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
Tag: tensorflow
Keras and Tensorflow OS resource requirement
I keep getting F tensorflow/core/platform/default/env.cc:73] Check failed: ret == 0 (11 vs. 0)Thread tf_data_private_threadpool creation via pthread_create() failed. errors during training, although the machine is quite powerful: altogether 64 logical cores ulimit -s gives 32768, ulimit -u gives 1030608 I want to train the following network with a bunch of online generated 512*512 grayscale images along with two additional parameters
Install Donkey car Python TensorFlow – Raspberry Pi
use Raspberry Pi 4 Model B 2GB Raspbian BULLSEYE (DEBIAN 11) ARMV71 package name “2022-04-04-raspios-bullseye-armhf-full” i try Install Donkeycar Python Code from official website when I enter installation code Error: donkeycar tensorflow-2.2.0-cp37-none-linux_armv7l.whl is not a supported wheel on this platform. Answer If you run the 64-bit Raspberry Pi OS you can install Tensorflow in the standard way – pip3 install
Keras, simple neural network Error Code (model.predict)
Do any of you know why I get the following error code? My Code : You can ignore the Integrator Part, I just want to know why the model.predict wont work. Here is the error: Answer The problem is with the lines: Here your model is setup to receive a rank 2 tensor as input, but you are only giving
While predicting on trained model I’ve getting an Image shape error
I use the deeptrack library (that also uses tensorflow) to train a model dealing with cell counting using UNet. This is the code defines the UNet model using deeptrack (dt) library: And this is the summary of the model I trained: And when I try to make a prediction with the model I trained, with a 256X256 image (both color
layer.get_weights() is not equal in the same model layers
Why not all the layer weights equal: Here is the output: The a_weights == b_weights are not all the “True”. What’s the problem? Answer Notice that the only time a_weights == b_weights is True, is when you are referencing a layer, which does not have any weights. np.array_equal is returning False because you are actually comparing lists of arrays and
Adam Optimizer Not Working on cost function
I wanted to make own neural network for MNIST data set and for that using tensorflow i am writing the code imported library and dataset then done one hot encoding and after all done the weights and baises assignment and then done the forward propagation with the random values and for back propagation and cost minimization used a loss function
Tensorflow Fused conv implementation does not support grouped convolutions
I did a neural network machine learning on colored images (3 channels). It worked but now I want to try to do it in grayscale to see if I can improve accuracy. Here is the code: You can see that I have changed the input_shape to have 1 single channel for grayscale. I’m getting an error: Node: ‘sequential_26/conv2d_68/Relu’ Fused conv
ValueError: Classification metrics unable to handle multiclass
I am trying to build a object classification model, but when trying to print out the classification report it returned a value error. ValueError: Classification metrics can’t handle a mix of multiclass and continuous-multioutput targets This is my current code: However, when I attempt to print out the classification report, it ran into this error: Answer Your problem is caused
Set random labels for images in tf.data.Dataset
I have a tf data dataset of images with a signature as seen below : All the labels in this dataset are 0. What I would like to do is change each of these labels to a random number from 0 to 3. My code is : This however just assigns 1 to all images as a label. The strange