In the first chapter of Deep Learning with TensorFlow, it gives an example on how to build a simple neural network for recognizing handwritten digits. According to its description, the code bundle for the book can be found at GitHub. From the context, I think section Running a simple TensorFlow 2.0 net and establishing a baseline uses the code same
Tag: tensorflow
Is the linear model trainable in Convolution or NN?
If I’ve an following layer Is this layer trainable? As we know derivative of linear function is constant, so in this cases will the weight get ever updated? Situation like also Answer The layer is trainable. Your data will be approximated by linear function. Training process is finding a function which is the best approximation of your data. If you
What is Keras’ Tokenizer fit_on_sequences used for?
I’m familiar with the method ‘fit_on_texts’ from the Keras’ Tokenizer. What does ‘fit_on_sequences’ do and when is it useful? According to the documentation, it “Updates internal vocabulary based on a list of sequences.”, and it takes as input: ‘A list of sequence. A “sequence” is a list of integer word indices.’. When is this useful? For fitting on texts, I
ValueError when using vae.fit in Keras
I am creating a vae model over the mnist dataset with hopes of plotting the loss function against the epochs. However I am experiencing some issues and was unable to find a solution online. During my imports I have the following imports (just to give some context to the code): I have also created a function called bn for my
How do I interpret my BERT output from Huggingface Transformers for Sequence Classification and tensorflow?
Short TL;DR: I am using BERT for a sequence classification task and don’t understand the output I get. This is my first post, so please bear with me: I am using bert for a sequence classification task with 3 labels. To do this, I am using huggingface transformers with tensorflow, more specifically the TFBertForSequenceClassification class with the bert-base-german-cased model (yes,
I created a CIFAR10 dataset learning model using a CNN model. Why is there an error?
I created a CIFAR10 dataset learning model using a CNN model. Why is there an error? How should I fix it? I did it in Google colab environment. This error occurred to me ValueError Traceback (most recent call last) in () /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs) ValueError: in user code: Thank you for your answering. Answer I think that your labels
Lstm for multivariate sequence prediction
I am confused with my Stacked LSTM model. Lstm has different type of applications. For example, in the image, two types of LSTM are shown, machine translation and video classification. My model is as follow. Input x has shape (1269, 4, 7). A few samples of input x and output y are as follows. Does this implementation fall into machine
max pooling across one dimension using keras
I have implemented a 3D-convolution neural network. The shape of my input is (500,10,4,1). I only want to convolve in first dimension such that it is ‘fully connected’ in second and third dimension in a way. So I use kernel size of (30,10,4). So far it’s fine. But when I do max pooling it reduces the second and third dimension
Tensorflow use : codec can’t decode byte XX in position XX : invalid continuation byte
i’m trying to train a model, I’m used the code that can be found here : https://medium.com/@martin.lees/image-recognition-with-machine-learning-in-python-and-tensorflow-b893cd9014d2 The thing is, even when I just copy / paste the code, I got a problem that I really don’t understand why I have it. I searched a lot on the tensorflow Github but found nothing to settle my problem. Here is the
Tensorflow example works fine on colab, but gives nan while running on jupyter lab
After working with the tensorflow’s example tutorial on colab: Basic regression: Predict fuel efficiency, which can be found here: https://www.tensorflow.org/tutorials/keras/regression. I’ve been trying to run this example on jupyter notebook via anaconda in order to be able to run it offline too. The code that one can find on the link works fine on google colab, but when I try