How do I get the output of the model?
I'm writing a classifier using CNTK, and I want to print out the probability distribution (final output) so I can manually evaluate my results. How can I do that?
Right now, for evaluation, I use evaluation methods provided in CNTK, which doesn't require me to get the output of the model.
Thanks a bunch!
If you have a model function (z). You can convert the model output to probabilities using the softmax function.
C.softmax(z).eval().
You can pass the necessary data in the eval function.
Related
I have a setup like this:
model = keras.Model(input,[output1,output2])
My loss function is only a function of output1. How do I tell Keras to ignore output2 for the purposes of computing loss? The best I have come up with is to generate a bogus loss function which always returns 0.0:
model.compile(optimizer=..., loss=[realLossFunction, zeroLossFunction])
I can live with this, but I have to see the statistics and progress of this loss function all over the place and would like to know if there is a more elegant way.
You could just avoid putting this output in the model, and then reusing the weights (or sharing them with the functional API) to add the additional output to the full model.
But using a zero loss is also fine.
I'm using a pre-trained deeplab model (from here) to obtain segmentations for an input image. I'm able to obtain the sematic labels (i.e. SemanticPredictions) which is argmax applied to logits (link).
I was wondering if there is an easy way to obtain the logits before argmax? I was hoping to find the output tensor name and simply pass it into my tfsession
as in the following:
tf_session.run(
self.OUTPUT_TENSOR_NAME,
feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(input_image)]})
But I have not been able to locate such tensor name in the code that reveals the logits, or softmax outputs.
For a model trained from MobileNet_V2 setting self.OUTPUT_TENSOR_NAME = 'ResizeBilinear_2:0' retrieves the logits before the argmax is performed.
I suspect this is the same for xception, but have not verified it.
I arrived at this answer by loading my model in tensorflow. Then, printing the name of all layers in the loaded graph. Finally, I took the name of the final output layer before the last 'ArgMax' layer and ran some inferencing using that.
Here is a link to a stackoverflow question on printing the names of the layers in a graph. I found the answer by Ted to be most helpful.
By the way, the output layers of DeeplabV3 models does not apply SoftMax. So you cannot simply take the raw value of the elements of output vectors as a confidence.
I am trying to build a regression model, for which I have a nominal variable with very high cardinality. I am trying to get the categorical embedding of the column.
Input:
df["nominal_column"]
Output:
the embeddings of the column.
I want to use the op of the embedding column alone since I would require that as a input to my traditional regression model. Is there a way to extract that output alone.
P.S I am not asking for code, any suggestion on the approach would be great.
If the embedding is part of the model and you train it, then you can use functional API of keras to get output of any intermediate operation in your graph:
x=Input((number_of_categories,))
y=Embedding(parameters_of_your_embeddings)(x)
output=Rest_of_your_model()(y)
model=Model(inputs=[x],outputs=[output,y])
if you do it before you train the model, you'll have to define custom loss function, that deals only with part of the output. The other way is to train the model with just one output, then create identical model with two outputs and set the weights of the second model from the trained one.
If you want to get the embedding matrix from your model, you can just use method get_weights of the embedding layer which returns the weights in numpy array.
XGBClassifier outputs probabilities if we use the method "predict_proba", however, when I train the model using xgboost.train, I cannot figure out how to get probabilities as output. Here is a chunk of my code:
dtrain=xgb.DMatrix(X_train, label=y)
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
modelXG=xgb.train(param,dtrain,xgb_model='xgbmodel')
xgboost.train() returns a xgb.Booster object. The xgb.Booster.predict() call returns probabilities in the case of a classification problem instead of the expected labels, if you are used to the .predict()methods of sklearn models. So modelXG.predict(dtest) call will give you want you need.
I'm using Keras w/ Tensorflow backend to train a NN.
I'm using train_on_batch for training, which returns the loss on the given batch. How do I also get the output classification on that batch ? (I'd like to do some visualisations of the output)
To do that I currently do another call to predict to get the model output, but that's redundant since train_on_batch have already passed the input batch "forward".
In Caffe, when an image is fed forward, the intermediate layer outputs stay stored in net.blobs, but in Keras/Tensorflow it seems that if we want to get an intermediate output we have to rerun the computational graph for each intermediate output we want to access on CPU, as described here. Is there a way to access many/all intermediate layers' outputs without rerunning the graph for each ?
I don't mind having a tensorflow-specific workaround.
If you use the function API, this is pretty straight forward.
In addition to #MohamedEzz's answer, you can create a custom callback which can perform the operations you require during the training process. They have methods which will run your code onEpochEnd, onEpochStart, onTrainingEnd and so on...
This way you can preserve the batch.