I have a question regarding the implementation of a custom loss-function for my neural network.
I am currently trying to segment cells for a project and I decided to use a unet as it seems to work quite well. In order to improve my current model, I decided to follow the idea of the original paper of the unet (https://arxiv.org/abs/1505.04597) where they implemented a weight-map assigning thus more weight to pixels that are located in between cells that are tightly associated, as you can see in this picture: Example of a weight map.
I am currently using Keras for my unet and my problem is that I do not know how to give my weights to my model without creating any problem. My idea was to create a generator with the images and a 2-channeled array containing the labels in the first channel and the weights in the second channel, that way I can extract my weights and my labels easily in my custom loss function.
My code looks like that:
train_generator = zip(image_generator, label_generator, weight_generator)
for (img, label, weight) in train_generator:
img, label = adjustData(img, True, label)
label_weights = np.concatenate((label, weight),axis=3)
# This is the final generator
yield (img, label_weights)
As you can see, I construct the train_generator with three previously constructed generators, I adjust some things and then I yield my images and combined labels and weights.
Then, when I try to fit my model with fit_generator, I get this error: ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays.
I really do not know what to do and how to implement correctly what I want to do.
Thank you in advance for your answers.
Related
We want to train our model on varying input dimensions. Every input in a given batch and across batches has different dimensions.
We cannot resize our input (since we’ll lose our microscopic features). Now, since we cannot resize our input, converting them into batches of numpy array becomes impossible. In order to handle this now I have made the list for the input and each list of element contained (height, width, 1). Height is variable size and width is constant.
Sometime my input excessively large. In order to do that I have plan to use model.fit_generator(). In this, We find the max height and width of input in a batch and pad every other input with zeros so that every input in the batch has an equal dimension. Now we can easily convert it to a numpy array or a tensor and pass it to the fit_generator(). The model automatically learns to ignore the zeros and learns features from the intended portion from the padded input. This way we have a batch with equal input dimensions but every batch has a different shape (due to difference in max height and width of input across batches).
Now until here, I described the things what I have learned and what I have plan to do with variable input data. But I am stuck with the following confusions:
1- I have plan to use CNN first and then LSTM on that. I am using tensorflow keras. There, we have the facility of padding and masking . However, As for as I know that LSTM can work on masking and padding ignore 0-padded values. However, I am concerned about the CNN (does CNN ignores 0-padded values), because my padded input will first feed to CNN. I have seen some discussion in the following links:
How to apply masking layer to sequential CNN model in Keras?
https://github.com/keras-team/keras/issues/411
In these link, they mentioned that Unfortunately masking is not yet supported by the Keras Conv layers. However, now we can see alot of development and advancements specifically in the form of tensorflow Keras. So I am wondering that now tensorflow keras can support masking input?
2- To use the generator, we can use custom keras generator. For that I went through a vary good tutorial. I made the mind to use this. But I am wondering is there any advance built-in facility in tensorflow keras to use generator and save me to write custom keras generator?
I have searched the code that uses list or array input data for training DQN code. But I have could not find any code.
Currently, I reference the reinforcement learning tutorial(DQN) of Pytorch.
However, this code uses image input data.
I want to know how to change the image input data to list or array input data.
(I need help to resolve my research that uses list input data. List input data shape is 1 by 9. )
In PyTorch, we deal with tensors. Images, text, even sounds can be transformed to tensors and then PyTorch models can learn on the data.
In PyTorch image classifier examples, you often see something like this, to transform images to tensors:
train_transform = transforms.Compose([
transforms.Resize(x),
...
transforms.ToTensor()
])
If your input is a numpy array x, you can convert it to a tensor like this:
torch.from_numpy(x)
You also have to pay attention to tensor dimensions, your input data needs to match what the model expects in the first layer.
For an assignment, I'm supposed to write a single layer neural network for one part of it. I think I got most of the stuff right, however when I tried using the tf.nn.softmax_cross_entropy_with_logits method, I got an error saying "ValueError: Both labels and logits must be provided." Which obviously means I need to provide both labels and logits, as I only provided logits in my code right now, so I understand what is wrong. What I don't understand is, what is labels and how I use them in this context? Keep in mind that I'm fairly new and inexperienced in tensorflow and neural networks in general. Thanks!
In supervised learning you have to give labels along with the training data and softmax_cross_entropy_with_logits calculates the softmax cross entropy between logits and labels. It helps to give the probability of a data being in a particular class. You can read more about it here https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
I've given you a snippet of code from tensorflow tutorials wheresoftmax_cross_entropy_with_logits is used. Here y_ is a placeholder to which the labels are fed.
Also softmax_cross_entropy_with_logits is currently deprecated.
I've implemented a neural network using Keras. Once trained and tested for final test accuracy, using a matrix with a bunch of rows containing features (plus corresponding labels), I have a model which I should be able to use for prediction.
How can I feed a single unseen example, meaning a feature vector to the model, to obtain a class prediction?
I've looked at their documentation here but could not find a method for it.
What you want is the predict method, it takes a batch of input samples and produces predictions, which are the outputs computer by your network. To feed a single example you can just put it inside a numpy ndarray wrapper.
A silly question: after i train my SVM in scikit-learn i have to use predict function: predict(X) for predicting at which class belongs? (http://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC.predict)
X parameter is the image feature vector?
In case i give an image not trained (not trained because SVM ask at least 3 samples for class), what returns?
First remark: "predict() returns image similarities with SVM in scikit learn" is not a question. Please put a question in the header of Stack Overflow entries.
Second remark: the predict method of the SVC class in sklearn does not return "image similarities" but a class assignment prediction. Read the http://scikit-learn.org documentation and tutorials to understand what we mean by classification and prediction in machine learning.
X parameter is the image feature vector?
No, X is not "the image" feature vector: it is a set of image feature vectors with shape (n_samples, n_features) as explained in the documentation you refer to. In your case a sample is an image hence the expected shape would be (n_images, n_features). The predict API was design to compute many predictions at once for efficiency reason. If you want to compute a single prediction, you will have to wrap your single feature vector in an array with shape (1, n_features).
For instance if you have a single feature vector (1D) called my_single_image_features with shape (n_features,) you can call predict with:
predictions = clf.predict([my_single_image_features])
my_single_prediction = predictions[0]
Please note the [] signs around the my_single_image_features variable to turn it into a 2D array.
my_single_prediction will be an integer whose meaning depends on the integer values provided by you when calling the clf.fit(X_train, y_train) method in the first place.
In case i give an image not trained (not trained because SVM ask at least 3 samples for class), what returns?
An image is not "trained". Only the model is trained. Of course you can pass samples / images that are not part of the training set to the predict method. This is the whole purpose of machine learning: making predictions on new unseen data based on what you learn from the statistical regularities seen in the past training data.