conv-neural-network

问题 I have trained a GAN to reproduce CIFAR10 like images. Initially I notice all images cross one batch produced by the generator look always the same, like the picture below: After hours of debugging and comparison to the tutorial which is a great learning source for beginners (https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-a-cifar-10-small-object-photographs-from-scratch/), I just add only one letter on my original code and the generated images start

问题 I am trying to understand how Darknet works, and I was looking at the yolov3-tiny configuration file, specifically the layer number 13 (line 107). [convolutional] batch_normalize=1 filters=256 size=1 stride=1 pad=1 activation=leaky The size of the kernel is 1x1, the stride is 1 and the padding is 1 too. When I load the network using darknet, it indicates that the output width and height are the same as the input: 13 conv 256 1 x 1/ 1 13 x 13 x1024 -> 13 x 13 x 256 However, shouldn't the width

问题 I'm fairly new to Keras, please excuse me if I made a fundamental error. So, my model has 3 Convolutional (2D) layers and 4 Dense Layers, interspersed with Dropout Layers. I am trying to train a Regression Model using images. X_train.shape = (5164, 160, 320, 3) y_train.shape = (5164) from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense, Flatten, Conv2D, Activation, MaxPooling2D, Dropout import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from tensorflow

问题 I'm fairly new to Keras, please excuse me if I made a fundamental error. So, my model has 3 Convolutional (2D) layers and 4 Dense Layers, interspersed with Dropout Layers. I am trying to train a Regression Model using images. X_train.shape = (5164, 160, 320, 3) y_train.shape = (5164) from tensorflow.keras import Sequential from tensorflow.keras.layers import Dense, Flatten, Conv2D, Activation, MaxPooling2D, Dropout import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from tensorflow

问题 I am trying to understand the .cfg file of YOLOv2. I didn't understand steps=-1,100,80000,100000 scales=.1,10,.1,.1 Can someone explain me this. 回答1: steps is a checkpoints (number of iterations) at which scales will be applied. scales is a coefficients at which learning_rate will be multiplied at this checkpoints. Determines how the learning_rate will be changed during increasing number of iterations during training. Both of them are related to each other and they have the same amount. steps

问题 I was looking through the architecture of EfficientnetB0 and noticed DepthwiseConv2D operation. Did some digging and found that there's also a SeparableConv2D. What exactly are these operations? 来源： https://stackoverflow.com/questions/61967172/what-is-depthwiseconv2d-and-separableconv2d-how-is-it-different-from-normal-con

问题 I was looking through the architecture of EfficientnetB0 and noticed DepthwiseConv2D operation. Did some digging and found that there's also a SeparableConv2D. What exactly are these operations? 来源： https://stackoverflow.com/questions/61967172/what-is-depthwiseconv2d-and-separableconv2d-how-is-it-different-from-normal-con

问题 I am newbie to keras. I ran it on a dataset where my objective was to reduce the logloss. For every epoch it is giving me the same loss value. I am confused whether i am on the right track or not. For example: Epoch 1/5 91456/91456 [==============================] - 142s - loss: 3.8019 - val_loss: 3.8278 Epoch 2/5 91456/91456 [==============================] - 139s - loss: 3.8019 - val_loss: 3.8278 Epoch 3/5 91456/91456 [==============================] - 143s - loss: 3.8019 - val_loss: 3.8278

问题 I am trying to implement a convolutional layer in Python using Numpy. The input is a 4-dimensional array of shape [N, H, W, C] , where: N : Batch size H : Height of image W : Width of image C : Number of channels The convolutional filter is also a 4-dimensional array of shape [F, F, Cin, Cout] , where F : Height and width of a square filter Cin : Number of input channels ( Cin = C ) Cout : Number of output channels Assuming a stride of one along all axes, and no padding, the output should be

问题 I am trying to implement a convolutional layer in Python using Numpy. The input is a 4-dimensional array of shape [N, H, W, C] , where: N : Batch size H : Height of image W : Width of image C : Number of channels The convolutional filter is also a 4-dimensional array of shape [F, F, Cin, Cout] , where F : Height and width of a square filter Cin : Number of input channels ( Cin = C ) Cout : Number of output channels Assuming a stride of one along all axes, and no padding, the output should be