I have added following libraries and half part of the code is executing. In second half I get this error. Libraries added: Error: AttributeError: module ‘keras.preprocessing.image’ has no attribute ‘img_to_array’ I was following this code and changes the libraries too still can’t resolve the issue. https://www.analyticsvidhya.com/blog/2021/06/k-means-clustering-and-transfer-learning-for-image-classification/ Answer It has now moved to tf.keras.utils.img_to_array. See the docs
Tag: keras
Save keras preprocessing layer
I have a model where I’m doing different preprocessing, outside the model itself. One part of the preprocessing is using a category encoder based on keras with: I apply this than with to my pandas dataframe. Now I want to store my model and in order to store the model I also have to store the 2 preprocessing layers cat_index
Obtaining the parameters of layers after concatenation in Keras
I’m trying to get the output and input parameters after concatenation in keras, more specifically in “concat_” and “hidden 6” layers. Is there way to obtain the parameters by layer name? Also, is there any way to run the model (after training) until the concatenation point? Answer You could give each layer that you want to later retrieve, a specific
Reshaping problem (Input to reshape is a tensor with 10 values, but the requested shape has 1)
I’m trying to recreate this work using my own dataset: https://www.kaggle.com/code/amyjang/tensorflow-pneumonia-classification-on-x-rays/notebook I’ve made some slight tweaks to the code to accommodate my data but I don’t think that is what is causing an issue here; it could be though of course. My code: And the error: I can gather from the error that I have a mismatch in resizing, I
Access denied to subclassed Keras model when loaded from a .py script
I have the following subclassed Keras model which I have already trained. I want to be able to call all the methods in B_frame_CNN (e.g., get_embedding()) on the loaded model. The following code works perfectly and does what I need when run in an ipython notebook. However, when I run it in a python script (.py), I get the following
Model was constructed with shape (None, 65536) but it was called on an input with incompatible shape (None, 65536, None)
For reference the full error is here: I am using kymatio to classify audio signals. Before constructing the model I use tensorflow’s tf.keras.utils.audio_dataset_from_directory to create the training and testing sets. The audio samples are of shape (65536,) before the sets are created. To create the sets I use the following code: The element_spec of the train_dataset is (TensorSpec(shape=(None, 65536, None),
Masking layer vs attention_mask parameter in MultiHeadAttention
I use MultiHeadAttention layer in my transformer model (my model is very similar to the named entity recognition models). Because my data comes with different lengths, I use padding and attention_mask parameter in MultiHeadAttention to mask padding. If I would use the Masking layer before MultiHeadAttention, will it have the same effect as attention_mask parameter? Or should I use both:
Tensorflow accuracy from model.predict does not match final epoch val_accuracy of model.fit
I am trying to match the accuracy of a model.predict call to the final val_accuracy of model.fit(). I am using tf dataset. The dataset setup for train_ds is similar. I prefetch both… Than I get the labels for the val_ds so I can use them later My model Compiles fine Seems to fit fine The last epoch output Epoch 10:
Input 0 is incompatible with layer model_2
i have a generator rev_generator that yields a tuple of two elements (numpyarray of shape (1279,300,1) , int value: 0 or 1) then i pass it to: and then a simple model but when i call fit it throws me an error: Answer If you are using the tf.data.Dataset API, you should set the batch size explicitly and not in
Create a ML model with tensorflow that predicts a values at any given time range at hourly intervals
I am pretty new to ML and completely new to creating my own models. I have went through tensorflows time-series forecasting tutorial and other LSTM time series examples on how to predict with multi-variate inputs. After trying multiple examples I think I realized that this is not what I want to achieve. My problem involves a dataset that is in