python_for_microscopists
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bnsreenu
https://www.youtube.com/channel/UC34rW-HtPJulxr5wp2Xa04w?sub_confirmation=1
When changing patch_size from 256 to 512 and step size from 256 to 512 I get this error: "Error: AssertionError: ground truth has different shape (torch.Size([2, 1, 512, 512])) from...
After entering the following code: metrics=['accuracy', jacard_coef] def get_model(): return multi_unet_model(n_classes=n_classes, IMG_HEIGHT=IMG_HEIGHT, IMG_WIDTH=IMG_WIDTH, IMG_CHANNELS=IMG_CHANNELS) model = get_model() model.compile(optimizer='adam', loss=total_loss, metrics=metrics) #model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=metrics) model.summary() history1 = model.fit(X_train, y_train, batch_size =...
Hi Sreeni @bnsreenu , you're maintaining a fantastic resource here in this repository! I was just wondering under which conditions one could reuse the code published here. Would you mind...
As can be seen below, the following code was used when training a Unet model through Spyder. I ended up taking a look at a Youtube tutorial by DigitalSreeni,in episode...
correcting train/test shape and model input shape; Removing -1 from to_sequence;
In this notebook, why are bounding boxes used even though masks are provided?
Hi All, while i am executing 228-semantic segmentation of arieal images using U_net i encountered the below error. Please help me in resolving this. WARNING:matplotlib.image:Clipping input data to the valid...
I have mdified [this code](https://github.com/bnsreenu/python_for_microscopists/blob/master/178_179_variational_autoencoders_mnist.py) as follows: import keras from keras.layers import Conv2D, Conv2DTranspose, Input, Flatten, Dense, Lambda, Reshape #from keras.layers import BatchNormalization from keras.models import Model from keras.datasets import...
Only fit the label transformation on the train set and use it to transform both the train and the test set.
Since I was getting version issues with Flask, I upgraded to Flask 2.0.1, now it's working fine, you can merge it. I greatly appreciate your work, Sir
