keras-layer

问题 I am looking for a proper or best way to get variable importance in a Neural Network created with Keras. The way I currently do it is I just take the weights (not the biases) of the variables in the first layer with the assumption that more important variables will have higher weights in the first layer. Is there another/better way of doing it? 回答1: Since everything will be mixed up along the network, the first layer alone can't tell you about the importance of each variable. The following

问题 I am trying to code and learn different neural network models. I am having a lot of complication with input dimensionality. I am looking for some tutorial that shows differences in layers and how to set input and outputs for each layers. 回答1: Keras documentation shows you all the input_shape s expected by each layer. In Keras, you'll see input shapes in these forms: input_shape defined by user in layers shapes shown in summaries and others array shapes tensor shapes Input shape defined by

问题 I want to define my own Lstm model as follows: from keras import backend as K from keras.callbacks import ModelCheckpoint from keras.layers.core import Dense, Activation, Flatten, Dropout from keras.layers import Input,Concatenate, Average, Maximum from keras.layers.normalization import BatchNormalization from keras.layers import LSTM, Bidirectional from keras.models import Model from keras.optimizers import Adam class LSTMModel(object): def __init__(self, config): self.num_batch = config[

问题 I have a sequential model that I built in Keras. I try to figure out how to change the shape of the input. In the following example model = Sequential() model.add(Dense(32, input_shape=(500,))) model.add(Dense(10, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) let's say that I want to build a new model with different input shape, conceptual this should looks like this: model1 = model model1.layers[0] = Dense(32, input_shape=

问题 I would like my keras model to resize the input image using cv2 or similar. I have seen the use of ImageGenerator , but I would prefer to write my own generator and simply resize the image in the first layer with keras.layers.core.Lambda . How would I do this? 回答1: If you are using tensorflow backend then you can use tf.image.resize_images() function to resize the images in Lambda layer. Here is a small example to demonstrate the same: import numpy as np import scipy.ndimage import matplotlib

问题 Sometimes the default standard activations like ReLU, tanh, softmax, ... and the advanced activations like LeakyReLU aren't enough. And it might also not be in keras-contrib. How do you create your own activation function? 回答1: Credits to this Github issue comment by Ritchie Ng. # Creating a model from keras.models import Sequential from keras.layers import Dense # Custom activation function from keras.layers import Activation from keras import backend as K from keras.utils.generic_utils

问题 I am working on a CNN model in Keras/TF background. At the end of final convolutional layer, I need to pool the output maps from the filters. Instead of using GlobalAveragePooling or any other sort of pooling, I had to pool according to time frames which exist along the width of the output map. So if a sample output from one filter is let's say n x m , n being time frames and m outputs along the features. Here I just need to pool output from frames n1 to n2 where n1 and n2 <= n. So my output

问题 From nick blog it is clear that in dropout layer of CNN model we drop some nodes on the basis of bernoulli. But how to verify it, i.e. how to check which node is not selected. In DropConnect we leave some weights so I think with the help of model.get_weights() we can verify, but how in the case of dropout layer. model = Sequential() model.add(Conv2D(2, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) model.add(Conv2D(4, (3, 3), activation='relu')) model.add(MaxPooling2D(pool

问题 I am trying to feed in the individual kernel outputs of the previous layer to a new conv filter, to get the next layer. To do that, I tried passing each of the kernel outputs through a Conv2D , by calling them by their index. The function I used is: def modification(weights_path=None, classes=2): ########### ## Input ## ########### ### 224x224x3 sized RGB Input inputs = Input(shape=(224,224,3)) ################################# ## Conv2D Layer with 5 kernels ## ###############################

问题 I'd like to set up a Keras layer in which each node simply computes the logarithm of the corresponding node in the preceding layer. I see from the Keras documentation that there is a "log" function in its backend module. But somehow I'm not understanding how to use this. Thanks in advance for any hints you can offer! 回答1: You can use any backend function inside a Lambda layer: from keras.layers import Lambda import keras.backend as K Define just any function taking the input tensor: def