See also questions close to this topic

• Only download certain label tf dataset

  Looking to do some fine tuning. The dataset (found here: https://knowyourdata-tfds.withgoogle.com/#dataset=sun397&filters=kyd%2Fsun397%2Flabel:%2Fh%2Fhouse&tab=ITEM&select=kyd%2Fsun397%2Flabel&item=%2Fh%2Fhouse%2Fsun_blpzjomvikwtulrq.jpg&expanded_groups=sun397) that Im trying to finetune w is pretty large and i just want to use/download the images with label /h/house. Any tips on how I can best accomplish this? Thanks!

  import tensorflow as tf
  import tensorflow_hub as hub
  import tensorflow_datasets as tfds
  import numpy as np
  import matplotlib.pyplot as plt
  import functools
  import pandas
  
  (train_ds, valid_ds), info = tfds.load("sun397", split=["train", "validation"], as_supervised=True, with_info=True, label = "/h/house")
  int_to_class_label = info.features['label'].int2str
  
  
• TFF: How can I train any model using a server running tff-runtime and a client running tff-client?

  I read all the tensorflow-federated tutorials, including this one https://www.tensorflow.org/federated/gcp_setup, but I couldn't understand how to use this for training a model.

  I'm doing a graduation project, to start I need to do this POC using tensorflow-federated to train a model with one server and one client in order to apply cross-silo setup for recognition of organs affected by covid in the future. If anyone can point me a direction, I'd be very grateful.
• Can't use Keras MeanIoU to train semantic segmentation model

  I'm working on a binary semantic segmentation problem. I built an UNet model with MobileNetV2 backbone. Here is my model code:

  def upsample(filters, size, apply_dropout=False):
      initializer = tf.random_normal_initializer(0., 0.02)
      layer = Sequential()
      layer.add(layers.Conv2DTranspose(filters, size, strides=2, padding='same', kernel_initializer=initializer,
                                       use_bias=False))
      layer.add(layers.BatchNormalization())
      if apply_dropout:
          layer.add(layers.Dropout(0.5))
      layer.add(layers.ReLU())
      return layer
  
  
  def UNet(image_size, num_classes):
      inputs = Input(shape=image_size + (3,))
  
      base_model = applications.MobileNetV2(input_shape=image_size + (3,), include_top=False)
      layer_names = [
          'block_1_expand_relu',
          'block_3_expand_relu',
          'block_6_expand_relu',
          'block_13_expand_relu',
          'block_16_project',
      ]
      base_model_outputs = [base_model.get_layer(name).output for name in layer_names]
      down_stack = Model(inputs=base_model.input, outputs=base_model_outputs)
      down_stack.trainable = False
  
      up_stack = [
          upsample(512, 3),
          upsample(256, 3),
          upsample(128, 3),
          upsample(64, 3)
      ]
  
      skips = down_stack(inputs)
      x = skips[-1]
      skips = reversed(skips[:-1])
  
      for up, skip in zip(up_stack, skips):
          x = up(x)
          x = layers.Concatenate()([x, skip])
  
      outputs = layers.Conv2DTranspose(filters=num_classes, kernel_size=3, strides=2, padding='same')(x)
  
      return Model(inputs, outputs)
  

  To load the images and masks for training, I built an image loader inherits from keras.Sequnce.

  class ImageLoader(utils.Sequence):
  
      def __init__(self, batch_size, img_size, img_paths, mask_paths):
          self.batch_size = batch_size
          self.img_size = img_size
          self.img_paths = img_paths
          self.mask_paths = mask_paths
  
      def __len__(self):
          return len(self.mask_paths) // self.batch_size
  
      def __getitem__(self, idx):
          i = idx * self.batch_size
          batch_img_paths = self.img_paths[i:i + self.batch_size]
          batch_mask_paths = self.mask_paths[i:i + self.batch_size]
  
          x = np.zeros((self.batch_size,) + self.img_size + (3,), dtype='float32')
          for j, path in enumerate(batch_img_paths):
              img = utils.load_img(path, target_size=self.img_size)
              img = utils.img_to_array(img)
              x[j] = img
  
          y = np.zeros((self.batch_size,) + self.img_size + (1,), dtype='uint8')
          for j, path in enumerate(batch_mask_paths):
              img = utils.load_img(path, target_size=self.img_size, color_mode='grayscale')
              img = utils.img_to_array(img)
              # [0, 255] -> [0, 1]
              img //= 255
              y[j] = img
  
          return x, y
  

  In my segmentation problem, all the labels are in the range [0, 1]. However, when I try to compile and then fit the model using Adam optimizer, Sparse categorical cross entropy loss and metric tf.keras.metrics.MeanIoU, I encountered with the following problem:

  Node: 'confusion_matrix/assert_non_negative_1/assert_less_equal/Assert/AssertGuard/Assert'
  2 root error(s) found.
    (0) INVALID_ARGUMENT:  assertion failed: [`predictions` contains negative values.  ] [Condition x >= 0 did not hold element-wise:] [x (confusion_matrix/Cast:0) = ] [-1 -1 -1...]
       [[{{node confusion_matrix/assert_non_negative_1/assert_less_equal/Assert/AssertGuard/Assert}}]]
       [[confusion_matrix/assert_less_1/Assert/AssertGuard/pivot_f/_31/_67]]
    (1) INVALID_ARGUMENT:  assertion failed: [`predictions` contains negative values.  ] [Condition x >= 0 did not hold element-wise:] [x (confusion_matrix/Cast:0) = ] [-1 -1 -1...]
       [[{{node confusion_matrix/assert_non_negative_1/assert_less_equal/Assert/AssertGuard/Assert}}]]
  

  At first, I used accuracy as a metrics for training and I didn't encounter this problem, however when I changed to MeanIoU, this problem happened. Does anyone know how to fix this problem? Thank you very much!

  UPDATE: I've searched on StackOverflow and found this question about a similar error, however the fix mentioned in that link (reduce learning rate) doesn't work in my case.
• how to print all parameters of a keras model

  I am trying to print all the 1290 parameters in dense_1 layer, but model.get_weights()[7] only show 10 parameters. How could I print all the 1290 parameters of dense_1 layer? What is the difference between model.get_weights() and model.layer.get_weights()

  >model.get_weights()[7]
  array([-2.8552295e-04, -4.3254648e-03, -1.8752701e-04,  2.3482188e-03,
         -3.4848123e-04,  7.6121779e-04, -2.7494309e-06, -1.9068648e-03,
          6.0777756e-04,  1.9550985e-03], dtype=float32)
  
  >model.summary()
  Model: "sequential"
  _________________________________________________________________
  Layer (type)                Output Shape              Param #   
  =================================================================
   conv2d (Conv2D)             (None, 26, 26, 32)        320       
                                                                   
   conv2d_1 (Conv2D)           (None, 24, 24, 64)        18496     
                                                                   
   max_pooling2d (MaxPooling2D  (None, 12, 12, 64)       0         
   )                                                               
                                                                   
   dropout (Dropout)           (None, 12, 12, 64)        0         
                                                                   
   flatten (Flatten)           (None, 9216)              0         
                                                                   
   dense (Dense)               (None, 128)               1179776   
                                                                   
   dropout_1 (Dropout)         (None, 128)               0         
                                                                   
   dense_1 (Dense)             (None, 10)                1290      
  _________________________________________________________________
                                                                   
  =================================================================
  Total params: 1,199,882
  Trainable params: 1,199,882
  Non-trainable params: 0
  _________________________________________________________________
  
• Training plot is not appearing properly for keras model

  I have data where I need to train it with X and Y. Traning part is done but when I want to plot the prediction and actual data, it is appearing with so many lines instead of showing just non-linear regression line.

  model= Sequential()
  model.add(Dense(7,input_dim=1, activation="tanh"))
  model.add(Dense(1))
  
  model.compile(loss="mse", 
  optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), metrics= 
  ["mae"])
  history=model.fit(X,Y,epochs=1000)
  
  predict=model.predict(X)
  plt.scatter(X, Y,edgecolors='g')
  plt.plot(X, predict,'r')
  plt.legend([ 'Predictated Y' ,'Actual Y'])
  plt.show()
  

  Please see the attached imageplotting image
• Unhandled exception at 0x00007FFABE6A9538 (cudnn_cnn_infer64_8.dll) in Onnx.exe

  I am using onnxrutime-gpu for running object detection model in C++. I installed onnxruntime GPU version 1.6.0. I am using it in visual studio 2019. But no matter what version I use, I am getting this error "Unhandled exception at 0x00007FFABE6A9538 (cudnn_cnn_infer64_8.dll) in Onnx.exe". Model is loaded successfully with onnxruntime-gpu , while performing the inference, it gives this error. Please help me figure this out. Model can be loaded and run successfully with onnxruntime CPU.
• How to convert TensorFlow 2 saved model to be used with OpenCV dnn.readNet

  I am struggling to find a way to convert my trained network using TensorFlow 2 Object detection API to be used with OpenCV for deployment purposes. I tried two methods for that but without success. Could someone help me resolve this issue or propose the best and easy deep learning framework to convert my model to OpenCV (OpenCV friendly)? I really appreciate any help you can provide.

  This is my information system

  OS Platform: Windows 10 64 bits

  Tensorflow Version: 2.8

  Python version: 3.9.7

  OpenCV version: 4.5.5

  1st Method: Using tf2onnx

  I used the following code since I am using TensorFlow 2

  python -m tf2onnx.convert --saved-model tensorflow-model-path --output model.onnx --opset 15
  

  The conversion process generates the model.onnx successfully and returns the following:

  However, when I try to read the converted model, I get the following error:

  File "C:\Tensorflow\testcovertedTF2ToONNX.py", line 10, in <module> net = cv2.dnn.readNetFromONNX('C:/Tensorflow/model.onnx') cv2.error: Unknown C++ exception from OpenCV code
  
  

  The code used to read the converted network is simple.

  import cv2
  import numpy as np
   
  image = cv2.imread("img002500.jpg")
  if image is None:
      print("image emplty")
  image_height, image_width, _ = image.shape
  net = cv2.dnn.readNetFromONNX('model.onnx')
  image = image.astype(np.float32)
  
  input_blob = cv2.dnn.blobFromImage(image, 1, (640,640), 0, swapRB=False, crop=False)
  net.setInput(input_blob)
  output = net.forward()
  
  

  2nd Method: Trying to get Frozen graph from saved model

  I tried to get frozen_graph.pb from my saved_model using the script below, found in
  https://github.com/opencv/opencv/issues/16879#issuecomment-603815872

  import tensorflow as tf
  print(tf.__version__)
  
  from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
  
  loaded = tf.saved_model.load('models/mnist_test')
  infer = loaded.signatures['serving_default']
  
  f = tf.function(infer).get_concrete_function(input_tensor=tf.TensorSpec(shape=[None, 640, 640, 3], dtype=tf.float32))
  f2 = convert_variables_to_constants_v2(f)
  graph_def = f2.graph.as_graph_def()
  
  # Export frozen graph
  with tf.io.gfile.GFile('frozen_graph.pb', 'wb') as f:
     f.write(graph_def.SerializeToString())
  
  

  Then, I tried to generate the text graph representation (graph.pbtxt) using tf_text_graph_ssd.py found in https://github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API

  python tf_text_graph_ssd.py --input path2frozen_graph.pb --config path2pipeline.config --output outputgraph.pbtxt
  

  The execution of this script returns the following error:

  cv.dnn.writeTextGraph(modelPath, outputPath)
  cv2.error: OpenCV(4.5.5) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\tensorflow\tf_graph_simplifier.cpp:1052: error: (-215:Assertion failed) permIds.size() == net.node_size() in function 'cv::dnn::dnn4_v20211220::sortByExecutionOrder'
  
  During the handling of the above exception, another exception occurred:
  
  Traceback (most recent call last):
    File "C:\Tensorflow\generatepBtxtgraph\tf_text_graph_ssd.py", line 413, in <module>
      createSSDGraph(args.input, args.config, args.output)
    File "C:\Tensorflow\generatepBtxtgraph\tf_text_graph_ssd.py", line 127, in createSSDGraph
      writeTextGraph(modelPath, outputPath, outNames)
    File "C:\Tensorflow\generatepBtxtgraph\tf_text_graph_common.py", line 320, in writeTextGraph
      from tensorflow.tools.graph_transforms import TransformGraph
  ModuleNotFoundError: No module named 'tensorflow.tools.graph_transforms'
  
  

  Trying to read the generated frozen model without a graph.pb using dnn.readNet the code below:

  import cv2
  import numpy as np
   
  image = cv2.imread("img002500.jpg")
  if image is None:
      print("image emplty")
  image_height, image_width, _ = image.shape
  net = cv2.dnn.readNet('frozen_graph_centernet.pb')
  image = image.astype(np.float32)
  # create blob from image (opencv dnn way of pre-processing)
  input_blob = cv2.dnn.blobFromImage(image, 1, (1024,1024), 0, swapRB=False, crop=False)
  net.setInput(input_blob)
  output = net.forward()
  
  

  returns the following error

  Traceback (most recent call last):
    File "C:\Tensorflow\testFrozengraphTF2.py", line 14, in <module>
      output = net.forward()
  cv2.error: OpenCV(4.5.5) D:\a\opencv-python\opencv-python\opencv\modules\dnn\src\dnn.cpp:621: error: (-2:Unspecified error) Can't create layer "StatefulPartitionedCall" of type "StatefulPartitionedCall" in function 'cv::dnn::dnn4_v20211220::LayerData::getLayerInstance'
  
  

  I understand that OpenCV doesn't import models with StatefulPartitionedCall (TF Eager mode). Unfortunately, this means the script found to export my saved model to frozen_graph did not work.

  saved model

  you can get my saved model from the link below

  https://www.dropbox.com/s/liw5ff87rz7v5n5/my_model.zip?dl=0

  #note: the exported model works well with the TensorFlow script