It is possible to log with mlflow the confusion matrix every step like a simple metrics?
If it is possible it have a visualization like this?
For every possible run, you could get the individual confusion matric values
# get confusion matrix values
conf_matrix = confusion_matrix(y_test,y_pred)
true_positive = conf_matrix[0][0]
true_negative = conf_matrix[1][1]
false_positive = conf_matrix[0][1]
false_negative = conf_matrix[1][0]
mlflow.log_metric("true_positive", true_positive)
mlflow.log_metric("true_negative", true_negative)
mlflow.log_metric("false_positive", false_positive)
mlflow.log_metric("false_negative", false_negative)
And then log_artifact(<your_plot>, "confusion_matrix")
Since you already have the confusion matrix as visualization you can log it with the mlflow.log_artifact() call as file.
The official documenation also has an example even though it is with a txt file it should be no problem to serialize the visualization somehow.
Related
I'm trying to reload or access the Keras-Tuner Trials after the Tuner's search has completed for inspecting the results. I'm not able to find any documentation or answers related to this issue.
For example, I set up BayesianOptimization to search for the best hyper-parameters as follows:
## Build Hyper Parameter Search
tuner = kt.BayesianOptimization(build_model,
objective='val_categorical_accuracy',
max_trials=10,
directory='kt_dir',
project_name='lstm_dense_bo')
tuner.search((X_train_seq, X_train_num), y_train_cat,
epochs=30,
batch_size=64,
validation_data=((X_val_seq, X_val_num), y_val_cat),
callbacks=[callbacks.EarlyStopping(monitor='val_loss', patience=3,
restore_best_weights=True)])
I see this creates trial files in the directory kt_dir with project name lstm_dense_bo such as below:
Now, if I restart my Jupyter kernel, how can I reload these trials into a Tuner object and subsequently inspect the best model or the best hyperparameters or the best trial?
I'd very much appreciate your help. Thank you
I was trying to do the same thing. I was looking into the keras docs for an easier way than this but could not find one - so if any other SO-ers have a better idea, please let us know!
Load the previous tuner. Make sure overwrite=False or else you'll delete your trials.
workdir = "mlp_202202151345"
obj = "val_recall"
tuner = kt.Hyperband(
hypermodel=build_model,
metrics=metrics,
objective=kt.Objective(obj, direction="max"),
executions_per_trial=1,
overwrite=False,
directory=workdir,
project_name="keras_tuner",
)
Look for a trial you want to load. Note that TensorBoard works really well for this. In this example, I'm loading 1a38ebaba07b77501999cb1c4ab9413e.
Here's the part that I could not find in Keras docs. This might be dependent on the tuner you use (I am using Hyperband):
tuner.oracle.get_trial('1a38ebaba07b77501999cb1c4ab9413e')
Returns a Trial object (also could not find in the docs). The Trial object has a hyperparameters attribute that will return that trial's hyperparameters. Now:
tuner.hypermodel.build(trial.hyperparameters)
Gives you the trial's model for training, evaluation, predictions, etc.
NOTE This seems convuluted and hacky, would love to see a better way.
j7skov has correctly mentioned that you need to reload previous tuner and set the parameter overwrite=False(so that tuner will not overwrite already generated trials).
Further if you want to load first K best models then we need to use tuner's get_best_models method as below
# This will load 10 best hyper tuned models with the weights
# corresponding to their best checkpoint (at the end of the best epoch of best trial).
best_model_count = 10
bo_tuner_best_models = tuner.get_best_models(num_models=best_model_count)
Then you can access a specific best model as below
best_model_id = 7
model = bo_tuner_best_models[best_model_id]
This method is for querying the models trained during the search. For best performance, it is recommended to retrain your Model on the full dataset using the best hyperparameters found during search, which can be obtained using tuner.get_best_hyperparameters().
tuner_best_hyperparameters = tuner.get_best_hyperparameters(num_trials=best_model_count)
best_hp = tuner_best_hyperparameters[best_model_id]
model = tuner.hypermodel.build(best_hp)
If you want to just display hyperparameters for the K best models then use tuner's results_summary method as below
tuner.results_summary(num_trials=best_model_count)
For further reference visit this page.
Inspired by j7skov, I found that the models can be reloaded
by manipulating tuner.oracle.trials and tuner.load_model.
By assigning tuner.oracle.trials to a variable, we can find that it is a dict object containing all relavant trials in the tuning process.
The keys of the dictionary are the trial_id, and the values of the
dictionary are the instance of the Trial object.
Alternatively, we can return the best few trials by using tuner.oracle.get_best_trials.
To inspect the hyperparameters of the trial, we can use the summary method of the instance.
To load the model, we can pass the trial instance to tuner.load_model.
Beware that different versions can lead to incompatibilities.
For example the directory structure is a little different between keras-tuner==1.0 and keras-tuner==1.1 as far as I know.
Using your example, the working flow may be summarized as follows.
# Recreate the tuner object
tuner = kt.BayesianOptimization(build_model,
objective='val_categorical_accuracy',
max_trials=10,
directory='kt_dir',
project_name='lstm_dense_bo',
overwrite=False)
# Return all trials from the oracle
trials = tuner.oracle.trials
# Print out the ID and the score of all trials
for trial_id, trial in trials.items():
print(trial_id, trial.score)
# Return best 5 trials
best_trials = tuner.oracle.get_best_trials(num_trials=5)
for trial in best_trials:
trial.summary()
model = tuner.load_model(trial)
# Do some stuff to the model
using
tuner = kt.BayesianOptimization(build_model,
objective='val_categorical_accuracy',
max_trials=10,
directory='kt_dir',
project_name='lstm_dense_bo')
will load the tuner again.
When using the code in https://www.tensorflow.org/serving, but with a DNNClassifier Estimator model, the curl/query request returns all the possible label classes and their associated scores.
Using a model with 100,000+ possible output/label classes, the response becomes too large. Is there any way to limit the number of outputs to the top-k results? (Similar to how it can be done in keras).
The only possibility I could think of is feeding some parameter into the predict API through the signatures, but I haven't found any parameters that would give this functionality. I've read through a ton of documentation + code and googled a ton, but to no avail.
Any help would be greatly appreciated. Thanks in advance for any responses. <3
AFAIC, there are 2 ways to support your need.
You could add some lines in tensorflow-serving source code referring to this
You could do something like this while training/retraining your model.
Hope this will help.
Putting this up here in case it helps anyone. It's possible to override the classification_output() function in head.py (which is used by dnn.py) in order to filter the top-k results. You can insert this snippet into your main.py / train.py file, and whenever you save an DNNClassifier model, that model will always output at most num_top_k_results when doing inference/serving. The vast majority of the method is copied from the original classification_output() function. (Note this may or may not work with 1.13 / 2.0 as it hasn't been tested on those.)
from tensorflow.python.estimator.canned import head as head_lib
num_top_k_results = 5
def override_classification_output(scores, n_classes, label_vocabulary=None):
batch_size = array_ops.shape(scores)[0]
if label_vocabulary:
export_class_list = label_vocabulary
else:
export_class_list = string_ops.as_string(math_ops.range(n_classes))
# Get the top_k results
top_k_scores, top_k_indices = tf.nn.top_k(scores, num_top_k_results)
# Using the top_k_indices, get the associated class names (from the vocabulary)
top_k_classes = tf.gather(tf.convert_to_tensor(value=export_class_list), tf.squeeze(top_k_indices))
export_output_classes = array_ops.tile(
input=array_ops.expand_dims(input=top_k_classes, axis=0),
multiples=[batch_size, 1])
return export_output.ClassificationOutput(
scores=top_k_scores,
# `ClassificationOutput` requires string classes.
classes=export_output_classes)
# Override the original method with our custom one.
head_lib._classification_output = override_classification_output
I have written a custom data iterator using mx.io.DataIter class. What's the easiest way to use this data iterator with Gluon interface?
I went through the documentation and couldn't find an easy way to do so. One of my idea was to use it as iterator and get data adn label from each batch as follows.
for e in range(epochs):
train_iter.reset()
for batch_data in train_iter:
data = nd.concatenate(([d for d in batch_data.data]))
label = nd.concatenate(([l for l in batch_data.label]))
with autograd.record():
output = net(data)
loss = softmax_cross_entropy(output, label)
loss.backward()
trainer.step(batch_size)
print(nd.mean(loss).asscalar())
But this may not be optimal as I need to concatenate per batch.
What's the optimal way to achieve this? i.e. is there a systematic
way to write a simple custom iterator for gluon?
How do I add context information in above cases?
I think your approach works. Basically you can get data from batch_data.data and label from batch_data.label and feed them into the network.
I'm not sure why you need to concat the data and labels - maybe it's to do with your network definition.
If you ever need to split the data and train on multiple GPUs, you can use the gluon.utils.split_and_load function to do that.
I am reading tutorials about TensorFlow visualization and found out Tensorboard. I would like to know how can I visualize for example, Iris dataset taken from UCI Machine Learning repository. I have been able to run a specified port on localhost which shows TensorBoard, but do not know how to visualize a locally taken dataset there. I searched on google but really could not find how to do. Could you help me, please ?
If i understand you correctly then you wish to use tf.summary.image. The documentation is here: https://www.tensorflow.org/api_docs/python/tf/summary/image
Some example usage from my code is:
x_pl=tf.placeholder(tf.float32, [None,height,width,channels], name="ImageIn")
tf.summary.image('input', x_pl, 10)
x_pl is where I feed my image data in.
In my cummary declaration I say that I want to create a summary called 'input' and to take 10 images from x_pl.
Read the summary-writer example/tutorial here: https://www.tensorflow.org/get_started/summaries_and_tensorboard
You will need to merge your summaries:
merged = tf.summary.merge_all()
You will need to declare a summary-writer a bit like this:
train_writer = tf.summary.FileWriter(FLAGS.summaries_dir + '/train',sess.graph)
See the above tutorial/example to understand how Tensorboard works. Not that you will want to replace the summaries with image summaries for your purposes.
TensorFlow provides a tf.summary.tensor_summary() function that appears to be a multidimensional variant of tf.summary.scalar():
tf.summary.tensor_summary(name, tensor, summary_description=None, collections=None)
I thought it could be useful for summarizing inferred probabilities per class ... somewhat like
op_summary = tf.summary.tensor_summary('classes', some_tensor)
# ...
summary = sess.run(op_summary)
writer.add_summary(summary)
However it appears that TensorBoard doesn't provide a way to display these summaries at all. How are they meant to be used?
I cannot get it to work either. It seems like that feature is still under development. See this video from the TensorFlow Dev Summit that states that the tensor_summary is still under development (starting at 9:17): https://youtu.be/eBbEDRsCmv4?t=9m17s. It will probably be better defined and examples should be provided in the future.