neural-network

问题 I have read posts and articles that talk about finding the right number of hidden layer and number of neurons in each hidden layer for a classification problem. However, I couldn't find any relevant information for regression? Can someone help explain the calculation of right number of hidden layer and neurons in them? 回答1: The is no calculation for the "right" number of hidden layers and neurons in them. Trying to find the best model by tuning the number of hidden layers and neurons in them

问题 I have some neural networks for the classification of images developed in Python 3.7 using the Keras package that is included in TensorFlow and I want to export the layers and weights model to import it in a Node.js application that uses Tensorflow.js. I have the neural networks saved in h5 format with two separate files, one for the layer model and the other for the weights model. Try to follow this tutorial that Tensorflow proposes (https://www.tensorflow.org/js/tutorials/conversion/import

问题 I have some neural networks for the classification of images developed in Python 3.7 using the Keras package that is included in TensorFlow and I want to export the layers and weights model to import it in a Node.js application that uses Tensorflow.js. I have the neural networks saved in h5 format with two separate files, one for the layer model and the other for the weights model. Try to follow this tutorial that Tensorflow proposes (https://www.tensorflow.org/js/tutorials/conversion/import

问题 I have some neural networks for the classification of images developed in Python 3.7 using the Keras package that is included in TensorFlow and I want to export the layers and weights model to import it in a Node.js application that uses Tensorflow.js. I have the neural networks saved in h5 format with two separate files, one for the layer model and the other for the weights model. Try to follow this tutorial that Tensorflow proposes (https://www.tensorflow.org/js/tutorials/conversion/import

问题 i am implementing a CLSTM based on This problem but facing error of dimensionality. Data set: I am currently working on one video, which has 4500 images of size (28,28) . The data set is in vectorized form so i get (4500,780) . I split the images using Timeseriessplit and reshape the images with x_train=x_train.reshape(-1, 28, 28, 1) x_test=x_test.reshape(-1, 28, 28, 1) My model is as follows model = models.Sequential() model.add(layers.ConvLSTM2D( filters=40, kernel_size=(3, 3), input_shape=

问题 i am implementing a CLSTM based on This problem but facing error of dimensionality. Data set: I am currently working on one video, which has 4500 images of size (28,28) . The data set is in vectorized form so i get (4500,780) . I split the images using Timeseriessplit and reshape the images with x_train=x_train.reshape(-1, 28, 28, 1) x_test=x_test.reshape(-1, 28, 28, 1) My model is as follows model = models.Sequential() model.add(layers.ConvLSTM2D( filters=40, kernel_size=(3, 3), input_shape=

问题 I'm testing simple networks on Keras with TensorFlow backend and I ran into an issue with using sigmoid activation function The network isn't leraning for first 5-10 epochs, and then everything is fine. I tried using initializers and regularizers, but that only made it worse. I use the network like this: import numpy as np import keras from numpy import expand_dims from keras.preprocessing.image import ImageDataGenerator from matplotlib import pyplot # load the image (x_train, y_train), (x

问题 I trained a simple neural network with TensorFlow on the MNIST dataset. The training portion of the code works fine. However, when I feed a single image into the network, it gives me the following traceback: Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1021, in _do_call return fn(*args) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages

问题 I trained a simple neural network with TensorFlow on the MNIST dataset. The training portion of the code works fine. However, when I feed a single image into the network, it gives me the following traceback: Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1021, in _do_call return fn(*args) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages

问题 my neural network is failing at the training set and every iteration at gradient descent is decreasing the error, but the hypothesis vector values are decreasing each time. When I run the test set on the learned parameters, the network just outputs 1 as its guess for every image. So far, i've tried removing the sigmoid activation from the output, checked the gradients with gradient checking, and i've tried to put the dataset between the values 0-1. Heres an example of the hypothesis during