Background
I have a multi-label classification problem with 5 labels (e.g. [1 0 1 1 0]). Therefore, I want my model to improve at metrics such as fixed recall, precision-recall AUC or ROC AUC.
It doesn't make sense to use a loss function (e.g. binary_crossentropy) that is not directly related to the performance measurement I want to optimize. Therefore, I want to use TensorFlow's global_objectives.recall_at_precision_loss() or similar as loss function.
Relevant GitHub:
https://github.com/tensorflow/models/tree/master/research/global_objectives
Relevant paper (Scalable Learning of Non-Decomposable Objectives): https://arxiv.org/abs/1608.04802
Not metric
I'm not looking for implementing a tf.metrics. I already succeeded in that following: https://stackoverflow.com/a/50566908/3399066
Problem
I think my issue can be divided into 2 problems:
How to use global_objectives.recall_at_precision_loss() or similar?
How to use it in a Keras model with TF backend?
Problem 1
There is a file called loss_layers_example.py on the global objectives GitHub page (same as above). However, since I don't have much experience with TF, I don't really understand how to use it. Also, Googling for TensorFlow recall_at_precision_loss example or TensorFlow Global objectives example won't give me any clearer example.
How do I use global_objectives.recall_at_precision_loss() in a simple TF example?
Problem 2
Would something like (in Keras): model.compile(loss = ??.recall_at_precision_loss, ...) be enough?
My feeling tells me it is more complex than that, due to the use of global variables used in loss_layers_example.py.
How to use loss functions similar to global_objectives.recall_at_precision_loss() in Keras?
Similar to Martino's answer, but will infer shape from input (setting it to a fixed batch size did not work for me).
The outside function isn't strictly necessary, but it feels a bit more natural to pass params as you configure the loss function, especially when your wrapper is defined in an external module.
import keras.backend as K
from global_objectives.loss_layers import precision_at_recall_loss
def get_precision_at_recall_loss(target_recall):
def precision_at_recall_loss_wrapper(y_true, y_pred):
y_true = K.reshape(y_true, (-1, 1))
y_pred = K.reshape(y_pred, (-1, 1))
return precision_at_recall_loss(y_true, y_pred, target_recall)[0]
return precision_at_recall_loss_wrapper
Then, when compiling the model:
TARGET_RECALL = 0.9
model.compile(optimizer='adam', loss=get_precision_at_recall_loss(TARGET_RECALL))
I managed to make it work by:
Explicitly reshaping tensors to BATCH_SIZE length (see code below)
Cutting the dataset size to a multiple of BATCH_SIZE
def precision_recall_auc_loss(y_true, y_pred):
y_true = keras.backend.reshape(y_true, (BATCH_SIZE, 1))
y_pred = keras.backend.reshape(y_pred, (BATCH_SIZE, 1))
util.get_num_labels = lambda labels : 1
return loss_layers.precision_recall_auc_loss(y_true, y_pred)[0]
Related
I'm trying to write a wrapper around a model, such that the tf model can be called as a function of its weights (and input). However this wrapper returns different gradients than the gradients fromt the original model. Details in the code below (including a colab notebook to reproduce directly), but at the core I'm using the custom gradient decorator - the respective gradient is computed directly as the upstream 'gradient' matmul (via tensordot) the respective jacobian.
To make this clear: I'm computing the gradient for a model, once directly, once by using my custom wrapper. In both cases the parameters in the model are the same. The Jacobian is implemented by TF, so nothing should be wrong there. Still the resulting gradient seems to be wrong.
I'm not sure, whether this is a coding mistake I made somewhere, or possibly just a numeric problem stemming from the Jacobian matmul - however my tests regarding correlation of the gradients suggest this is more than a numeric issue for now. Code of the function is provided below, a link to colab notebook reproducing the problem can be found here: Colab Notebook reproducing the problem
Why: This is important for a bunch of metalearning, which I'm trying to build a small library for currently.
My current 'wrapper' looks something like this:
#calls model on input x but replaces internal weights with the weights argument
#critically supposed to compute the respective gradient for the weights tensor argument!
def call_model_with_weights(model, x, weights, dim_output=2):
#tf.custom_gradient
def _call_with_weights(x_and_w):
x, weights = x_and_w
#be careful; this assigns weights to the model as a side effect, can ignore for dummy version
ctrls = [var.assign(val) for var, val in zip(model.trainable_weights, weights)]
with tf.control_dependencies(ctrls):
with tf.GradientTape() as tape:
y = model(x)
jacobians = tape.jacobian(y, model.trainable_weights)
def grad(upstream, variables):
assert len(variables)==len(weights)
#gradient for each weight should be upstream dotproduct respective jacobian
dy_dw = [tf.tensordot(upstream, j, axes=[list(range(dim_output)), list(range(dim_output))]) for j in jacobians]
dy_dw_weights = dy_dw
return (None, dy_dw_weights), [None for _ in dy_dw] # returning x as derivative of x is wrong, but not important here rn
return y, grad
y = _call_with_weights((x, weights))
return y
Thanks a lot for any help (including how this could be done in a more elegant way), helping out means you are contributing to package that plans to mimic PyTorch 'higher' for TF which I hope helps some more people <3
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.
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.
I would like to get the values of the y_pred and y_true tensors of this keras backend function. I need this to be able to perform some custom calculations and change the loss, these calculations are just possible with the real array values.
def mean_squared_error(y_true, y_pred):
#some code here
return K.mean(K.square(y_pred - y_true), axis=-1)
There is a way to do this in keras? Or in any other ML framework (tf, pytorch, theano)?
No, in general you can't compute the loss that way, because Keras is based on frameworks that do automatic differentiation (like Theano, TensorFlow) and they need to know which operations you are doing in between in order to compute the gradients of the loss.
You need to implement your loss computations using keras.backend functions, else there is no way to compute gradients and optimization won't be possible.
Try including this within the loss function:
y_true = keras.backend.print_tensor(y_true, message='y_true')
Following is an excerpt from the Keras documentation (https://keras.io/backend/):
print_tensor
keras.backend.print_tensor(x, message='')
Prints message and the tensor value when evaluated.
Note that print_tensor returns a new tensor identical to x which should be used in the later parts of the code. Otherwise, the print operation is not taken into account during evaluation.
This is my third attempt to get a deep learning project off the ground. I'm working with protein sequences. First I tried TFLearn, then raw TensorFlow, and now I'm trying Keras.
The previous two attempts taught me a lot, and gave me some code and concepts that I can re-use. However there has always been an obstacle, and I've asked questions that the developers can't answer (in the case of TFLearn), or I've simply gotten bogged down (TensorFlow object introspection is tedious).
I have written this TensorFlow loss function, and I know it works:
def l2_angle_distance(pred, tgt):
with tf.name_scope("L2AngleDistance"):
# Scaling factor
count = tgt[...,0,0]
scale = tf.to_float(tf.count_nonzero(tf.is_finite(count)))
# Mask NaN in tgt
tgt = tf.where(tf.is_nan(tgt), pred, tgt)
# Calculate L1 losses
losses = tf.losses.cosine_distance(pred, tgt, -1, reduction=tf.losses.Reduction.NONE)
# Square the losses, then sum, to get L2 scalar loss.
# Divide the loss result by the scaling factor.
return tf.reduce_sum(losses * losses) / scale
My target values (tgt) can include NaN, because my protein sequences are passed in a 4D Tensor, despite the fact that the individual sequences differ in length. Before you ask, the data can't be resampled like an image. So I use NaN in the tgt Tensor to indicate "no prediction needed here." Before I calculate the L2 cosine loss, I replace every NaN with the matching values in the prediction (pred) so the loss for every NaN is always zero.
Now, how can I re-use this function in Keras? It appears that the Keras Lambda core layer is not a good choice, because a Lambda only takes a single argument, and a loss function needs two arguments.
Alternately, can I rewrite this function in Keras? I shouldn't ever need to use the Theano or CNTK backend, so it isn't necessary for me to rewrite my function in Keras. I'll use whatever works.
I just looked at the Keras losses.py file to get some clues. I imported keras.backend and had a look around. I also found https://keras.io/backend/. I don't seem to find wrappers for ANY of the TensorFlow function calls I happen to use: to_float(), count_nonzero(), is_finite(), where(), is_nan(), cosine_distance(), or reduce_sum().
Thanks for your suggestions!
I answered my own question. I'm posting the solution for anyone who may come across this same problem.
I tried using my TF loss function directly in Keras, as was independently suggested by Matias Valdenegro. I did not provoke any errors from Keras by doing so, however, the loss value went immediately to NaN.
Eventually I identified the problem. The calling convention for a Keras loss function is first y_true (which I called tgt), then y_pred (my pred). But the calling convention for a TensorFlow loss function is pred first, then tgt. So if you want to keep a Tensorflow-native version of the loss function around, this fix works:
def keras_l2_angle_distance(tgt, pred):
return l2_angle_distance(pred, tgt)
<snip>
model.compile(loss = keras_l2_angle_distance, optimizer = "something")
Maybe Theano or CNTK uses the same parameter order as Keras, I don't know. But I'm back in business.
You don't need to use keras.backend, as your loss is directly written in TensorFlow, then you can use it directly in Keras. The backend functions are an abstraction layer so you can code a loss/layer that will work with the multiple available backends in Keras.
You just have to put your loss in the model.compile call:
model.compile(loss = l2_angle_distance, optimizer = "something")