Hello i am follow the time series forecasting tutorial in tensorflow https://www.tensorflow.org/tutorials/structured_data/time_series, I have the same project, the only difference is that I am using a different dataset, when evaluating the models, the model.evaluate () method returns an empty list, it does not return a value. When the model is trained with the fit() method, evaluation values are generated in
Tag: keras
Gradient Accumulation with Custom model.fit in TF.Keras?
Please add a minimum comment on your thoughts so that I can improve my query. Thank you. -) I’m trying to train a tf.keras model with Gradient Accumulation (GA). But I don’t want to use it in the custom training loop (like) but customize the .fit() method by overriding the train_step.Is it possible? How to accomplish this? The reason is
Keras: Classification report accuracy is different between model.predict accuracy for multiclass
Colab link is here: The data is imported the following was The model is trained the following way I am struggling with getting the right predicted categories and right true_categories to get the classification report to work: At the moment the output of the epoch is contradicting the classification report The validation set on the model returns while the classification
How to Calculate Confusion Matrix on test Data?
I want to plot a confusion matrix on the validation data. Specifically, I want to calculate a confusion matrix of the model output on the validation data. I tried everything online, but couldn’t figure it out. here is my model: Answer Here is a dummy example. DataSet Model Confusion Matrix Your interest is mostly here. Visualization Let’s visualize. Update Based
Non-zero binary accuracy but 0 accuracy in Keras classifer
I’m trying to train an LSTM classifier in TensorFlow. Here is a reproducible example Using BinaryAccuracy: Using Accuracy: I have used the ‘Accuracy’ metric for binary classification before, can someone explain why this is happening? Answer The metric is ‘accuracy’, not ‘Accuracy’.
shape of an output tensor after convolutional filter on a colour image
I find it difficult to understand a notion about tensors. For VGG (https://www.tensorflow.org/api_docs/python/tf/keras/applications/VGG16), we start from a batch of colour images (none,224,224,3) and apply 64 2D convolutional filters. At the output we obtain a tensor of (none,224,224,64), we can see this by making a summary of the model. However, a filter must treat all 3 colours and my intuition tells
Keras model.evaluate accuracy stuck at 50 percent while using ImageDataGenerator
I am trying to find the accuracy of my saved Keras model using model.evaluate. I have loaded in my model using this: I have a CSV file with two columns, one for the filename of an image and one for the label. Here is a sample: I have loaded this CSV into a pandas dataframe and fed it into an
How to add code lines when calling a function depending on an iterator on Python
I am trying to test many ML models using keras.models.Sequential. My idea is that once I have an iterator that looks like [num_layers, num_units_per_layers], for example [(1, 64),(2, (64,128))], to create a script using a kind of for loop running the iterator to be able to create a keras sequential model with the number of layers and units in each
Adaptive Threshold error: (-215:Assertion failed) src.type() == CV_8UC1 in function ‘adaptiveThreshold’
I am working on pre-trained vgg16 model, for that I need to have input size of image file to be (224,224,3). The code I am working on is: Help me in resolving the issue. Answer The error says the solution: src.type() == CV_8UC1 meaning you need to set your image type to the uint8 source So if you redefine your
Tensorflow dataset from numpy array
I have two numpy Arrays (X, Y) which I want to convert to a tensorflow dataset. According to the documentation it should be possible to run When doing this however I get the error: ValueError: Shapes (15, 1) and (768, 15) are incompatible This would make sense if the shapes of the numpy Arrays would be incompatible to the expected