neural-network

问题 I'm trying to train a CNN regression net in TF 1.12, using TPU v3-8 1.12 instance. The model succesfully compiles with XLA, starting the training process, but some where after the half iterations of the 1t epoch freezes, and doing nothing. I cannot find the root of the problem. def read_tfrecord(example): features = { 'image': tf.FixedLenFeature([], tf.string), 'labels': tf.FixedLenFeature([], tf.string) } sample=tf.parse_single_example(example, features) image = tf.image.decode_jpeg(sample[

问题 I have a data set of brain which is in .nii.gz format to work on neural network I used nilearn package to read the file and get the image data. The shape of the image is (256, 256, 150) I can plot the 2D image from slicing the image using Matplotlib but, How can I plot the image in 3D using Python Thanks 来源： https://stackoverflow.com/questions/62256355/plotting-3d-brain-image-python

问题 I tried different solutions but still facing the issue. Actually I am new in Ml/DL (python). In which case we face this error "Can not squeeze dim1, expected a dimension of 1, got 5"? Please help me here, what I am doing wrong here and what is correct Here is InvalidArgumentError Traceback (most recent call last) --------------------------------------------------------------------------- <ipython-input-9-0826122252c2> in <module>() 98 model.summary() 99 model.compile(loss='sparse_categorical

问题 I am using Stochastic Weight Averaging (SWA) with Batch Normalization layers in Tensorflow 2.2. For Batch Norm I use tf.keras.layers.BatchNormalization . For SWA I use my own code to average the weights (I wrote my code before tfa.optimizers.SWA appeared). I have read in multiple sources that if using batch norm and SWA we must run a forward pass to make certain data (running mean and st dev of activation weights and/or momentum values?) available to the batch norm layers. What I do not

问题 I am using Stochastic Weight Averaging (SWA) with Batch Normalization layers in Tensorflow 2.2. For Batch Norm I use tf.keras.layers.BatchNormalization . For SWA I use my own code to average the weights (I wrote my code before tfa.optimizers.SWA appeared). I have read in multiple sources that if using batch norm and SWA we must run a forward pass to make certain data (running mean and st dev of activation weights and/or momentum values?) available to the batch norm layers. What I do not

问题 I am using Stochastic Weight Averaging (SWA) with Batch Normalization layers in Tensorflow 2.2. For Batch Norm I use tf.keras.layers.BatchNormalization . For SWA I use my own code to average the weights (I wrote my code before tfa.optimizers.SWA appeared). I have read in multiple sources that if using batch norm and SWA we must run a forward pass to make certain data (running mean and st dev of activation weights and/or momentum values?) available to the batch norm layers. What I do not

问题 I got the results by running the code provided in this link Neural Network – Predicting Values of Multiple Variables. I was able to compute losses accuracy etc. However, every time I run this code, I get a new result. Is it possible to get the same (consistent) result? 回答1: The code is full of random.randint() everywhere! Furthermore, the weights are most of the time randomly set aswell, and the batch_size also has an influence (although pretty minor) in the result. Y_train, X_test, X_train

问题 I got the results by running the code provided in this link Neural Network – Predicting Values of Multiple Variables. I was able to compute losses accuracy etc. However, every time I run this code, I get a new result. Is it possible to get the same (consistent) result? 回答1: The code is full of random.randint() everywhere! Furthermore, the weights are most of the time randomly set aswell, and the batch_size also has an influence (although pretty minor) in the result. Y_train, X_test, X_train

问题 I have a neural network model and I am using KerasClassifier and then using KFold for cross-validation. Now I am having issues in plotting the ROC curve. I have tried few codes but most of them is giving me an error of multi-labeled is not interpreted. I have the following code till my neural network produces the accuracy. I will be thankful if anyone can help me with the later part of the code. import numpy as np import pandas as pd from keras.layers import Dense, Input from keras.models

问题 I have a neural network model and I am using KerasClassifier and then using KFold for cross-validation. Now I am having issues in plotting the ROC curve. I have tried few codes but most of them is giving me an error of multi-labeled is not interpreted. I have the following code till my neural network produces the accuracy. I will be thankful if anyone can help me with the later part of the code. import numpy as np import pandas as pd from keras.layers import Dense, Input from keras.models