I am having a problem running a prediction using a saved MultiLayerPerceptronClassifier model. It throws error: The original mlpc in the pipeline had layers defined: My attempts to solve it: If I run the pipeline model and do predictions without first saving the model. I works with no error. But saving and re-using the model throws this error. Any help
Tag: neural-network
How to test a trained model saved in .pth.tar files?
I am working with CORnet-Z and I am building a separate test file. The model seems to be saved as .pth.tar files What would be the best approach to load this model and run evaluation and testing? Answer To test a model you need to load the state dictionary of your trained model and optimizer (if applicable). But, if you
Adaptation module design for stacking two CNNs
I’m trying to stack two different CNNs using an adaptation module to bridge them, but I’m having a hard time determining the adaption module’s layer hyperparameters correctly. To be more precise, I would like to train the adaptation module to bridge two convolutional layers: Layer A with output shape: (29,29,256) Layer B with input shape: (8,8,384) So, after Layer A,
How to add two separate layers on the top of one layer using pytorch?
I want to add two separate layers on the top of one layer (or a pre-trained model) Is that possible for me to do using Pytorch? Answer Yes, when defining your model’s forward function, you can specify how the inputs should be passed through the layers. For example: Where forward is a member of MyNet: Training The model should be
How to build a CNN model for MNIST fashion and test it with a another set of image from web?
Importing the data and splitting it into 4 for test and train [5] This is actually wrong. It should be printed as a trouser which is denoted by 1 cause in mnist dataset 5 is sandal Label Description 0 T-shirt/top 1 Trouser 2 Pullover 3 Dress 4 Coat 5 Sandal 6 Shirt 7 Sneaker 8 Bag 9 Ankle boot I
Forward Propagation for Neural Network
I am trying to create a forward-propagation function in Python 3.8.2. The inputs look like this: I am not using biases (not sure if they are that important and it makes my code very complicated) but I am using weights. The weights are stored in a list, Layer1W, I’m not sure how long to make it, but I think, len(Test_Training_Input)+len(Test_Training_Output)
Predicting in parallel using concurrent.futures of tensorflow.keras models
I am trying to implement some parallel jobs using concurrent.futures. Each worker requires a copy of a TensorFlow model and some data. I implemented it in the following way (MWE) simple_model() creates the model. clone_model clones a TensorFlow model. work represents an MWE of possible work. worker assigns the work in parallel. This is not working, it just stuck and
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
How do i embed nnv diagram in flask?
I want to embed a nnv render in flask but my code semms to not be rendering the neural-network diagram. My code: Answer The problem is that matplotlib is used to plot the neural network graph; in order to return an image as an HTTP response, you need to convert the plot to bytes.
Neural Network loss is significantly changing for same set of weights – Keras
I use pre-initialized weights as initial weights of the neural network, but the loss value keeps changing every time I train the model. If the initial weights are the same, then the model should predict exactly the same value every time I train it. But the mse keeps changing. Is there anything that I am missing? Answer You have all