total_float_ops is 0 by tf.profiler.profile from tensorflow

See also questions close to this topic

• Python Kafka Streaming API - Binning

  I am using python kafka stream binning example given in this, Python Kafka Streaming API

  I am able to generate the data using generator.py file given under winton-kafka-streams/examples/binning/, whereas when i run the binning.py file from the same folder, i got the below issue. Could someone help me, to resolve this?

  

  
• Change color of missing values in Seaborn heatmap

  Consider the example of missing values in the Seaborn documentation:

  corr = np.corrcoef(np.random.randn(10, 200))
  mask = np.zeros_like(corr)
  mask[np.triu_indices_from(mask)] = True
  sns.heatmap(corr, mask=mask, vmax=.3, square=True)
  

  

  How do I change the color of the missing values to, for example, black? The color of the missing values should be specified independent of the color scheme of the heatmap, it may not be present in the color scheme.

  I tried adding facecolor = 'black' but that didn't work. The color can be affected by e.g. sns.axes_style("white") but it isn't clear to me how that can be used to set an arbitrary color.
• Xpath + Scrapy + Python : data point couldn't be scraped

  This is the XML structure:

  <tr>
     <td>
         <font size="3">
             <strong>Location:</strong> 
             Hiranandani Gardens, Powai
         </font>
     </td> 
  </tr>  
  

  I want to extract : Hiranandani Gardens, Powai

  I tried with these:

  response.xpath('//font[@size="3"]/strong[text()="Location:"]/text()').extract()
  
  response.xpath('//font[@size="3"]/strong[text()="Location:"]').extract()
  

  Both returned an empty list.

  Note: we must have to use the text of tag, i.e., "Location:". Otherwise, there are many other places on the site where the same XML structure is used. So, it'll fetch many more unnecessary things apart from the desired value if the text of strong tag is not used.
• Keras InvalidArgumentError: Incompatible shapes: [1,8,32] vs. [1,10,32]

  It stops training at the beginning of the first epoch.

  The error is:

  InvalidArgumentError: Incompatible shapes: [1,8,32] vs. [1,10,32]
       [[Node: training_8/RMSprop/gradients/loss_8/time_distributed_9_loss/mul_grad/BroadcastGradientArgs = BroadcastGradientArgs[T=DT_INT32, _class=["loc:@train.../Reshape_1"], _device="/job:localhost/replica:0/task:0/device:CPU:0"](training_8/RMSprop/gradients/loss_8/time_distributed_9_loss/mul_grad/Shape, training_8/RMSprop/gradients/loss_8/time_distributed_9_loss/mul_grad/Shape_1)]]
  

  I'm not sure if the error might be the format of the training data. This is a sample of train_x:

  [array([ 0,  1,  2,  3,  4,  5,  6,  3,  7,  8,  9, 10, 11, 12, 13, 14], dtype=int32), array([15, 16, 17, 18, 19], dtype=int32), array([20, 16, 17, 18, 21, 22, 23, 24, 25, 26], dtype=int32), array([27,  1, 17, 28, 18, 29, 30, 31, 24, 32], dtype=int32), array([33,  1, 17,  3, 34, 35, 36, 37, 18, 38], dtype=int32), array([39, 16, 40, 28, 41, 42], dtype=int32), array([39,  1, 40, 28, 41, 43], dtype=int32), array([44,  6,  3, 45], dtype=int32), array([15, 16, 40, 46, 47, 48,  3, 49, 50, 51, 52, 53], dtype=int32), array([ 0, 54, 28, 55, 56, 57, 58, 59], dtype=int32)]
  

  This is a sample of train_label

  [array([0, 1, 2, 1, 2, 3, 0, 1, 4, 2, 2, 4, 5, 6, 0, 7], dtype=int32), array([0, 5, 8, 9, 9], dtype=int32), array([10,  5,  8,  9,  7,  9,  7,  7], dtype=int32), array([10,  1,  8,  1,  9,  9,  3,  0,  7,  2,  7], dtype=int32), array([10,  1,  8,  1,  9,  9,  9,  7,  9,  7], dtype=int32), array([0, 5, 8, 1, 2, 2], dtype=int32), array([0, 1, 8, 1, 2, 2], dtype=int32), array([11,  0,  1,  9], dtype=int32), array([ 0,  5,  8,  7, 12, 13,  1,  2,  9,  9,  5,  6], dtype=int32), array([ 0, 14,  1,  2,  2, 12,  4, 15], dtype=int32)]
  

  This is the code:

  n_vocab = len(unique_words) #1517
  model = Sequential()
  model.add(Embedding(n_vocab,100))
  model.add(Convolution1D(128, 5, border_mode='same', activation='relu'))
  model.add(Dropout(0.25))
  model.add(GRU(100,return_sequences=True))
  model.add(TimeDistributed(Dense(n_classes, activation='softmax')))
  model.compile('rmsprop', 'categorical_crossentropy')
  
  n_epochs = 30
  train_x = [np.asarray(x, dtype=np.int32) for x in encoded_sentences]
  train_label = [np.asarray(x, dtype=np.int32) for x in encoded_POS]
  
  for i in range(n_epochs):
      print("Training epoch {}".format(i))
  
      bar = progressbar.ProgressBar(maxval=len(train_x))
      for n_batch, sent in bar(enumerate(train_x)):
          label = train_label[n_batch]
          # Make labels one hot
          label = np.eye(n_classes)[label][np.newaxis,:]
          # View each sentence as a batch
          sent = sent[np.newaxis,:]
  
          if sent.shape[1] > 1: #ignore 1 word sentences
              model.train_on_batch(sent, label)
  
• Is there a way to name a tensorflow variable based on the value of another tensor-variable

  I want to be able to do the following

  n = str(tf.constant(2))
  v = tf.get_variable(name=n, shape=(256,256), initializer=tf.contrib.layers.xavier_initializer())
  

  but doing this is converts n to a string representation of tensorflow variable i.e

  "<tf.Tensor 'Const_4:0' shape=() dtype=int32>"
  
• How To Let Tensorflow Use All CPU

  My Linux has 2 CPUs (16 cores per CPU). Each core has 2 threads.

  CPU usage rate only 50% less if i use tf.Session(tf.ConfigProto())

  CPU usage rate only 40% less if i use tf.Session(tf.ConfigProto(device_count={"CPU": 64}))

  Cpu usage rate is 30-70% if i use tf.ConfigProto(device_count={"CPU": 32}, inter_op_parallelism_threads=1, intra_op_parallelism_threads=64, )

  And in this configuration all cores are working, but the usage rate is only 30-80%

  I have tested in Windows Server (1 cpu / 4 cores). Cpu usage rate is more than 90%.

  what should I code can let Tensorflow use all cpu?
• Dataset containing spatial and temporal features (built on a CNN model)

  A dataset contains spatial and temporal features.

  It contains the time series data (2 min intervals) of the sections of a map.

  It is 320*480 (320 map sections and 480-time intervals). Each row belongs to a section and each column is a time interval. So each cell in the dataset is a value of data in a map section in a time interval.

  I would like to make a model to import all map sections in 40 min and predict the following 10 next min of all map sections.

  So the input shape equals 320*20 and the output shape will be 320*5.

  I split data to 320*20 as X and 320*5 as Y samples.

  The input and the output of the model are 2-dimensional matrixes.

  I can consider the input as a one channel image (320*20*1) and it is possible to use a CNN model.

  The codes of the final architecture of my model in Keras:

  model = models.Sequential()
  model.add(Conv2D(256,(3, 3),
               activation='relu',
               input_shape=(320,20,1), padding='same'))
  model.add(MaxPooling2D((2, 2)))
  
  model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
  model.add(MaxPooling2D((2, 2)))
  
  model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
  model.add(MaxPooling2D((2,2)))
  
  model.add(Flatten())
  model.add(Dense(1600))
  
  model.summary()
  

  The Loss value(MSE) is about 70. Is it lower than the random prediction?

  I reshaped the output to 1600 (320*5) which the final Dense layer expects, while the output is 2d. Does it make the model more prone to error?

  Thank you
• Which is best way of logging to file in C?

  I'm dealing with some deep learning model written in C.
  I wanna make some log file to check it later.
  log will be a line for each step.
  Each step will take few seconds.

  Sometimes I use keyboard interrupt to stop procedure

  Ways I thought are:

  // Way 1
  fp = fopen("log.txt", "a");
  for each step:
    fprintf(fp, "Log content\n");  
  flose(fp);
  

  I think way 1 may have lower file open/close overhead.
  But when I use keyboard interrupt to stop procedure, log file will be never closed properly.
  Is it OK?
  Or can pass file pointer as argument of the my own signal handler?

  // Way 2
  for each step:
    fp = fopen("log.txt", "a");
    fprintf(fp, "Log content\n");  
    fclose(fp);
  

  I think way 2 will have file open/close overhead for each step, could this slow down whole performance?
  Could it be crucial?

  // Way 3
  for each step:
    fprintf(buffer, "### Log content ###");  
  
    if step % 100 == 0:
      fp = fopen("log.txt", "a");
      fprintf(fp, buffer);  
      fclose(fp);
      flush buffer;
  

  for way 3, i consider 2 ways of buffer:
  1. array of string
  2. one long string with line feed between items

  Which way works best?
  It depends?

  Or if there is a good library for logging, could you recommend it to me?
• Explanation of tensorflow "dataflow graphs"

  Google gave the following dataflow graph as an example without any explanation of the scenario itself (https://www.tensorflow.org/guide/graphs).

  I cannot understand the use case of such a graph. Why do we need a Logit Layer on top of ReLu layer? What's the use of Softmax (cannot see any link between the output and other nodes)? What are the meanings of the four parameters (two weights and two biases)? I would like to see a real case which matches with this datagraph.