DataBloom

im stupid ignore me

Didn’t see that the imgDimentions on the trainer was set to 225,225,1 but the detector was trying to find 255,255,1 images

its been a long day!

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Hi all, me again haha

Ive run into an error that has stumped me for about an hour;

Im trying to train a TensorFlow modal to detect a British one pence coin and a British 2 pence coin, while the model trained, when it detects the objects it crashes and gives me this error:

ValueError: Input 0 of layer “sequential” is incompatible with the layer: expected shape=(None, 225, 225, 1), found shape=(None, 255, 255, 1)

its finding the shape its supposed to find, but it says its not? Any advice would be greatly appreciated!

Thanks all!

Hey everyone,

I recently started doing some smaller TF projects and decided that it’s a good time to get some more formal training in the area and with TF.

I found a few interesting resources and was wondering if any of you have completed them and what your thought are? Are there any great resources I am missing on this list?

• https://www.coursera.org/professional-certificates/tensorflow-in-practice#courses
• https://www.coursera.org/specializations/deep-learning
• https://www.coursera.org/specializations/machine-learning-engineering-for-production-mlops#courses
• https://www.coursera.org/specializations/ai-for-medicine
• https://www.coursera.org/specializations/tensorflow-data-and-deployment
• https://www.coursera.org/specializations/tensorflow-advanced-techniques
• https://www.coursera.org/specializations/practical-data-science
• https://www.freecodecamp.org/learn/machine-learning-with-python/
• https://www.kaggle.com/learn/time-series
• Obviously the TF Documentation

For background, I am most interested in time series analysis, medical AI, and TF on embedded devices like the Google Coral but think a combination of a broad course + one specialized on these areas would be best.

What are your thoughts?

I’m trying to make a GAN model to reconstruct one image from another. So I have two MRI images I put one example of these images, which my input is the top one and the target is the bottom one. My problem here is that I’m trying to create a custom loss function that would penalize pixels that are from the background that are not inside of the brain. A professor told me about masking the image inside of the loss function so I’m kinda lost :/. Can anyone help me ?

Edit: My image didn’t upload so I’m attaching the link https://www.researchgate.net/publication/273638459/figure/fig4/AS:272623535915014@1442009924273/Brain-MRI-and-DTI-at-2years-of-age-A-Axial-T1-weighted-images-reveal-microcephaly-with.png

Hi all, Im trying to train a TensorFlow model to differentiate between a British one pence coin and a two pence coin.

I have 50 Photos for each at the dimension size 255 X 255, all photos are in focus and taken in natural sunlight.

Here is the training code I am using

https://pastebin.com/khjZrSgs

And here is the testing code I am using:

https://pastebin.com/1RZc35xK

I am pretty new to this and only following YouTube Tutorials, any advice would be fantastic!

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Thanks all!

Consider my input data.shape = [batch_size, 10_images, img_size, img_size, 1]

I want to extract patches of patch_size of each 10 images in a particular batch.

After that combine patches of all these 10 images as a whole in sequential order.

So that my output is like, output.shape = [batch_size, 10_images * patch_size * patch_size * 1]

Please help.

I am currently working on a tensorflow 1 project which I would like to migrate to tensorlfow 2, the project builds a tf model and then extracts the operations used in the model with the following code

graph = tf.get_default_graph()

operations = graph.get_operations()

I can’t find a way to do the same using tensorflow2, does anyone know how to do so? thanks!

I am a beginner in machine learning and I would like to forecast some pollution data.

I am using a dataset with values for pm2.5, pm10 and pm1 as features and I am predicting the values for the pm2.5. I built an LSTM network but the predicted values are quite from the real values.

What I used:

win_length=2 batch_size=32 num_features=3 train_generator=TimeseriesGenerator(x_train,y_train,length=win_length,sampling_rate=1,batch_size=batch_size) test_generator=TimeseriesGenerator(x_test,y_test,length=win_length,sampling_rate=1,batch_size=batch_size) 

`

The used model is LSTM:

model=tf.keras.Sequential() model.add(tf.keras.layers.LSTM(200,input_shape=(win_length,num_features),return_sequences=True)) model.add(tf.keras.layers.LeakyReLU(alpha=0.5)) model.add(tf.keras.layers.LSTM(128,return_sequences=True)) model.add(tf.keras.layers.LeakyReLU(alpha=0.5)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.LSTM(64,return_sequences=False)) model.add(tf.keras.layers.Dropout(0.3)) model.add(tf.keras.layers.Dense(1)) 

This is the data snippet and how the predicted values look compared to the original pm2.5 values: prediction snippet

How to increase the accuracy of the forecast? I am also attaching the jupyter notebook, which contains all the analysis: https://github.com/creativitylab/dataset/blob/main/pollution%20data.ipynb