There are 2 ways to get the output of an intermediate area, as posted here on this... moreThere are 2 ways to get the output of an intermediate area, as posted here on this forum:https://www.cluzters.ai/forums/topic/353/keras-how-to-get-the-output-of-each-layer?c=1597You don't need (or want) both of these, either works:
get_layer_output = K.function([model.layers, [model.layers)
layer_output = get_layer_output()
or
tmp_model = Model(model.layers.input, model.layers.output)
tmp_output = tmp_model.predict(x_train)
This works well for a smaller model, but I always get OOM if my model is too large. Is there an easy way to free the GPU memory of the original model before creating tmp_model or calling get_layer_output? I can save my weights to a text file and create another program in another session, but it seems like there should be an easier way. less
I'm trying to build a Python Lambda to send images to TensorFlow Serving for inferences. I have at least two dependencies: CV2 and tensorflow_serving.apis. I've run multiple... moreI'm trying to build a Python Lambda to send images to TensorFlow Serving for inferences. I have at least two dependencies: CV2 and tensorflow_serving.apis. I've run multiple tutorials showing it's possible to run tensorflow in a lambda, but they provide the package to install and don't explain how they got it to fit in the limit of less than 256MB unzipped.
How to Deploy ... Lambda and TensorFlow
Using TensorFlow and the Serverless Framework...
I've tried following the official instructions for packaging but just this downloads 475MB of dependencies:
$ python -m pip install tensorflow-serving-api --target .
Collecting tensorflow-serving-api
Downloading https://files.pythonhosted.org/packages/79/69/1e724c0d98f12b12f9ad583a3df7750e14ec5f06069aa4be8d75a2ab9bb8/tensorflow_serving_api-1.12.0-py2.py3-none-any.whl
...
$ du -hs .
475M .
I see that others have fought this dragon and won (1) (2) by doing contortions to rip out all unused libraries from all dependencies or compile from scratch. But... less
I need to utilize TensorFlow for a project to classify items based on their attributes to a certain class (either 1, 2, or 3).
Only problem is almost every TF tutorial or example... moreI need to utilize TensorFlow for a project to classify items based on their attributes to a certain class (either 1, 2, or 3).
Only problem is almost every TF tutorial or example I find online is about image recognition or text classification. I can't find anything about classification based on numbers. I guess what I'm asking for is where to get started. If anyone knows of a relevant example, or if I'm just thinking about this completely wrong.
We are given the 13 attributes for each item, and need to use the TF neural network to classify each item correctly (or mark the margin of error). But nothing online is showing me even how to start with this kind of dataset.
Example of dataset: (first value is class, other values are attributes)
2, 11.84, 2.89, 2.23, 18, 112, 1.72, 1.32, 0.43, 0.95, 2.65, 0.96, 2.52, 500
3, 13.69, 3.26, 2.54, 20, 107, 1.83, 0.56, 0.5, 0.8, 5.88, 0.96, 1.82, 680
3, 13.84, 4.12, 2.38, 19.5, 89, 1.8, 0.83, 0.48, 1.56, 9.01, 0.57, 1.64, 480
2, 11.56, 2.05, 3.23, 28.5, 119,... less
I am facing the problem where tensorflow is not running in the jupyter notebook it is showing me
No module named tensorflow
But it is running ine anaconda prompt how to fix this
I'm aware of this question, but it is for an outdated function.Let's say I'm trying to predict whether a person will visit country 'X' given the countries they have already... moreI'm aware of this question, but it is for an outdated function.Let's say I'm trying to predict whether a person will visit country 'X' given the countries they have already visited and their income.I have a training data set in a pandas DataFrame that's in the following format.Each row represents a different person, each unrelated to the others in matrix.The first 10 columns are all names of countries and the values in the column are binary (1 if they have visited that country or 0 if they haven't).Column 11 is their income. It's a continuous decimal variable.Lastly, column 12 is another binary table that says yes they have visited 'X' or not.So essentially, if I have a 100,000 people in my dataset, then I have a dataframe of dimensions 100,000 x 12. I want to be able to properly pass this into a linear classifier using tensorflow. But not sure even how to approach this.I am trying to pass the data into this function
estimator = LinearClassifier(
n_classes=n_classes, feature_columns=,... less
Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH:... moreCould not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /home/nickopotamus/.local/share/r-miniconda/envs/r-reticulate/lib:/usr/lib/R/lib::/lib:/usr/lib/x86_64-linux-gnu:/usr/lib/jvm/default-java/lib/server
sudo find / -name 'libcudart.so.11.0' finds the file in:
/home/nickopotamus/.local/share/r-miniconda/envs/r-reticulate/lib/libcudart.so.11.0
/home/nickopotamus/anaconda3/pkgs/cudatoolkit-11.3.1-h2bc3f7f_2/lib/libcudart.so.11.0
/home/nickopotamus/anaconda3/pkgs/cudatoolkit-11.2.0-h73cb219_8/lib/libcudart.so.11.0
/home/nickopotamus/anaconda3/pkgs/cudatoolkit-11.2.72-h2bc3f7f_0/lib/libcudart.so.11.0
/usr/local/cuda-11.2/targets/x86_64-linux/lib/libcudart.so.11.0
The top entry at least appears to be in the path that the error is searching, so I'm at a bit of a loss as to what to try next. Is it a conflict with the other anaconda packages (which I can't seem to remove), or am I simply being... less
It is a principal question, regarding the theory of neural networks:
Why do we have to normalize the input for a neural network?
I understand that sometimes, when for example the... moreIt is a principal question, regarding the theory of neural networks:
Why do we have to normalize the input for a neural network?
I understand that sometimes, when for example the input values are non-numerical a certain transformation must be performed, but when we have a numerical input? Why the numbers must be in a certain interval?
What will happen if the data is not normalized?
I am really new to Data Science/ML and have been working on Tensorflow to implement Linear Regression on California Housing Prices from Kaggle.
I tried to train a mode in two... moreI am really new to Data Science/ML and have been working on Tensorflow to implement Linear Regression on California Housing Prices from Kaggle.
I tried to train a mode in two different ways:
Using a Sequential model
Custom implementation
In both cases, the loss of the model was really high and I have not been able to understand what are the ways to improve it.
Dataset prep
df = pd.read_csv('california-housing-prices.zip')
df = df
print('Shape of dataset before removing NAs and duplicates {}'.format(df.shape))
df.dropna(inplace=True)
df.drop_duplicates(inplace=True)
input_train, input_test, target_train, target_test = train_test_split(df.values, df.values, test_size=0.2)
scaler = MinMaxScaler()
input_train = input_train.reshape(-1,1)
input_test = input_test.reshape(-1,1)
input_train = scaler.fit_transform(input_train)
input_test = scaler.fit_transform(input_test)
target_train = target_train.reshape(-1,1)
target_train = scaler.fit_transform(target_train)
target_test =... less
What are the differences between all these cross-entropy losses?
Keras is talking... moreWhat are the differences between all these cross-entropy losses?
Keras is talking about
Softmax cross-entropy with logits
Sparse softmax cross-entropy with logits
Sigmoid cross-entropy with logits
What are the differences and relationships between them? What are the typical applications for them? What's the mathematical background? Are there other cross-entropy types that one should know? Are there any cross-entropy types without logits? less
I trained quora question pair detection with LSTM but training accuracy is very low and always changes when i train. I dont understand what mistake i did.
I tried changing loss... moreI trained quora question pair detection with LSTM but training accuracy is very low and always changes when i train. I dont understand what mistake i did.
I tried changing loss and optimiser and with increased epoch.
import numpy as np
from numpy import array
from keras.callbacks import ModelCheckpoint
import keras
from keras.optimizers import SGD
import tensorflow as tf
from sklearn import preprocessing
import xgboost as xgb
from keras import backend as K
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from keras.preprocessing.text import Tokenizer , text_to_word_sequence
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from keras.models import Sequential, model_from_json, load_model
from keras.layers import LSTM, Dense, Input, concatenate, Concatenate, Activation, Flatten
from keras.models import Model
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from... less
gave this error in installation. Does this cause a problem?
ERROR: tensorboard 2.0.2 has requirement setuptools>=41.0.0, but you'll have setuptools 40.6.2 which is incompatible.
From C:\Anaconda3\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\platform\app.py:125: main (from __main__) is deprecated and will be removed in a future... moreFrom C:\Anaconda3\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\platform\app.py:125: main (from __main__) is deprecated and will be removed in a future version.
Instructions for updating:
Use object_detection/model_main.py.
Traceback (most recent call last):
File "train.py", line 184, in <module>
tf.app.run()
File "C:\Anaconda3\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\platform\app.py", line 125, in run
_sys.exit(main(argv))
File "C:\Anaconda3\envs\tensorflow_cpu\lib\site-packages\tensorflow\python\util\deprecation.py", line 306, in new_func
return func(*args, **kwargs)
File "train.py", line 180, in main
graph_hook_fn=graph_rewriter_fn)
File "C:\Users\arfan\Documents\TensorFlow\models\research\object_detection\legacy\trainer.py", line 248, in train
detection_model = create_model_fn()
File "C:\Users\arfan\Documents\TensorFlow\models\research\object_detection\builders\model_builder.py", line 122, in build
raise ValueError('Unknown meta... less
In the Tensorflow ML Basics with Keras tutorial for making a basic text classification, when preparing the trained model for export, the tutorial suggests including the... moreIn the Tensorflow ML Basics with Keras tutorial for making a basic text classification, when preparing the trained model for export, the tutorial suggests including the TextVectorization layer into the Model so it can "process raw strings". I understand why to do this.
But then the code snippet is:
export_model = tf.keras.Sequential()
Why when preparing the model for export, does the tutorial also include a new activation layer layers.Activation('sigmoid')? Why not incorporate this layer into the original model? less
I don't understand which accuracy in the output to use to compare my 2 Keras models to see which one is better.
Do I use the "acc" (from the training data?) one or the "val acc"... moreI don't understand which accuracy in the output to use to compare my 2 Keras models to see which one is better.
Do I use the "acc" (from the training data?) one or the "val acc" (from the validation data?) one?
There are different accs and val accs for each epoch. How do I know the acc or val acc for my model as a whole? Do I average all of the epochs accs or val accs to find the acc or val acc of the model as a whole?Model 1 Output
Train on 970 samples, validate on 243 samples
Epoch 1/20
0s - loss: 0.1708 - acc: 0.7990 - val_loss: 0.2143 - val_acc: 0.7325
Epoch 2/20
0s - loss: 0.1633 - acc: 0.8021 - val_loss: 0.2295 - val_acc: 0.7325
Epoch 3/20
0s - loss: 0.1657 - acc: 0.7938 - val_loss: 0.2243 - val_acc: 0.7737
Epoch 4/20
0s - loss: 0.1847 - acc: 0.7969 - val_loss: 0.2253 - val_acc: 0.7490
Epoch 5/20
0s - loss: 0.1771 - acc: 0.8062 - val_loss: 0.2402 - val_acc: 0.7407
Epoch 6/20
0s - loss: 0.1789 - acc: 0.8021 - val_loss: 0.2431 - val_acc: 0.7407
Epoch 7/20
0s - loss: 0.1789 - acc: 0.8031 -... less
