Do I need to call model.fit() function every time after loading the model using Keras
I am using Keras to create model :
TypeError: Could not locate class ‘Sequential’ [closed]
Closed yesterday.
Incomprehensible shape error with one of the inputs of my non-sequential keras model
I wrote the following keras model
python: Layer “sequential” expects 1 input(s), but it received 48 input tensors
I’m trying to construct a simple neural network which gets a 2D Matrix (16×3) and outputs a single value, here is how I am trying to construct that network;
python: Layer “sequential” expects 1 input(s), but it received 48 input tensors
I’m trying to construct a simple neural network which gets a 2D Matrix (16×3) and outputs a single value, here is how I am trying to construct that network;
ValueError: called `set_weights(weights)` on layer ‘conv2d_7’ with a weight list of length 2, but the layer was expecting 1 weights
I’m trying to set weights on one of the layers within my model to no avail.
Tensorflow Incompatible shapes: [64] vs. [64,8,8,3] when calculating MSE
My code is failing due to incompatible shapes [64] vs [64, 8, 8, 3] when calculating MSE.
CNN model trained with Keras gives different output predictions for the same input
Hi I have trained a CNN model with keras similar to the example model they use on their website but with slightly smaller layers and an extra dropout layer at the end. The model build functions looks a little something like this:
Keras model input shapes are incompatible with the layer (despite being a compatible shape)
I’ve literally been pulling my hair out for the last 2 days trying to fix this. I have made a file test.py to test my model by predicting a single sample:
How to increase my MAE loss function in Python tensorflow Image Regression Model
I have 8000 images of circles on a black-background. Each label is the x coordinate of the circle. Each image is size (128,128,3). My training loss is starting at 2000 and ending at 10 while validation loss is staying around 200. Also, I am normalizing each image by dividing it by 255.