Category: Misc

Hello there. I’m thinking of using Genetic Algorithm to tune Hyper-parameters of Neural Networks.

Apart form this paper and this blog blog, I don’t find anything that relates with the topic. However there are many GA libraries such as PyGAD etc, but they only apply GA onto weights to fine tune the model instead of finding the best hyper-parameters.

By any chance, anyone here tried anything like this before, as in using GA to find the best hyperparameter in a Tensorflow/Keras Model? Mind share your thoughts?

thanks!

Hi everyone,

I am trying to use a model saved in .h5 format to analyze a video stream and identify the speed in real time. How do I implement this in Python?

Thank you in advance!

I’m trying to create a DNN classifier and am running into the following error:

Invalid argument: assertion failed: [Labels must be <= n_classes – 1] [Condition x <= y did not hold element-wise:] [x (head/losses/labels:0) = ] [[3][2][4]…] [y (head/losses/check_label_range/Const:0) = ] [4]

I am following the general structure from https://www.tensorflow.org/tutorials/estimator/premade and am not sure what I’m doing wrong.

In my data, there are 255 columns and 4 possible classifications for each row.

I have excluded the imports

training_data = pd.read_csv(data_file)
target_data = pd.read_csv(target_file)
train_y = training_data.pop(‘StateCode’)
target_y = target_data.pop(‘StateCode’)
def input_fn(features, labels, training=True, batch_size=256):
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle and repeat if you are in training mode.
if training:
dataset = dataset.shuffle(1000).repeat()

return dataset.batch(batch_size)
my_feature_columns = []
for key in training_data.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
labels = list(training_data.columns)
print(labels)
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 30 and 10 nodes respectively.
hidden_units=[30, 10],
# The model must choose between 4 classes.
n_classes=4)

classifier.train(
input_fn=lambda: input_fn(training_data, train_y, training=True),
steps=5000)

​

Any help would be appreciated.

After several hours of Google searching, I cannot find a straightforward answer. I’m not working with the W model, so my RPi doesn’t have a wireless adapter. I have found results that all involve cloning repos on the RPi itself, but I have no clue how to go about it. Do I need a Raspberian build that already has TF built-in to it? Or is there a way I can scp all the files over to my Pi?

I’ve tried what’s listed here, but instead downloading TF and its dependencies on my local machine, but didn’t know what to do in regards to the file path.

If anyone knows of a good guide or knows how to do this…please let me know. Thanks!

​

Hey!

I’m following this installation guide for object detection using tensorflow 2, my goal is to train a CNN using my GPU. Tensorflow seems to recognize it after the GPU support section and everything runs smoothly. However, after I install the Object Detection API, tensorflow just starts ignoring it and runs on CPU. Any help would be deeply apreciated, thanks!

I understand the simple tf.distribute.mirroredStrategy(), it’s actually pretty simple. I’m hoping to scale a large problem across multiple computers so I’m trying to learn how to use multiWorkerMirrorredStrategy() but I have not found a good example yet.

My understanding is that I would write one python script and distribute it across the machines. Then I define the roles of each machine via the environment variable TF_CONFIG. I create the strategy and then do something like:

mystrategy = tf.distribute.multiWorkerMirroredStrategy() with mystrategy.scope(): model = buildModel() model.compile() model.fit(x_train, y_train) 

That’s all very straightforward. My question is about the data. This code is executed on all nodes. Is each node supposed to parse TF_CONFIG and load its own subset of data? Does just the chief load all the data and then the scope block parses out shards? Or does each node load all the data?

If I install all the external dependencies of Tensor Flow such as the Bazel, make, on my linux machine, and build Tensor Flow from source, would I be able to arrive at a binary code I can feed into some server machine to just run?

I am also asking whether the python API calls a bunch of dynamically linked libraries, and then somehow the have a model.build() command or a model.compile() command that produces a binary in the directory in my linux machine?