autotune | AutoTune for Composer - For PHP Library Developers | Build Tool library
kandi X-RAY | autotune Summary
kandi X-RAY | autotune Summary
AutoTune for Composer - For PHP Library Developers
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Top functions reviewed by kandi - BETA
- Configure the autoloader .
- Get the config filename
- Get the configuration .
- Fix the path to make sure it exists
- Initializes the autoloader
- Set the base path
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autotune Examples and Code Snippets
Community Discussions
Trending Discussions on autotune
QUESTION
In my AppDelegate.m, I am doing something like this
...ANSWER
Answered 2021-Jun-10 at 07:22You're using assign for reference/pointer types: @property retain, assign, copy, nonatomic in Objective-C
They should probably be declared copy, because this is a kind of value object, I think.
No exceptions were caught because no exceptions were thrown. Throwing/catching exceptions for control flow is not common in Objective-C
You don't need to write explicit setter functions for @properties
You should prefer to use BOOL type instead of Boolean, with values of YES/NO instead of true/false.
You should return instancetype not id from init, at least in reasonably modern Objective C
Consider making an initialiser that takes all the properties (initWithRoomName:clientID:) and make them read only once set
You don't need to declare -(id) init in your header since it gets that from NSObject
QUESTION
My codes in following
...ANSWER
Answered 2021-Jun-08 at 09:22To be able to fiddle with the models after resampling its best to call resample with store_models = TRUE
Using your example
QUESTION
I have a TF dataset
...ANSWER
Answered 2021-Jun-08 at 12:32You could use tf.data.Dataset.shard.
Creates a Dataset that includes only 1/num_shards of this dataset.
shard is deterministic. The Dataset produced by A.shard(n, i) will contain all elements of A whose index mod n = i.
QUESTION
I am using the stanford dog dataset to do some image classification, loading with the following command.
...ANSWER
Answered 2021-May-21 at 09:45tf.data.Dataset
default to graph mode for performance reasons. In graph mode, you can't do tuple unpacking. You should use tf.unstack
instead.
Note that your dataset has a variable number of bounding boxes. You can either consider only one bounding box per image, or write a preprocess function that is agnostic to the number of bounding boxes.
Note also that the bounding boxes of your dataset are normalized between 0 and 1, while tf.image.crop_to_bounding_box
expects its coordinates (or offset) to be ints, with the format (top, left, height, width)
I would suggest to skip the tuple unpacking and to use tf.image.crop_and_resize
instead, that handle an arbitrary number of boxes per image.
This method will handle just 1 bounding box per image.
QUESTION
I've been trying to speed up training of my CRNN network for optical character recognition, but I can't get the accuracy metric working when using TFRecords and tf.data.Dataset
pipelines. I previously used a Keras Sequence and had it working. Here is a complete runnable toy example showing my problem (tested with Tensorflow 2.4.1):
ANSWER
Answered 2021-May-17 at 09:45There probably some issue with [accuracy]
with tf.data
, but I'm not super sure if this is the main cause in your case or if the issue still exits. If I try as follows, it runs anyway without Sequence
(with tf.data
).
QUESTION
I'm creating a tf.dataset object containing 2 images as inputs and a mask as target. All of them are 3D. After applying a custom map, the shape of the object changes from
to , , ), types: (tf.float32, tf.float32, tf.int32)>
, and when I fit the data, my model throws an error because it only detects one input instead of 2.
Here is what I'm doing:
ANSWER
Answered 2021-May-18 at 10:30I have managed to solve this problem by "flattening" (eliminating the parenthesis) the return of the random_crop_flip, and applying another map on top of them, where I specified the shapes and returned my desired structure (x ,y), z:
QUESTION
I made a simple HDF5 reader class to avoid loading entire dataset in memory. I used sequence class to do so, but I'm not sure if on_epoch_end() function will trigger correctly.
I put a single print inside it, but it never appear! So I think there is something wrong in my code:
...ANSWER
Answered 2021-May-14 at 07:24Since seems to be a TF bug, I found a workaround to trigger on_epoch_end()
of my generator.
QUESTION
The following code is part of my code for a tf graph to read images. When I use this code to iterate through the data, the program gets stuck in tf.io.read_file(path)
after a few hundred images forever and doesn't do anything. More specifically, the code even can't be paused and I had to restart the session every time.
ANSWER
Answered 2021-May-12 at 05:20As mentioned in the comment, try the following function to decode the image file as it can handle mixed extension file format (jpg
, png
etc), ref.
QUESTION
I have created a data pipeline using tf.data for speech recognition using the following code snippets:
...ANSWER
Answered 2021-Mar-16 at 17:47I have found that the issue happened in the padding step, I mean
QUESTION
I am training an LSTM deep learning model with time series sequences and labels.
I generate a tensorflow dataset "train_data" and "test_data"
...ANSWER
Answered 2021-Mar-25 at 12:18The dataset gets shuffled everytime you iterate through it. What you get after your list comprehension isn't in the same order as when you write predict
. If you don't want that, pass:
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