Has anyone seen this error.. if so how is it fixed?

from keras.layers import Conv2D, UpSampling2D, LeakyReLU, Concatenate, Lambda, Input, UpSampling2D from tensorflow.keras import Model from keras.applications.densenet import DenseNet169 ”’ Following is to get layers for skip connection and num_filters ”’ base_model = DenseNet169(include_top=False,input_shape=(224,224,3)) base_model_output_shape=base_model.layers[-1].output.shape decoder_filters = int(base_model_output_shape[-1]/2) def UpProject(array,filters,name,concat_with): up_i = UpSampling2D((2,2),interpolation=’bilinear’)(array) up_i=Concatenate(name=name+’_concat’)([up_i,base_model.get_layer(concat_with).output]) #skip connection up_i=Conv2D(filters=filters,kernel_size=3,strides=1,padding=’same’,name=name+’_convA’)(up_i) up_i=LeakyReLU(alpha=.2)(up_i) up_i=Conv2D(filters=filters,kernel_size=3,strides=1,padding=’same’,name=name+’_convB’)(up_i) up_i=LeakyReLU(alpha=.2)(up_i) return up_i def get_Model(): #encoder network…

Which CNN hyper-parameters are most sensitive to centered versus off centered data?

Which hyper-parameters of a convolutional neural network are likely to be the most sensitive to depending on whether the training (and test and inference) data involves only accurately centered images versus off-centered images. More convolutional layers, wider convolution kernels, more dense layers, wider dense layers, more or less pooling, or ??? e.g. If I can…

Average Reward for Temporal Difference (TD), and how it’s used in Actor-Critic algorithm

In Sutton & Barto’s book (2nd edition) chapter 10 is given the equation for TD(0) Error with Average Reward: $\delta_t = R_{t+1} – \bar{R} + \hat{v}(S_{t+1}, \mathbf{w}) – \hat{v}(S_{t}, \mathbf{w}) \hspace{6em} (10.10)$ Can anyone explain the intuition behind this equation? And how exactly it is derived? Also, in chapter 13, section 6, is given the…

Why do we average gradients and not loss in distributed training?

I’m running some distributed trainings in Tensorflow with Horovod. It runs training separately on multiple workers, each of which uses the same weights and does forward pass on unique data. Computed gradients are averaged within the communicator (worker group) before applying them in weight updates. I’m wondering – why not average the loss function across…