I have a couple of questions about LSTM layers in Keras library
In LSTM layer we have two kind of dropouts: dropout and recurrent-dropout. According to my understanding the first one will drop randomly some features from input (set them to zero) and the second one will do it on hidden units (features of h_t). Since we have different time steps in a LSTM network, is dropping applied seperately to each time step or only one time and will be the same for every step?
My second question is about regularizers in LSTM layer in keras. I know that for example the kernel regularizer will regularize weights corresponding to inputs. but we have different weights for inputs.
For example input gate, update gate and output gates use different weights for input (aslo different weights for h_(t-1)) . So will they be regularized in the same time ? What if I want to regularize only one of them? For example if I want to regularize only the weights used in the formula for input gate.
The last question is about activation functions in keras. In LSTM layer I have activation and recurrent activations. activation is tanh by default and I know in LSTM architecture tanh is used two times (for h_t and candidate of memory cell) and sigmoid is used 3 times (for gates). So does that mean if I change tanh (in LSTM layer in keras) to another function say Relu then it will change for both of h_t and memory cell candidate?
It would be perfect if any of those question could be answered. Thank you very much for your time.
Related
I'm reading through the PyTorch DCGAN tutorial and the last activation of the generator is tanh. Later on in the tutorial, make_grid is called with normalize=True to bring the values to the 0-1 range. I was wondering if there was a reason to use tanh instead of sigmoid for the final activation, as sigmoid would bring the values to the 0-1 range and would obviate the need for the normalization. Is it a question of taste, or does tanh improve something about the model? Thanks!
I am currently turning my Binary Classification Model to a multi-class classification Model. Bare with me.. I am very knew to pytorch and Machine Learning.
Most of what I state here, I know from the following video.
https://www.youtube.com/watch?v=7q7E91pHoW4&t=654s
What I read / know is that the CrossEntropyLoss already has the Softmax function implemented, thus my output layer is linear.
What I then read / saw is that I can just choose my Model prediction by taking the torch.max() of my model output (Which comes from my last linear output. This feels weird because I Have some negative outputs and i thought I need to apply the SOftmax function first, but It seems to work right without it.
So know the big confusing question I have is, when would I use the Softmax function? Would I only use it when my loss doesnt have it implemented? BUT then I would choose my prediction based on the outputs of the SOftmax layer which wouldnt be the same as with the linear output layer.
Thank you guys for every answer this gets.
For calculating the loss using CrossEntropy you do not need softmax because CrossEntropy already includes it. However to turn model outputs to probabilities you still need to apply softmax to turn them into probabilities.
Lets say you didnt apply softmax at the end of you model. And trained it with crossentropy. And then you want to evaluate your model with new data and get outputs and use these outputs for classification. At this point you can manually apply softmax to your outputs. And there will be no problem. This is how it is usually done.
Traning()
MODEL ----> FC LAYER --->raw outputs ---> Crossentropy Loss
Eval()
MODEL ----> FC LAYER --->raw outputs --> Softmax -> Probabilites
Yes you need to apply softmax on the output layer. When you are doing binary classification you are free to use relu, sigmoid,tanh etc activation function. But when you are doing multi class classification softmax is required because softmax activation function distributes the probability throughout each output node. So that you can easily conclude that the output node which has the highest probability belongs to a particular class. Thank you. Hope this is useful!
I am new to deep learning and I want to build an image classifier using CNN(keras). I have built a model with 2 convolution layers (filters = 32 , kernel = 3x3) followed by a MaxPooling layer(2x2) and this repeated 2 times. Finally 2 fully connected layers. I am getting an accuracy of 50%. My question is how do we choose the model to begin with. Like how do we decide that there should be 2 convolution layers followed by a MaxPooling layer or 1 convolution and 1 MaxPooling layer. Also how do we choose the number of filters in each convolution layer and the kernel size.
If my model is not working then how to decide what changes to be made to the model .
model = Sequential()
model.add(Convolution2D(32,3,3,input_shape=
(280,280,3),activation='relu'))
model.add(Convolution2D(32,3,3,activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
#model.add(Dropout(0.25))
model.add(Convolution2D(64,3,3,activation='relu'))
model.add(Convolution2D(64,3,3,activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
#model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(output_dim=256 , activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(output_dim=5,activation='softmax'))
I am getting an accuracy of 50% after 5 epochs. What changes should i make in my model?
Let us first start with the more straightforward part. Knowing the number of input and output layers and the number of their neurons is the easiest part. Every network has a single input layer and a single output layer. The number of neurons in the input layer equals the number of input variables in the data being processed. The number of neurons in the output layer equals the number of outputs associated with each input.
But the challenge is knowing the number of hidden layers and their neurons.
The answer is you cannot analytically calculate the number of layers or the number of nodes to use per layer in an artificial neural network to address a specific real-world predictive modeling problem.
The number of layers and the number of nodes in each layer are model hyperparameters that you must specify and learn.
You must discover the answer using a robust test harness and controlled experiments. Regardless of the heuristics, you might encounter, all answers will come back to the need for careful experimentation to see what works best for your specific dataset.
Again the filter size is one such hyperparameter you should specify before training your network.
For an image recognition problem, if you think that a big amount of pixels are necessary for the network to recognize the object you will use large filters (as 11x11 or 9x9). If you think what differentiates objects are some small and local features you should use small filters (3x3 or 5x5).
These are some tips but do not exist any rules.
There are many tricks to increase the accuracy of your deep learning model. Kindly refer to this link Improve deep learning model performance.
Hope this will help you.
My question is does the this code make sense? And if this makes sense what should be the purpose?
model.add(LSTM(18, return_sequences=True,batch_input_shape=(batch_size,look_back,dim_x), stateful=True))
model.add(Dropout(0.3))
model.add(LSTM(50,return_sequences=False,stateful=False))
model.add(Dropout(0.3))
model.add(Dense(1, activation='linear'))
Because if my first LSTM layer returns my state from one batch to the next, why shouldn't do my second LSTM layer the same?
I'm having a hard time to understand the LSTM mechanics in Keras so I'm very thankful for any kind of help :)
And if you down vote this post could you tell me why in the commands? thanks.
Your program is a regression problem where your model consists of 2 lstm layers with 18 and 50 layers each and finally a dense layer to show the regression value.
LSTM requires a 3D input.Since the output of your first LSTM layer is going to the input for the second LSTM layer.The input of the Second LSTM layer should also be in 3D. so we set the retrun sequence as true in 1st as it will return a 3D output which can then be used as an input for the second LSTM.
Your second LSTMs value does not return a sequence because after the second LSTM you have a dense layer which does not need a 3D value as input.
[update]
In keras by default LSTM states are reset after each batch of training data,so if you don't want the states to be reset after each batch you can set the stateful=True. If LSTM is made stateful final state of a batch will be used as an initial state for the next batch.
You can later reset the states by calling reset_states()
In Keras you can specify a dropout layer like this:
model.add(Dropout(0.5))
But with a GRU cell you can specify the dropout as a parameter in the constructor:
model.add(GRU(units=512,
return_sequences=True,
dropout=0.5,
input_shape=(None, features_size,)))
What's the difference? Is one preferable to the other?
In Keras' documentation it adds it as a separate dropout layer (see "Sequence classification with LSTM")
The recurrent layers perform the same repeated operation over and over.
In each timestep, it takes two inputs:
Your inputs (a step of your sequence)
Internal inputs (can be states and the output of the previous step, for instance)
Note that the dimensions of the input and output may not match, which means that "your input" dimensions will not match "the recurrent input (previous step/states)" dimesions.
Then in every recurrent timestep there are two operations with two different kernels:
One kernel is applied to "your inputs" to process and transform it in a compatible dimension
Another (called recurrent kernel by keras) is applied to the inputs of the previous step.
Because of this, keras also uses two dropout operations in the recurrent layers. (Dropouts that will be applied to every step)
A dropout for the first conversion of your inputs
A dropout for the application of the recurrent kernel
So, in fact there are two dropout parameters in RNN layers:
dropout, applied to the first operation on the inputs
recurrent_dropout, applied to the other operation on the recurrent inputs (previous output and/or states)
You can see this description coded either in GRUCell and in LSTMCell for instance in the source code.
What is correct?
This is open to creativity.
You can use a Dropout(...) layer, it's not "wrong", but it will possibly drop "timesteps" too! (Unless you set noise_shape properly or use SpatialDropout1D, which is currently not documented yet)
Maybe you want it, maybe you dont. If you use the parameters in the recurrent layer, you will be applying dropouts only to the other dimensions, without dropping a single step. This seems healthy for recurrent layers, unless you want your network to learn how to deal with sequences containing gaps (this last sentence is a supposal).
Also, with the dropout parameters, you will be really dropping parts of the kernel as the operations are dropped "in every step", while using a separate layer will let your RNN perform non-dropped operations internally, since your dropout will affect only the final output.