The following code works without the attention thing, however when I add it as seen below I get the following error: 61 62 decoder1 = layers.Reshape(target_shape=(4,512))(decoder1) —> 63 attention = layers.Attention()([encoder,decoder1])(decoder1) 64 attention = layers.Concatenate()([encoder, decoder1])(attention) 65 TypeError: ‘KerasTensor’ object is not callable The code: encoder = layers.Reshape(target_shape=(4,512))(encoder) decoder1 = layers.LSTM(2048)(encoder) decoder1 = layers.Reshape(target_shape=(4,512))(decoder1) attention ..
Category : tensorflow
I am using mediapipe library for Pose detection using python. I am following the below link to give GPU support for my mediapipe program for pose detection. [this is the link for the tutorial for giving GPU suppport for google mediapipe.2 I have completed some initial steps but I am unable to understand the steps ..
So as the title says, if I the following Python code on Spyder my machine freezes on the first epoch. What happens is: RAM usage goes to ~94%, after a while Spyder spits out a problem window but I get no error message in the console. The code just freezes. I am doing this with ..
What is the encoding here tf.Tensor( [b"ROMEO:nThe glory of comfort, that which I durst not,nI painted his action again.nCome, cousin, forward to London,nTo I occuped to such a man of fable.nBut, by my grave, look for life; forgot my sweet news,nBy any in the while; by brief when traitor, they they themselves,nHe water in heaven ..
I’m new to Tensorflow and Python and I have been following this tutorial, specifically along Titanic section. However, the data I was using was data of XYZ position of 20 joints captured from Kinect. I have saved my model with model.save but when I tried to load the model, I got the following error: ValueError ..
Currently using these Dependencies from these snapshot repositories To try and use on device training. As shown in this article by tf https://www.tensorflow.org/lite/examples/on_device_training/overview. When I try initialising the tflite interpreter I am getting "java.lang.IllegalArgumentException: Internal error: Cannot create interpreter: Op builtin_code out of range: 142. Are you using old TFLite binary with newer model? Registration ..
I have a question on how to load models weight if the .h5 file is inside the zip file. I am intended to load my machine learning model without extracting the zip file. I have a zip file called ‘model.zip’ now, and inside has ‘model.json’ and ‘model.h5’ I first use the below method to call ..
x = tf.placeholder(tf.float32, (None, 2)) noise = tf.random_uniform(shape=x.get_shape) # error x += noise TypeError: Expected binary or unicode string, got <bound method Tensor.get_shape of <tf.Tensor ‘Placeholder:0’ shape=(?, 2) dtype=float32>> TypeError: Failed to convert object of type <class ‘method’> to Tensor. Contents: <bound method Tensor.get_shape of <tf.Tensor ‘Placeholder:0’ shape=(?, 2) dtype=float32>>. Consider casting elements to a ..
I have a multidimensional input (None, 8, 105) I need to access the value – i[-1:][0][-1:][0][:1] and make comparisons between y_py_actual, y_predicted and input_tensor This is more or less what I got, but the function doesn’t work def custon_loss(self, input_tensor): def loss(y_actual, y_predicted): i = input_tensor[0][-1:][0][:1] mse = K.mean(K.sum(K.square(y_actual – y_predicted))) return K.switch((K.greater(i, y_predicted) & ..
The aim is to run a Semantic analysis using BERT. I checked most Q/As here but could not find an answer to my question. Data is in a dataframe. Training data is: id review name label 1 it is a great product for turning lights on. Ashley 1 2 plays music and have a good ..
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