I am using TensorFlow's eager execution and I would like to visualize embeddings in TensorBoard. I use the following code to setup the visualization:
self._writer = tf.contrib.summary.create_file_writer('path')
embedding_config = projector.ProjectorConfig()
embedding = embedding_config.embeddings.add()
embedding.tensor_name = self._word_embeddings.name
embedding.metadata_path = 'metadata.tsv'
projector.visualize_embeddings(self._writer, embedding_config)
where self._word_embeddings is my variable for the embeddings. However, when executing this script TensorFlow throws the following error message:
logdir = summary_writer.get_logdir()
AttributeError: 'SummaryWriter' object has no attribute 'get_logdir'
Has anybody experienced something similar and has an idea how to get the embedding visualization to run in eager mode?
I am using TensorFlow 1.10.0.
Any kind of help is greatly appreciated!
If you only care about visualization, and since you are working in eager mode, things can be much simpler.
As I can see, you already have your metadata.TSV file set. The only thing left, is to write your embedding matrix to a TSV file. Like, just a for loop over the matrix rows, with the values TAB separated.
Last step, you can load the tensorboard projector online, without installing it via: http://projector.tensorflow.org/ and upload your data. You have to upload the embedding file, and the metadata file separately, in two simple steps.
Related
I am trying to load 300W_lp dataset in tensorflow.
I downloaded and extracted the dataset manually at C:/datasets/the300w
Now when I try to load dataset into tensorflow using
the300w = tfds.load('the300w_lp',data_dir='C:\datasets\the300w', download=False)
it gives me error
Dataset the300w_lp: could not find data in C:\datasets\the300w. Please make sure to call dataset_builder.download_and_prepare(), or pass download=True to tfds.load() before trying to access the tf.data.Dataset object.
Please help. How to load dataset in tensorflow?
Try to use plain old
dataset = tfds.load('the300w_lp')
It works fine for me. Maybe You somehow incorrectly unzipped the dataset file? If you have spare time, try the above code and see if it works.
Just a simple way to tackle this issue. Simply run the above command in google colab, grab a portion of the dataset object, download it and use it for your own purpose :)
I'm using Keras with Cern ROOT and its analysis package TMVA. The way it works is that I use Keras to initialize the NN, then save it to a file, then TMVA loads that file in. The problem is that I am using custom metrics when setting up the neural network, and when doing this, Keras wants you to do something like
models.load_model(model_path, custom_objects={"my_object":my_object})
Unfortunately, the way that TMVA takes arguments requires that I only supply the filename of the model file being used. However, based on the error messages that I am getting, it is clear that it is simply using Keras to load the model in. My question is, how do I force Keras to automatically load my custom objects in without having to use the above line, as this is incompatible with the package I'm trying to use.
I am trying to check if my .onnx model is correct, and need to run inference to verify the output for the same.
I know we can run validation on .mlmodel using coremltools in Python - basically load the model and input and get the prediction. I am trying to do a similar thing for the .onnx model.
I found the MXNet framework but I can't seem to understand how to import the model - I just have the .onnx file and MXNet requires some extra input besides the onnx model.
Is there any other simple way to do this in Python? I am guessing this is a common problem but can't seem to find any relevant libraries/frameworks to do this as easily as coremltools for .mlmodel.
I do not wish to convert .onnx to another type of model (like say PyTorch) as I want to check the .onnx model as is, not worrying if the conversion was correct. Just need a way to load the model and input, run inference and print the output.
This is my first time encountering these formats, so any help or insight would be appreciated.
Thanks!
I figured out a way to do this using Caffe2 - just posting in case someone in the future tries to do the same thing.
The main code snippet is:
import onnx
import caffe2.python.onnx.backend
from caffe2.python import core, workspace
import numpy as np
# make input Numpy array of correct dimensions and type as required by the model
modelFile = onnx.load('model.onnx')
output = caffe2.python.onnx.backend.run_model(modelFile, inputArray.astype(np.float32))
Also it is important to note that the input to run_model can only be a numpy array or a string. The output will be an object of the Backend.Outputs type. I was able to extract the output numpy array from it.
I was able to execute inference on the CPU, and hence did not need the Caffe2 installation with GPU (requiring CUDA and CDNN).
I'm a beginner for python and TensorFlow. Following the instruction of "Reading data" in TensorFlow website, I want to load some data in to my project in python. That is my code, very simple
import tensorflow as tf
files = tf.train.match_filenames_once("*.txt")
print(files)
And the result is
Tensor("matching_filenames/read:0", dtype=string)
I have put the data which I want to read to the working space of this project. Why it still told me that matching file name is 0?
In addition, the data I want to read is a one dimensional data list, each double per line. And the file size is about 100W+ numbers.
The IDE I'm using is pycharm
Thank you!
Your variable Files is a Tensor (a node in the TensorFlow graph). You need to run it in a TensorFlow session, in order to get access to its value.
files = tf.train.match_filenames_once("*.txt")
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print(sess.run(files))
I would advise you to read the official documentation to know more about TensorFlow, the Tensors, and the TensorFlow graph.
I'm having an issue i can't manage to solve.
I'm just approaching the Super Resolution Images on Python and i found this on github: https://github.com/titu1994/Image-Super-Resolution
I think this is exactly what i need for my project.
So i just install everything i need to run it and i run it with this:
python main.py (path)t1.bmp
t1.bmp is an image stored in the "input-images" directory so my command is this:
python main.py C:\Users\cecilia....\t1.bmp
The error i get is this:
http://imgur.com/X3ssj08
http://imgur.com/rRSdyUb
Can you please help me solving this? (The code i'm using is the one on the github i linked)
Thanks in advance
The very first line on the Readme in the github link that you give says that the code is designed for theano only. Yet in your traceback it shows that you are using tensorflow as backend...
The error that you are having is typical of having the wrong image format for the used backend. You have to know that for convolutional networks, Theano and tensorflow have different conventions. Theano expects the following order for the dimensions (batch, channels, nb_rows , nb_cols) and tensorflow (batch, nb_rows, nb_cols, channels). The first is known as "channels_first" and the other "channels_last". So what happens is that the code you are trying to run (which is explicitly said to be designed for Theano) organises the data to match the channels_first format, which causes tensorflow to crash because the dimensions don't match what it expects.
Bottom line: use theano, or change the code appropriately to make it work on tensorflow.