Based on what you said it sounds like you need a larger batch_size, and of course there are implications with that which could impact the steps_per_epoch and number of epochs. To solve for jumping-around A larger batch size will give you a better gradient and will help to prevent jumping around You may also want … Read more
Here I’m answering to OP’s topic question rather than his exact problem. I’m doing this as the question shows up in the top when I google the topic problem. You can implement a custom metric in two ways. As mentioned in Keras docu. import keras.backend as K def mean_pred(y_true, y_pred): return K.mean(y_pred) model.compile(optimizer=”sgd”, loss=”binary_crossentropy”, metrics=[‘accuracy’, … Read more
Yes, it is possible and you should also use a doubly block circulant matrix (which is a special case of Toeplitz matrix). I will give you an example with a small size of kernel and the input, but it is possible to construct Toeplitz matrix for any kernel. So you have a 2d input x … Read more
I assume you mean how to define phase when writing a prototxt using caffe.NetSpec? from caffe import layers as L, params as P, to_proto import caffe ns = caffe.NetSpec() ns.data = L.Data(name=”data”, data_param={‘source’:’/path/to/lmdb’,’batch_size’:32}, include={‘phase’:caffe.TEST}) If you want to have BOTH train and test layers in the same prototxt, what I usually do is making one … Read more
First you must learn that in Keras when you apply a layer on an input, a new node is created inside this layer which connects the input and output tensors. Each layer may have multiple nodes connecting different input tensors to their corresponding output tensors. To build a model, these nodes are traversed and a … Read more
Reading from here, which is the source code of KerasClassifier, you can pass it the arguments of fit and they should be used. I don’t have your dataset so I cannot test it, but you can tell me if this works and if not I will try and adapt the solution. Change this line : … Read more
Just to add to the list, there’re several more ways to do batch-norm in tensorflow: tf.nn.batch_normalization is a low-level op. The caller is responsible to handle mean and variance tensors themselves. tf.nn.fused_batch_norm is another low-level op, similar to the previous one. The difference is that it’s optimized for 4D input tensors, which is the usual … Read more
I’ve created a gist with a simple generator that builds on top of your initial idea: it’s an LSTM network wired to the pre-trained word2vec embeddings, trained to predict the next word in a sentence. The data is the list of abstracts from arXiv website. I’ll highlight the most important parts here. Gensim Word2Vec Your … Read more
By “combine them” I assume you mean to concatenate the two inputs. Assuming you concat along the second dimension: import torch from torch import nn class TwoInputsNet(nn.Module): def __init__(self): super(TwoInputsNet, self).__init__() self.conv = nn.Conv2d( … ) # set up your layer here self.fc1 = nn.Linear( … ) # set up first FC layer self.fc2 = … Read more
It is a common practice to decrease the learning rate (lr) as the optimization/learning process progresses. However, it is not clear how exactly the learning rate should be decreased as a function of the iteration number. If you use DIGITS as an interface to Caffe, you will be able to visually see how the different … Read more