
      test_iter = None
      ......@@ -63,9 +59,9 @@ class CNNDataLoader_CifarClassifierNetwork:
      test_iter = mx.io.NDArrayIter(data=test_data,
      label=test_label,
      batch_size=test_batch_size)
      batch_size=batch_size)
      return train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images
      return train_iter, test_iter, data_mean, data_std, train_images, test_images
      def load_data_img(self, batch_size, img_size):
      train_h5, test_h5 = self.load_h5_files()
      ......
      src/test/resources/target_code/CNNDataLoader_VGG16.py
      View file @ cdd27689
      ......@@ -14,7 +14,7 @@ class CNNDataLoader_VGG16:
      def __init__(self):
      self._data_dir = "data/VGG16/"
      def load_data(self, train_batch_size, test_batch_size):
      def load_data(self, batch_size):
      train_h5, test_h5 = self.load_h5_files()
      train_data = {}
      ......@@ -38,11 +38,7 @@ class CNNDataLoader_VGG16:
      train_iter = mx.io.NDArrayIter(data=train_data,
      label=train_label,
      batch_size=train_batch_size)
      train_test_iter = mx.io.NDArrayIter(data=train_data,
      label=train_label,
      batch_size=test_batch_size)
      batch_size=batch_size)
      test_iter = None
      ......@@ -63,9 +59,9 @@ class CNNDataLoader_VGG16:
      test_iter = mx.io.NDArrayIter(data=test_data,
      label=test_label,
      batch_size=test_batch_size)
      batch_size=batch_size)
      return train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images
      return train_iter, test_iter, data_mean, data_std, train_images, test_images
      def load_data_img(self, batch_size, img_size):
      train_h5, test_h5 = self.load_h5_files()
      ......
      src/test/resources/target_code/CNNSupervisedTrainer_Alexnet.py
      View file @ cdd27689
      ......@@ -180,13 +180,15 @@ class CNNSupervisedTrainer_Alexnet:
      num_epoch=10,
      eval_metric='acc',
      eval_metric_params={},
      eval_train=False,
      loss ='softmax_cross_entropy',
      loss_params={},
      optimizer='adam',
      optimizer_params=(('learning_rate', 0.001),),
      load_checkpoint=True,
      context='gpu',
      checkpoint_period=5,
      log_period=50,
      context='gpu',
      save_attention_image=False,
      use_teacher_forcing=False,
      normalize=True):
      ......@@ -212,10 +214,7 @@ class CNNSupervisedTrainer_Alexnet:
      del optimizer_params['step_size']
      del optimizer_params['learning_rate_decay']
      train_batch_size = batch_size
      test_batch_size = batch_size
      train_iter, train_test_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(train_batch_size, test_batch_size)
      train_iter, test_iter, data_mean, data_std, train_images, test_images = self._data_loader.load_data(batch_size)
      if normalize:
      self._net_creator.construct(context=mx_context, data_mean=data_mean, data_std=data_std)
      ......@@ -274,10 +273,11 @@ class CNNSupervisedTrainer_Alexnet:
      else:
      logging.error("Invalid loss parameter.")
      speed_period = 50
      tic = None
      for epoch in range(begin_epoch, begin_epoch + num_epoch):
      loss_total = 0
      train_iter.reset()
      for batch_i, batch in enumerate(train_iter):
      with autograd.record():
      ......@@ -285,7 +285,7 @@ class CNNSupervisedTrainer_Alexnet:
      data_ = batch.data[0].as_in_context(mx_context)
      predictions_ = mx.nd.zeros((train_batch_size, 10,), ctx=mx_context)
      predictions_ = mx.nd.zeros((batch_size, 10,), ctx=mx_context)
      nd.waitall()
      ......@@ -302,33 +302,39 @@ class CNNSupervisedTrainer_Alexnet:
      loss.backward()
      loss_total += loss.sum().asscalar()
      for trainer in trainers:
      trainer.step(batch_size)
      if tic is None:
      tic = time.time()
      else:
      if batch_i % speed_period == 0:
      if batch_i % log_period == 0:
      try:
      speed = speed_period * batch_size / (time.time() - tic)
      speed = log_period * batch_size / (time.time() - tic)
      except ZeroDivisionError:
      speed = float("inf")
      logging.info("Epoch[%d] Batch[%d] Speed: %.2f samples/sec" % (epoch, batch_i, speed))
      loss_avg = loss_total / (batch_size * log_period)
      loss_total = 0
      logging.info("Epoch[%d] Batch[%d] Speed: %.2f samples/sec Loss: %.5f" % (epoch, batch_i, speed, loss_avg))
      tic = time.time()
      tic = None
      train_test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(train_test_iter):
      if True:
      if eval_train:
      train_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(train_iter):
      labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
      data_ = batch.data[0].as_in_context(mx_context)
      predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context)
      predictions_ = mx.nd.zeros((batch_size, 10,), ctx=mx_context)
      nd.waitall()
      ......@@ -346,16 +352,16 @@ class CNNSupervisedTrainer_Alexnet:
      import matplotlib.pyplot as plt
      logging.getLogger('matplotlib').setLevel(logging.ERROR)
      plt.clf()
      fig = plt.figure(figsize=(15,15))
      max_length = len(labels)-1
      if(os.path.isfile('src/test/resources/training_data/Show_attend_tell/dict.pkl')):
      with open('src/test/resources/training_data/Show_attend_tell/dict.pkl', 'rb') as f:
      dict = pickle.load(f)
      plt.clf()
      fig = plt.figure(figsize=(15,15))
      max_length = len(labels)-1
      ax = fig.add_subplot(max_length//3, max_length//4, 1)
      ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(train_images[0+batch_size*(batch_i)].transpose(1,2,0))
      for l in range(max_length):
      attention = attentionList[l]
      ......@@ -366,12 +372,12 @@ class CNNSupervisedTrainer_Alexnet:
      ax.set_title("<unk>")
      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))
      img = ax.imshow(train_images[0+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(labels[l+1][0].asscalar())])
      img = ax.imshow(train_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      img = ax.imshow(train_images[0+batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      plt.tight_layout()
      ......@@ -381,25 +387,27 @@ class CNNSupervisedTrainer_Alexnet:
      plt.savefig(target_dir + '/attention_train.png')
      plt.close()
      predictions = []
      for output_name in outputs:
      if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1:
      predictions.append(mx.nd.argmax(output_name, axis=1))
      else:
      predictions.append(output_name)
      predictions = []
      for output_name in outputs:
      if mx.nd.shape_array(mx.nd.squeeze(output_name)).size > 1:
      predictions.append(mx.nd.argmax(output_name, axis=1))
      else:
      predictions.append(output_name)
      metric.update(preds=predictions, labels=labels)
      train_metric_score = metric.get()[1]
      metric.update(preds=predictions, labels=labels)
      train_metric_score = metric.get()[1]
      else:
      train_metric_score = 0
      test_iter.reset()
      metric = mx.metric.create(eval_metric, **eval_metric_params)
      for batch_i, batch in enumerate(test_iter):
      if True:
      if True:
      labels = [batch.label[i].as_in_context(mx_context) for i in range(1)]
      data_ = batch.data[0].as_in_context(mx_context)
      predictions_ = mx.nd.zeros((test_batch_size, 10,), ctx=mx_context)
      predictions_ = mx.nd.zeros((batch_size, 10,), ctx=mx_context)
      nd.waitall()
      ......@@ -412,12 +420,22 @@ class CNNSupervisedTrainer_Alexnet:
      if save_attention_image == "True":
      if not eval_train:
      import matplotlib
      matplotlib.use('Agg')
      import matplotlib.pyplot as plt
      logging.getLogger('matplotlib').setLevel(logging.ERROR)
      if(os.path.isfile('src/test/resources/training_data/Show_attend_tell/dict.pkl')):
      with open('src/test/resources/training_data/Show_attend_tell/dict.pkl', 'rb') as f:
      dict = pickle.load(f)
      plt.clf()
      fig = plt.figure(figsize=(15,15))
      max_length = len(labels)-1
      ax = fig.add_subplot(max_length//3, max_length//4, 1)
      ax.imshow(test_images[0+test_batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(test_images[0+batch_size*(batch_i)].transpose(1,2,0))
      for l in range(max_length):
      attention = attentionList[l]
      ......@@ -428,15 +446,18 @@ class CNNSupervisedTrainer_Alexnet:
      ax.set_title("<unk>")
      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))
      img = ax.imshow(test_images[0+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(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))
      img = ax.imshow(test_images[0+batch_size*(batch_i)].transpose(1,2,0))
      ax.imshow(attention_resized, cmap='gray', alpha=0.6, extent=img.get_extent())
      plt.tight_layout()
      target_dir = 'target/attention_images'
      if not os.path.exists(target_dir):
      os.makedirs(target_dir)
      plt.savefig(target_dir + '/attention_test.png')
      plt.close()
      ......