I'm trying to train a discriminator on L-system outputs.
They have the form of a string like "F[/F]FF" with a limited vocabulary of symbols (9 symbols F0[]tTpP/)
I have a bunch of output / bool couple as data (the l system output string, and weither or not this string is "correct").
I'm wondering how many neurons my Input layer need to have ? each string is of variable length, so should the input layer scale as well ?
If you have any clue it would be greatly appreciated.
Thank you
Related
I just created a model that does a binary classification and has a dense layer of 1 unit at the end. I used Sigmoid activation. However, I get this error now when I wanna convert it to CoreML.
I tried to change the number of units to 2 and activation to softmax but still didn't work.
import coremltools as ct
#1. define input size
image_input = ct.ImageType(scale=1/255)
#2. give classifier
classifier_config = coremltools.ClassifierConfig(class_labels=[0, 1]) #ERROR here
#3. convert the model
coreml_model = coremltools.convert("mask_detection_model_surgical_mask.h5",
inputs=[image_input], classifier_config=classifier_config)
#4. load and resize an example image
example_image = Image.open("Unknown3.jpg").resize((256, 256))
# Make a prediction using Core ML
out_dict = coreml_model.predict({mymodel.input_names[0]: example_image})
print(out_dict["classLabels"])
# save to disk
#coreml_model.save("FINALLY.mlmodel")
I found the answer to my question.
Use Softmax activation and 2 Dense units as the final layer with either loss='binary_crossentropy' or `loss='categorical_crossentropy'
Good luck to hundreds of people who posted a similar question but received no answer.
I am working on a inter-class and intra-class classification problem with one CNN such as first there is two classes Cat and Dog than in Cat there is a classification three different breeds of cats and in Dog there are 5 different breeds dogs.
I haven't tried the coding yet just working on feasibility if that works.
My question is what will be the feasible design for this kind of problem.
I am thinking to design for the training, first CNN-1 network that will differentiate cat and dog and gather the image data of all the training images. After the separation of cat and dog, CNN-2 and CNN-3 will train these images further for each breed of dog and cat. I am just not sure how the testing will work in this situation.
I have approached a similar problem previously in Python. Hopefully this is helpful and you can come up with an alternative implementation in Matlab if that is what you are using.
After all was said and done, I landed on a single model for all predictions. For your purpose you could have one binary output for dog vs. cat, another multi-class output for the dog breeds, and another multi-class output for the cat breeds.
Using Tensorflow, I created a mask for the irrelevant classes. For example, if the image was of a cat, then all of the dog breeds are irrelevant and they should not impact model training for that example. This required a customized TF Dataset (that converted 0's to -1 for the mask) and a customized loss function that returned 0 error when the mask was present for that example.
Finally for the training process. Specific to your question, you will have to create custom accuracy functions that can handle the mask values how you want them to, but otherwise this part of the process should be standard. It was best practice to evenly spread out the classes among the training data but they can all be trained together.
If you google "Multi-Task Training" you can find additional resources for this problem.
Here are some code snips if you are interested:
For the customize TF dataset that masked irrelevant labels...
# Replace 0's with -1 for mask when there aren't any labels
def produce_mask(features):
for filt, tensor in features.items():
if "target" in filt:
condition = tf.equal(tf.math.reduce_sum(tensor), 0)
features[filt] = tf.where(condition, tf.ones_like(tensor) * -1, tensor)
return features
def create_dataset(filepath, batch_size=10):
...
# **** This is where the mask was applied to the dataset
dataset = dataset.map(produce_mask, num_parallel_calls=cpu_count())
...
return parsed_features
Custom loss function. I was using binary-crossentropy because my problem was multi-label. You will likely want to adapt this to categorical-crossentropy.
# Custom loss function
def masked_binary_crossentropy(y_true, y_pred):
mask = backend.cast(backend.not_equal(y_true, -1), backend.floatx())
return backend.binary_crossentropy(y_true * mask, y_pred * mask)
Then for the custom accuracy metrics. I was using top-k accuracy, you may need to modify for your purposes, but this will give you the general idea. When comparing this to the loss function, instead of converting all to 0, which would over-inflate the accuracy, this function filters those values out entirely. That works because the outputs are measured individually, so each output (binary, cat breed, dog breed) would have a different accuracy measure filtered only to the relevant examples.
backend is keras backend.
def top_5_acc(y_true, y_pred, k=5):
mask = backend.cast(backend.not_equal(y_true, -1), tf.bool)
mask = tf.math.reduce_any(mask, axis=1)
masked_true = tf.boolean_mask(y_true, mask)
masked_pred = tf.boolean_mask(y_pred, mask)
return top_k_categorical_accuracy(masked_true, masked_pred, k)
Edit
No, in the scenario I described above there is only one model and it is trained with all of the data together. There are 3 outputs to the single model. The mask is a major part of this as it allows the network to only adjust weights that are relevant to the example. If the image was a cat, then the dog breed prediction does not result in loss.
I would like to retrain the vgg-imagenet-f network to do classification (rather than direct image comparison, which is what I have done with my own network).
The downloaded network however is a deployment net, and doesn't have a loss layer included. As I've not done classification training before, I'm a bit stumped as to how to design this last layer. I expect it will be something like this:
layer.name = 'loss' ;
layer.type = 'custom' ;
layer.forward = #forward ;
layer.backward = #backward ;
layer.class = [] ;
but I don't know what my #forward and #backward functions should be. Should they be softmax?
Of note, I have a imdb with about 10k images, corresponding labels, and an ID element with unique numbers running 1 - 10k.
Thanks for any help, or any links to a sample of the way one should construct this layer in matconvnet/matlab!
You could implement your own network adjusting the filters accordingly, since you want to 'retrain' vgg instead of initializing the weights with random numbers you can adapt your classification network using trained filers from downloaded network. The last layer could be softmaxloss
http://www.vlfeat.org/matconvnet/mfiles/vl_nnsoftmaxloss/
I have been using matlab's neural network toolbox lately for my research. I created some neural networks using fitting tool of this toolbox. Now I encountered a problem while testing the network with new data. Basically, I want to test the network with some new data, which do not have the same sample number as the network. For example, I have created the network with the input of 12x36 matrix (12 variables, 36 samples) and output of 1x36 vector (1 variable, 36 samples). Now, I want to get the results for a new data (12x11520 (12 variables and 11520 samples)). However, when I put this data into the network and get the results, I only get an output vector of 1x36 (1 variable 36 samples) while I was expecting to get the results as 1x11520 (1 variable and 11520 samples). I am using the below line to get the results from the neural network named as network.
output_estimate = network(input');
I also tried the line below to get the results but the result did not change.
output_estimate = sim(network,input');
Could you please help me understand this and get the output as the same sample length so that I can compare the expected and actual results for the new data.
Thank you very much in advance.
Irem
I am very new in Matlab and that too in Neural network.. I have 4*81 input dataset and 1*81 output/target dataset. 'divideblock' or 'dividerand' randomly split the dataset into training, validation and testing.My question is that... After training and simulation... how to trace the individual input dataset(training, testing, validation) which are used to train the network.
so that i can able to find the error of the input dataset for testing, validation individually..
thanks in advance for any suggestion...
Use trainInd,valInd,testInd:
[trainInd,valInd,testInd] = dividerand(Q,trainRatio,valRatio,testRatio);
see http://www.mathworks.com/help/toolbox/nnet/ref/dividerand.html .