Keras: Custom loss function with training data not directly related to model - tensorflow

I am trying to convert my CNN written with tensorflow layers to use the keras api in tensorflow (I am using the keras api provided by TF 1.x), and am having issue writing a custom loss function, to train the model.
According to this guide, when defining a loss function it expects the arguments (y_true, y_pred)
https://www.tensorflow.org/guide/keras/train_and_evaluate#custom_losses
def basic_loss_function(y_true, y_pred):
return ...
However, in every example I have seen, y_true is somehow directly related to the model (in the simple case it is the output of the network). In my problem, this is not the case. How do implement this if my loss function depends on some training data that is unrelated to the tensors of the model?
To be concrete, here is my problem:
I am trying to learn an image embedding trained on pairs of images. My training data includes image pairs and annotations of matching points between the image pairs (image coordinates). The input feature is only the image pairs, and the network is trained in a siamese configuration.
I am able to implement this successfully with tensorflow layers and train it sucesfully with tensorflow estimators.
My current implementations builds a tf Dataset from a large database of tf Records, where the features is a dictionary containing the images and arrays of matching points. Before I could easily feed these arrays of image coordinates to the loss function, but here it is unclear how to do so.

There is a hack I often use that is to calculate the loss within the model, by means of Lambda layers. (When the loss is independent from the true data, for instance, and the model doesn't really have an output to be compared)
In a functional API model:
def loss_calc(x):
loss_input_1, loss_input_2 = x #arbirtray inputs, you choose
#according to what you gave to the Lambda layer
#here you use some external data that doesn't relate to the samples
externalData = K.constant(external_numpy_data)
#calculate the loss
return the loss
Using the outputs of the model itself (the tensor(s) that are used in your loss)
loss = Lambda(loss_calc)([model_output_1, model_output_2])
Create the model outputting the loss instead of the outputs:
model = Model(inputs, loss)
Create a dummy keras loss function for compilation:
def dummy_loss(y_true, y_pred):
return y_pred #where y_pred is the loss itself, the output of the model above
model.compile(loss = dummy_loss, ....)
Use any dummy array correctly sized regarding number of samples for training, it will be ignored:
model.fit(your_inputs, np.zeros((number_of_samples,)), ...)
Another way of doing it, is using a custom training loop.
This is much more work, though.
Although you're using TF1, you can still turn eager execution on at the very beginning of your code and do stuff like it's done in TF2. (tf.enable_eager_execution())
Follow the tutorial for custom training loops: https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough
Here, you calculate the gradients yourself, of any result regarding whatever you want. This means you don't need to follow Keras standards of training.
Finally, you can use the approach you suggested of model.add_loss.
In this case, you calculate the loss exaclty the same way I did in the first answer. And pass this loss tensor to add_loss.
You can probably compile a model with loss=None then (not sure), because you're going to use other losses, not the standard one.
In this case, your model's output will probably be None too, and you should fit with y=None.

Related

Keras - sample_weights vs. sample_weight in model.fit?

I am using Keras with a tensorflow backend to train some CNNs for semantic segmentation of biomedical images. I am trying to weight every pixel in my input images during training and believe I am doing so with the data generator I am passing to model.fit.
However, I am a little confused about the meaning of 'sample_weights' vs. 'sample_weight' in the documentation for model.fit.
'sample_weights' is the third optional output from your dataset or image generator - i.e. the output of the generator can either be the tuple (inputs, targets) or the tuple (inputs, targets, sample_weights). I believe this lets me create a mask that weights my samples pixel-by-pixels, but this isn't super clear from the documentation.
'sample_weight' is a separate field that seems to be pretty clearly defined as a weight you can give to every sample. If I understand, this would allow me to give more or less weight to particular images in my training set.
Do I have this right? Thanks.

TensorFlow / Keras Model as function of its weights (and inputs) / Making a tf model pure functional, non-stateful

As the title suggests I'm looking for a way to copy an arbitrary tensorflow (/keras) model, such that I can run the same computational graph from the model, but have the weights (or different weights or a tensor copy of them) as part of the input of the function, something like the following, where an implementation (or idea how to implement) 'smart_copy_function' is what is missing:
model = some_model() #a TF/Keras Model
model_from_weights = smart_copy_function(model)
x = tf.some_model_input # imagine some random input to the model here, e.g. an mnist image
y_model = model(x) #normal way to call model
y_model_from_weights = model_from_weights(model.trainable_weights, x) #same call to copied model
y_model == y_model_from_weights #should be True
I believe this should be doable in a somewhat easy way, as the respective computational graph does exist in TF anyway already.
'This sounds stupid, why would you do this?': I want to build an analog to the PyTorch MetaLearning Framework higher for TensorFlow, since gradients through calls of TF optimizer 'apply_gradients' and variable 'assign' are not supported. To achieve such gradients through parameter gradient updates the above seems to be the way to go. Such gradients through parameter updates are in turn very important for Meta-Learning Research, with papers like 'Model-Agnostic Meta Learning' and 'Teaching with Commentaries' being somewhat famous examples / use cases.

Implementing stochastic forward passes in part of a neural network in Keras?

my problem is the following:
I am working on an object detection problem and would like to use dropout during test time to obtain a distribution of outputs. The object detection network consists of a training model and a prediction model, which wraps around the training model. I would like to perform several stochastic forward passes using the training model and combine these e.g. by averaging the predictions in the prediction wrapper. Is there a way of doing this in a keras model instead of requiring an intermediate processing step using numpy?
Note that this question is not about how to enable dropout during test time
def prediction_wrapper(model):
# Example code.
# Arguments
# model: the training model
regression = model.outputs[0]
classification = model.outputs[1]
predictions = # TODO: perform several stochastic forward passes (dropout during train and test time) here
avg_predictions = # TODO: combine predictions here, e.g. by computing the mean
outputs = # TODO: do some processing on avg_predictions
return keras.models.Model(inputs=model.inputs, outputs=outputs, name=name)
I use keras with a tensorflow backend.
I appreciate any help!
The way I understand, you're trying to average the weight updates for a single sample while Dropout is enabled. Since dropout is random, you would get different weight updates for the same sample.
If this understanding is correct, then you could create a batch by duplicating the same sample. Here I am assuming that the Dropout is different for each sample in a batch. Since, backpropagation averages the weight updates anyway, you would get your desired behavior.
If that does not work, then you could write a custom loss function and train with a batch-size of one. You could update a global counter inside your custom loss function and return non-zero loss only when you've averaged them the way you want it. I don't know if this would work, it's just an idea.

What is the difference between model.fit() an model.evaluate() in Keras?

I am using Keras with TensorFlow backend to train CNN models.
What is the between model.fit() and model.evaluate()? Which one should I ideally use? (I am using model.fit() as of now).
I know the utility of model.fit() and model.predict(). But I am unable to understand the utility of model.evaluate(). Keras documentation just says:
It is used to evaluate the model.
I feel this is a very vague definition.
fit() is for training the model with the given inputs (and corresponding training labels).
evaluate() is for evaluating the already trained model using the validation (or test) data and the corresponding labels. Returns the loss value and metrics values for the model.
predict() is for the actual prediction. It generates output predictions for the input samples.
Let us consider a simple regression example:
# input and output
x = np.random.uniform(0.0, 1.0, (200))
y = 0.3 + 0.6*x + np.random.normal(0.0, 0.05, len(y))
Now lets apply a regression model in keras:
# A simple regression model
model = Sequential()
model.add(Dense(1, input_shape=(1,)))
model.compile(loss='mse', optimizer='rmsprop')
# The fit() method - trains the model
model.fit(x, y, nb_epoch=1000, batch_size=100)
Epoch 1000/1000
200/200 [==============================] - 0s - loss: 0.0023
# The evaluate() method - gets the loss statistics
model.evaluate(x, y, batch_size=200)
# returns: loss: 0.0022612824104726315
# The predict() method - predict the outputs for the given inputs
model.predict(np.expand_dims(x[:3],1))
# returns: [ 0.65680361],[ 0.70067143],[ 0.70482892]
In Deep learning you first want to train your model. You take your data and split it into two sets: the training set, and the test set. It seems pretty common that 80% of your data goes into your training set and 20% goes into your test set.
Your training set gets passed into your call to fit() and your test set gets passed into your call to evaluate(). During the fit operation a number of rows of your training data are fed into your neural net (based on your batch size). After every batch is sent the fit algorithm does back propagation to adjust the weights in your neural net.
After this is done your neural net is trained. The problem is sometimes your neural net gets overfit which is a condition where it performs well for the training set but poorly for other data. To guard against this situation you run the evaluate() function to send new data (your test set) through your neural net to see how it performs with data it has never seen. There is no training occurring, this is purely a test. If all goes well then the score from training is similar to the score from testing.
fit(): Trains the model for a given number of epochs (this is for training time, with the training dataset).
predict(): Generates output predictions for the input samples (this is for somewhere between training and testing time).
evaluate(): Returns the loss value & metrics values for the model in test mode (this is for testing time, with the testing dataset).
While all the above answers explain what these functions : fit(), evaluate() or predict() do however more important point to keep in mind in my opinion is what data you should use for fit() and evaluate().
The most clear guideline that I came across in Machine Learning Mastery and particular quote in there:
Training set: A set of examples used for learning, that is to fit the parameters of the classifier.
Validation set: A set of examples used to tune the parameters of a classifier, for example to choose the number of hidden units in a neural network.
Test set: A set of examples used only to assess the performance of a fully-specified classifier.
: By Brian Ripley, page 354, Pattern Recognition and Neural Networks, 1996
You should not use the same data that you used to train(tune) the model (validation data) for evaluating the performance (generalization) of your fully trained model (evaluate).
The test data used for evaluate() should be unseen/not used for training(fit()) in order to be any reliable indicator of model evaluation (for generlization).
For Predict() you can use just one or few example(s) that you choose (from anywhere) to get quick check or answer from your model. I don't believe it can be used as sole parameter for generalization.
One thing which was not mentioned here, I believe needs to be specified. model.evaluate() returns a list which contains a loss figure and an accuracy figure. What has not been said in the answers above, is that the "loss" figure is the sum of ALL the losses calculated for each item in the x_test array. x_test would contain your test data and y_test would contain your labels. It should be clear that the loss figure is the sum of ALL the losses, not just one loss from one item in the x_test array.
I would say the mean of losses incurred from all iterations, not the sum. But sure, that's the most important information here, otherwise the modeler would be slightly confused.

DeepLearning Anomaly Detection for images

I am still relatively new to the world of Deep Learning. I wanted to create a Deep Learning model (preferably using Tensorflow/Keras) for image anomaly detection. By anomaly detection I mean, essentially a OneClassSVM.
I have already tried sklearn's OneClassSVM using HOG features from the image. I was wondering if there is some example of how I can do this in deep learning. I looked up but couldn't find one single code piece that handles this case.
The way of doing this in Keras is with the KerasRegressor wrapper module (they wrap sci-kit learn's regressor interface). Useful information can also be found in the source code of that module. Basically you first have to define your Network Model, for example:
def simple_model():
#Input layer
data_in = Input(shape=(13,))
#First layer, fully connected, ReLU activation
layer_1 = Dense(13,activation='relu',kernel_initializer='normal')(data_in)
#second layer...etc
layer_2 = Dense(6,activation='relu',kernel_initializer='normal')(layer_1)
#Output, single node without activation
data_out = Dense(1, kernel_initializer='normal')(layer_2)
#Save and Compile model
model = Model(inputs=data_in, outputs=data_out)
#you may choose any loss or optimizer function, be careful which you chose
model.compile(loss='mean_squared_error', optimizer='adam')
return model
Then, pass it to the KerasRegressor builder and fit with your data:
from keras.wrappers.scikit_learn import KerasRegressor
#chose your epochs and batches
regressor = KerasRegressor(build_fn=simple_model, nb_epoch=100, batch_size=64)
#fit with your data
regressor.fit(data, labels, epochs=100)
For which you can now do predictions or obtain its score:
p = regressor.predict(data_test) #obtain predicted value
score = regressor.score(data_test, labels_test) #obtain test score
In your case, as you need to detect anomalous images from the ones that are ok, one approach you can take is to train your regressor by passing anomalous images labeled 1 and images that are ok labeled 0.
This will make your model to return a value closer to 1 when the input is an anomalous image, enabling you to threshold the desired results. You can think of this output as its R^2 coefficient to the "Anomalous Model" you trained as 1 (perfect match).
Also, as you mentioned, Autoencoders are another way to do anomaly detection. For this I suggest you take a look at the Keras Blog post Building Autoencoders in Keras, where they explain in detail about the implementation of them with the Keras library.
It is worth noticing that Single-class classification is another way of saying Regression.
Classification tries to find a probability distribution among the N possible classes, and you usually pick the most probable class as the output (that is why most Classification Networks use Sigmoid activation on their output labels, as it has range [0, 1]). Its output is discrete/categorical.
Similarly, Regression tries to find the best model that represents your data, by minimizing the error or some other metric (like the well-known R^2 metric, or Coefficient of Determination). Its output is a real number/continuous (and the reason why most Regression Networks don't use activations on their outputs). I hope this helps, good luck with your coding.