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ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors on a VQVAE

ValueError: Layer "sequential" expects 1 input(s), but it received 10 input tensors

ValueError: Unexpected result of `train_function` (Empty logs). for RNN

Simulate streaming learning using Tensorflow's fit() and evaluate() built-in methods

Add single tensor in model.fit()

splitting the data into training and testing in federated learning

Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)

tf.Dataset will not repeat without - WARNING:tensorflow:Your input ran out of data; interrupting training

Using TFDS datasets with Keras Functional API

Tff: define the usage of Tensorflow.take() function

QUESTION

ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors on a VQVAE

Asked 2022-Mar-21 at 06:09

I am training a VQVAE with this dataset (64x64x3). I have downloaded it locally and loaded it with keras in Jupyter notebook. The problem is that when I ran fit() to train the model I get this error: ValueError: Layer "vq_vae" expects 1 input(s), but it received 2 input tensors. Inputs received: [, ] . I have taken most of the code from here and adapted it myself. But for some reason I can't make it work for other datasets. You can ignore most of the code here and check it in the page, help is much appreciated.

The code I have so far:

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ANSWER

Answered 2022-Mar-21 at 06:09

This kind of model does not work with labels. Try running:

Source https://stackoverflow.com/questions/71540034

QUESTION

ValueError: Layer "sequential" expects 1 input(s), but it received 10 input tensors

Asked 2022-Mar-15 at 15:48

I am following TFF tutorials to build my FL model My data is contained in different CSV files which are considered as different clients. Following this tutorial, and build the Keras model function as following

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ANSWER

Answered 2022-Mar-15 at 15:48

A couple problems: Your data has ten separate features, which means you actually need 10 separate inputs for your model. However, you can also stack the features into a tensor and then use a single input with the shape (10,). Here is a working example, but please note that it uses dummy data and therefore may not make much sense in reality.

Create dummy data:

Source https://stackoverflow.com/questions/71428904

QUESTION

ValueError: Unexpected result of `train_function` (Empty logs). for RNN

Asked 2022-Mar-14 at 10:06

I am reproducing the examples of the chapter 16 of the book Hands-On Machine Learning of Aurélien Géron and found an error while trying to train a simple RNN model.

The error is the following:

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ANSWER

Answered 2022-Mar-14 at 10:06

The problem is that tokenizer.document_count considers the whole text as one data entry, which is why dataset_size equals 1 and train_size therefore equals 0, resulting in an empty data set. Try using the encoded array to get the true number of data entries:

Source https://stackoverflow.com/questions/71242821

QUESTION

Simulate streaming learning using Tensorflow's fit() and evaluate() built-in methods

Asked 2022-Mar-11 at 11:40

What I'm trying to achieve is to simulate a streaming learning method using Tensorflow's fit() and evaluate() methods.

What I have until now is a script like this, after getting some help from the community here:

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ANSWER

Answered 2022-Mar-11 at 11:40

You can try something like this:

Source https://stackoverflow.com/questions/71284872

QUESTION

Add single tensor in model.fit()

Asked 2022-Mar-10 at 14:39

I have a dataset made of tensors. A sample tensor looks like this:

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ANSWER

Answered 2022-Mar-10 at 14:22

Not too sure why you want to call model.fit in a loop but you can try something like this:

Source https://stackoverflow.com/questions/71417487

QUESTION

splitting the data into training and testing in federated learning

Asked 2022-Mar-10 at 13:35

I am new in federated learning I am currently experimenting with a model by following the official TFF documentation. But I am stuck with an issue and hope I find some explanation here.

I am using my own dataset, the data are distributed in multiple files, each file is a single client (as I am planning to structure the model). and the dependant and independent variables have been defined.

Now, my question is how can I split the data into training and testing sets in each client(file) in federated learning? like what we -normally- do in the centralized ML models The following code is what I have implemented so far: note my code is inspired by the official documentation and this post which is almost similar to my application, but it aims to split the clients as training and testing clients itself while my aim is to split the data inside these clients.

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ANSWER

Answered 2022-Mar-10 at 13:35

See this tutorial. You should be able to create two datasets (train and test) based on the clients and their data:

Source https://stackoverflow.com/questions/71330639

QUESTION

Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)

Asked 2022-Mar-08 at 14:22

I am training a Unet segmentation model for binary class. The dataset is loaded in tensorflow data pipeline. The images are in (512, 512, 3) shape, masks are in (512, 512, 1) shape. The model expects the input in (512, 512, 3) shape. But I am getting the following error. Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)

Here are the images in metadata dataframe.

Randomly sampling the indices to select the training and validation set

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ANSWER

Answered 2022-Mar-08 at 13:38

Use train_batches in model.fit and not train_images. Also, you do not need to use repeat(), which causes an infinite dataset if you do not specify how many times you want to repeat your dataset. Regarding your labels error, try rewriting your model like this:

Source https://stackoverflow.com/questions/71395504

QUESTION

tf.Dataset will not repeat without - WARNING:tensorflow:Your input ran out of data; interrupting training

Asked 2022-Mar-05 at 22:02

Using Tensorflow's Dataset generator without repeat works. However when I use repeat to double my train dataset from 82,000 to 164,000 for additional augmentation I "run out of data."

I've read that steps_per_epoch can "slow cook" models by allowing multiple epochs for a single pass through training data. It's not my intent, but even when I pass a small number of steps_per_epoch (which should create this slow cooking pattern), TF says I've ran out of data.

There is a case where TF says I'm close ("in this case, 120 batches"). I've attempted plus/minus this value but still getting errors with drop_remainder set to True to drop anything left over.

Error:

WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 82,000 batches). You may need to use the repeat() function when building your dataset. WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 120 batches). You may need to use the repeat() function when building your dataset.

Parameters Train Dataset 82,000 Val Dataset 12,000 Test Dataset 12,000 epochs (early stopping usually stops about 30) 100 batch_size 200

**batch_size is the same for model mini-batch and generator batches

Attempt steps_per_epoch Value Error steps_per_epoch==None None "..in this case, 82,000 batches" steps_per_epoch==train_len//batch_size 820 "..in this case, 82,000 batches" steps_per_epoch==(train_len//batch_size)-1 819 Training stops halfway "..in this case, 81,900 batches" steps_per_epoch==(train_len//batch_size)+1 821 Training stops halfway "..in this case, 82,100 batches" steps_per_epoch==(train_len//batch_size)//2 410 Training seems complete but errors before validation "..in this case, 120 batches" steps_per_epoch==((train_len//batch_size)//2)-1 409 Same as above:Training seems complete but errors before validation "..in this case, 120 batches" steps_per_epoch==((train_len//batch_size)//2)+1 411 Training seems complete but errors before validation "..in this case, 41,100 batches" steps_per_epoch==(train_len//batch_size)*2 1640 Training stops at one quarter "..in this case, 164,000 batches" steps_per_epoch==20 (arbitrarily small number) 20 Very surprisingly "..in this case, 120 batches"

Generators - goal is to repeat the train set two times:

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ANSWER

Answered 2022-Mar-04 at 10:13

Hmm, maybe you should not be explicitly defining the batch_size and steps_per_epoch in model.fit(...). Regarding the batch_size parameter in model.fit(...), the docs state:

[...] Do not specify the batch_size if your data is in the form of datasets, generators, or keras.utils.Sequence instances (since they generate batches).

This seems to work:

Source https://stackoverflow.com/questions/71345789

QUESTION

Using TFDS datasets with Keras Functional API

Asked 2022-Mar-02 at 20:05

I'm trying to train a neural network made with the Keras Functional API with one of the default TFDS Datasets, but I keep getting dataset related errors.

The idea is doing a model for object detection, but for the first draft I was trying to do just plain image classification (img, label). The input would be (256x256x3) images. The input layer is as follows:

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ANSWER

Answered 2022-Mar-02 at 07:54

I think the problem is that each image can belong to multiple classes, so I would recommend one-hot encoding the labels. It should then work. Here is an example:

Source https://stackoverflow.com/questions/71315426

QUESTION

Tff: define the usage of Tensorflow.take() function

Asked 2022-Mar-02 at 16:12

I am trying to mimic the federated learning implementation provided here: Working with tff's clientData in order to understand the code clearly. I reached to this point where I need clarification in.

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ANSWER

Answered 2022-Mar-02 at 12:57

In this line:

Source https://stackoverflow.com/questions/71322383

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