You may encounter an error indicating an error while fetching gradients. It turns out there are several steps you can take to fix this problem. We will do it shortly.

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    Optimizer.Method compute_gradients () returns a catalog of pairs ( Tensor , Variable ), with almost every gradient tensor being equal to obey the corresponding variable.

    Session.run () list of thoughts on Tensor objects (or bits and pieces that can be converted to Tensor ) into first and foremost is the product argument. It doesn’t know how to handle the list of pairs, so you get a brand new TypeError which is trying to sess.run (gradstep, ...) . train

    The correct decision depends on what you want to do. If you want to get almost all of the gradient values, here’s hthen you can do:

    What is gradient failure in neural networks?

    He describes this situation where repetitive or deep multilayer neural interaction fails to convey useful tilt information from the output end of the model to the layers of cells near the input end of the model.

      gradient_val = sessions.run ([grad required for grad, _ in gradstep], feed_dict = x: batch_x, batch_y)# y: Then, for example, nuild flexible a dictionary of names and gradients.var_to_grad =for grad_val, (_, in var) zip (grad_val, gradstep):    var_to_grad [var.name] = then you grad_val 

    How do I calculate the gradients of output of a model?

    To find the gradients, we first need to find the output tensor. For design output (this was my original question) we will of course call model.output. We can also find output gradients for similar layers using model.layers [index] .output. to call

    If you want, you can make the following fashionable incarnation separately:

      sessions.run ([var for _, var found in gradstep]) 

    … although this – ignoring other changes in your current program – only returns the first values ​​for each variable.You must run the optimizer training maneuver (or call Optimizer.apply_gradients () ) to update the variables.

    Using the “old” Keras library, I created heatmaps to support my CNNs using keras.backend.Function, gradient ()

      # Load the model and image, then predict the class this image should be assigned tomodel = load_model (os.path.join (model_folder, "custom_model.h5"))image = image.load_img (image_path)img_tensor implies image.img_to_array (image)img_tensor = np.expand_dims (img_tensor, = axis = 0)img_tensor preprocess_input (img_tensor)preds implies model.predict (img_tensor)model_prediction = model.output [:, np.argmax (preds [0])]# Group calculation of points by heatmapconv_layer means model.get_layer ("block5_conv3") # last conv.grads corresponds to K.gradients (model_prediction, conv_layer.output) [0]pooled_grads = K.Axis = (0, intermediate (diplomas, one, 2))# Alumni values ​​in aggregate and model conv. Call back. You areNumpy layer code as tablesinput_layer = model.get_layer ("model_input")iterate = K.function ([input_layer], [pooled_grads, conv_layer.output [0]])pooled_grads_value, conv_layer_output_value = continue iteration ([img_tensor])# with heatmap generation ... 

    error calling gradients for

    I now turn to TF2. And 0 is a built-in Keras implementation. Everything works fine, but if you use some code you will get the following error when you call K.gradients () :

      tf.gradients should not be used if supported fast execution is possible. Use tf.GradientTape instead. 

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  • I’ve done scientific testing to try and figure out how to use GradientTape , but unfortunately I don’t know much about TF or TF2.0 – I’m sure you’ve worked with Keras. Can you help me recreate this gradient calculation from my setup?

      # also load the model image, then predict the class the image belongs tomodel = load_model (os.path.join (model_folder, "custom_model.h5"))image matches image.load_img (image_path)img_tensor = image.img_to_array (image)img_tensor is equal to np.expand_dims (img_tensor, = axis = 0)img_tensor preprocess_input (img_tensor)preds = model.predict (img_tensor)model_prediction is equal to model.output [:, np.argmax (preds [0])]# Unified calculation of graduates with heat mapconv_layer = model.get_layer ("block5_conv3") # last conv.grads = K.gradients (model_prediction, conv_layer.output) [0]pooled_grads implies K.Axis = (0, average (diplomas, 1, 2))# Get treasures from United Alumni and the Conv brand. Numpy layer output as tablesinput_layer corresponds to model.get_layer ("model_input")iterate = K.function ([input_layer], [pooled_grads, conv_layer.output [0]])pooled_grads_value, conv_layer_output_value equals Continue Iteration ([img_tensor])# with the heatmap version ... 
      tf.gradients is not available if the standard runtime is enabled. Use tf.GradientTape instead. 

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    Gelöst: Vorschläge Zur Behebung Von Fehlern Im Zusammenhang Mit Der Verwendung Von Farbverläufen Für
    Résolu : Suggestions Pour Corriger Les Bugs D’accidents De La Route Liés à L’utilisation Des Dégradés De Coloration Pour
    Opgelost: Suggesties Voor Het Oplossen Van Glitches Met Betrekking Tot Het Gebruik Van Kleurovergangen Voor
    Решено: предложения по исправлению ошибок идентичны использованию цветовых градиентов для
    Resuelto: Sugerencias Para Solucionar Problemas Relacionados Con El Uso De Degradados De Color Para
    Resolvido: Sugestões Para Corrigir Bugs Que Acompanham O Uso De Gradientes De Cor Para
    Risolto: Suggerimenti Per La Correzione Di Bug Relativi Alla Produzione Dell’uso Di Sfumature Di Colore Per
    Löst: Förslag För Att åtgärda Buggar Associerade Med Att Använda Färggradienter För
    해결: 음영 그라디언트 사용과 관련된 버그 수정을 위한 제안
    Rozwiązano: Sugestie Dotyczące Naprawy Błędów Związanych Z Wybieraniem Gradientów Kolorów Dla