neural-network

问题 What would be the most straightforward way to implement a hypernetwork in Keras? That is, where one leg of the network creates the weights for another? In particular, I would like to do template matching where I feed the template in to a CNN leg that generates a convolutional kernel for a leg that operates on the main image. The part I'm unsure of is where I have a CNN layer that is fed weights externally, yet the gradients still flow through properly for training. 回答1: The weights leg: For

问题 What would be the most straightforward way to implement a hypernetwork in Keras? That is, where one leg of the network creates the weights for another? In particular, I would like to do template matching where I feed the template in to a CNN leg that generates a convolutional kernel for a leg that operates on the main image. The part I'm unsure of is where I have a CNN layer that is fed weights externally, yet the gradients still flow through properly for training. 回答1: The weights leg: For

问题 I am building an auto-encoder and training the model so the targeted output is the same as the input. I am using a sequential Keras model. When I use model.predict I would like it to export the array from a specific layer (Dense256) not the output. This is my current model: model = Sequential() model.add(Dense(4096, input_dim = x.shape[1], activation = 'relu')) model.add(Dense(2048, activation='relu')) model.add(Dense(1024, activation='relu')) model.add(Dense(512, activation='relu')) model

问题 I have used Keras and TensorFlow to classify the Fashion MNIST following this tutorial . It uses the AdamOptimizer to find the value for model parameters that minimize the loss function of the network. The input for the network is a 2-D tensor with shape [28, 28], and output is a 1-D tensor with shape [10] which is the result of a softmax function. Once the network has been trained, I want to use the optimizer for another task: find an input that maximizes one of the elements of the output

问题 I have used Keras and TensorFlow to classify the Fashion MNIST following this tutorial . It uses the AdamOptimizer to find the value for model parameters that minimize the loss function of the network. The input for the network is a 2-D tensor with shape [28, 28], and output is a 1-D tensor with shape [10] which is the result of a softmax function. Once the network has been trained, I want to use the optimizer for another task: find an input that maximizes one of the elements of the output

问题 I am trying to implement the resilient backpropagation optimizer for Keras (link), but the challenging part was being able to perform an update on each individual parameter based on whether its corresponding gradient is positive, negative or zero. I wrote the code below as a start towards implementing the Rprop optimizer. However, I can't seem to find a way to access the parameters individually. Looping over params (as in the code below) returns p, g, g_old, s, wChangeOld at each iteration

问题 I am trying to implement the resilient backpropagation optimizer for Keras (link), but the challenging part was being able to perform an update on each individual parameter based on whether its corresponding gradient is positive, negative or zero. I wrote the code below as a start towards implementing the Rprop optimizer. However, I can't seem to find a way to access the parameters individually. Looping over params (as in the code below) returns p, g, g_old, s, wChangeOld at each iteration

问题 How can I input data into keras? What is the structure? Specifically what is the x_train and y_train if I have more than 2 columns? This is the data I want to input: I am trying to define Xtrain in this example Multi Layer Perceptron Neural Network code Keras has in its documentation. (http://keras.io/examples/) Here is the code: from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.optimizers import SGD model = Sequential() model.add(Dense(64

问题 A simple neural network I found had the layers w1, Relu, and w2. I tried to add a new weight layer in the middle and a second Relu after it. So, the layers are as follows w1, Relu, w_mid, Relu, and w2. It is much much slower than the original 3 layer network if it works at all. I'm not sure if everything is getting a forward pass and if back prop is working across every part it is supposed to. The neural network is from this link. It is the third block of code down the page. This is the code

问题 A simple neural network I found had the layers w1, Relu, and w2. I tried to add a new weight layer in the middle and a second Relu after it. So, the layers are as follows w1, Relu, w_mid, Relu, and w2. It is much much slower than the original 3 layer network if it works at all. I'm not sure if everything is getting a forward pass and if back prop is working across every part it is supposed to. The neural network is from this link. It is the third block of code down the page. This is the code