deep-learning

问题 I Want to Combine Two CNN Into Just One In Keras, What I Mean Is that I Want The Neural Network To Take Two Images And Process Each One in Separate CNN, and Then Concatenate Them Together Into The Flattening Layer and Use Fully Connected Layer to Do The Last Work, Here What I Did: # Start With First Branch ############################################################ branch_one = Sequential() # Adding The Convolution branch_one.add(Conv2D(32, (3,3),input_shape = (64,64,3) , activation = 'relu'

问题 I Want to Combine Two CNN Into Just One In Keras, What I Mean Is that I Want The Neural Network To Take Two Images And Process Each One in Separate CNN, and Then Concatenate Them Together Into The Flattening Layer and Use Fully Connected Layer to Do The Last Work, Here What I Did: # Start With First Branch ############################################################ branch_one = Sequential() # Adding The Convolution branch_one.add(Conv2D(32, (3,3),input_shape = (64,64,3) , activation = 'relu'

问题 I Want to Combine Two CNN Into Just One In Keras, What I Mean Is that I Want The Neural Network To Take Two Images And Process Each One in Separate CNN, and Then Concatenate Them Together Into The Flattening Layer and Use Fully Connected Layer to Do The Last Work, Here What I Did: # Start With First Branch ############################################################ branch_one = Sequential() # Adding The Convolution branch_one.add(Conv2D(32, (3,3),input_shape = (64,64,3) , activation = 'relu'

问题 I tried to perform some deep learning application and got a module 'tensorflow' has no attribute 'random_uniform' error. On CPU the code works fine but it is really slow. In order to run the code on GPU i needed to change some definitions. Here is my code below. Any ideas? def CapsNet(input_shape, n_class, routings): x = tf.keras.layers.Input(shape=input_shape) # Layer 1: Just a conventional Conv2D layer conv1 = tf.keras.layers.Convolution2D(filters=256, kernel_size=9, strides=1, padding=

问题 I tried to perform some deep learning application and got a module 'tensorflow' has no attribute 'random_uniform' error. On CPU the code works fine but it is really slow. In order to run the code on GPU i needed to change some definitions. Here is my code below. Any ideas? def CapsNet(input_shape, n_class, routings): x = tf.keras.layers.Input(shape=input_shape) # Layer 1: Just a conventional Conv2D layer conv1 = tf.keras.layers.Convolution2D(filters=256, kernel_size=9, strides=1, padding=

问题 I am using python 3 with anaconda, and tensorflow 1.12 with eager eval. I am using it to create a triplet loss function for a siamese network, and need to calculate distance between different data samples. I created a function in order to create the distance calculation, but no matter what I do, when I try to calculate it's gradient with respect to the networks output, It keeps giving me all nan gradient. This is the code: def matrix_row_wise_norm(matrix): import tensorflow as tf tensor = tf

问题 I am using python 3 with anaconda, and tensorflow 1.12 with eager eval. I am using it to create a triplet loss function for a siamese network, and need to calculate distance between different data samples. I created a function in order to create the distance calculation, but no matter what I do, when I try to calculate it's gradient with respect to the networks output, It keeps giving me all nan gradient. This is the code: def matrix_row_wise_norm(matrix): import tensorflow as tf tensor = tf

问题 I have created a custom loss function to deal with binary class imbalance, but my loss function does not improve per epoch. For metrics, I'm using precision and recall. Is this a design issue where I'm not picking good hyper-parameters? weights = [np.array([.10,.90]), np.array([.5,.5]), np.array([.1,.99]), np.array([.25,.75]), np.array([.35,.65])] for weight in weights: print('Model with weights {a}'.format(a=weight)) model = keras.models.Sequential([ keras.layers.Flatten(), #input_shape=[X

问题 I have created a custom loss function to deal with binary class imbalance, but my loss function does not improve per epoch. For metrics, I'm using precision and recall. Is this a design issue where I'm not picking good hyper-parameters? weights = [np.array([.10,.90]), np.array([.5,.5]), np.array([.1,.99]), np.array([.25,.75]), np.array([.35,.65])] for weight in weights: print('Model with weights {a}'.format(a=weight)) model = keras.models.Sequential([ keras.layers.Flatten(), #input_shape=[X

问题 I am working in a machine learning problem and want to build neural network based classifiers on it in matlab. One problem is that the data is given in the form of features and number of samples is considerably lower. I know about data augmentation techniques for images, by rotating, translating, affine translation, etc. I would like to know whether there are data augmentation techniques available for general datasets ? Like is it possible to use randomness to generate more data ? I read the