I implemented a model with python using Keras which is series of convents layer that takes a 512*512 image and converts it to a tensor with the dimensions of 16*16. Im now trying to detect an object in this 16*16 tensor so that it gives me a 1 for detecting and a 0 otherwise. The problem is I don’t know
Tag: keras
Neural Network Results always the same
Edit: For anyone interested. I made it slight better. I used L2 regularizer=0.0001, I added two more dense layers with 3 and 5 nodes with no activation functions. Added doupout=0.1 for the 2nd and 3rd GRU layers.Reduced batch size to 1000 and also set loss function to mae Important note: I discovered that my TEST dataframe wwas extremely small compared
Implementing a minimal LSTMCell in Keras using RNN and Layer classes
I am trying to implement a simple LSTMCell without the “fancy kwargs” defaultly implemented in the tf.keras.layers.LSTMCell class, following a schematic model like this. It doesn’t really have a direct purpose, I would just like to practice implementing a more complex RNNCell than the one described here in the Examples section. My code is the following: However, when I tried
keras lstm error: expected to see 1 array
so i want to make a lstm network to run on my data but i get this message: ValueError: Error when checking input: expected lstm_1_input to have shape (None, 1) but got array with shape (1, 557) this is my code: Answer You need to change the input_shape value for LSTM layer. Also, x_train must have the following shape. So,
Could not load dynamic library ‘cudart64_101.dll’ on tensorflow CPU-only installation
I just installed the latest version of Tensorflow via pip install tensorflow and whenever I run a program, I get the log message: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library ‘cudart64_101.dll’; dlerror: cudart64_101.dll not found Is this bad? How do I fix the error? Answer Tensorflow 2.1+ What’s going on? With the new Tensorflow 2.1 release, the default tensorflow pip
Cannot use vggface-keras in Tensorflow 2.0
I am trying to use the keras-vggface library from https://github.com/rcmalli/keras-vggface to train a CNN. I have installed tensorflow 2.0.0-rc1, keras 2.3.1, cuda 10.1, cudnn 7.6.5 and the driver’s version is 418, the problem is that when i try to use the vggface model, as a convolutional base, i get an error, here is the code and the error Error! I
How to fix “ResourceExhaustedError: OOM when allocating tensor”
I wanna make a model with multiple inputs. So, I try to build a model like this. and the summary : _ But, when i try to train this model, the problem happens…. : Thanks for reading and hopefully helping me :) Answer OOM stands for “out of memory”. Your GPU is running out of memory, so it can’t allocate
How to use Model.fit which supports generators (after fit_generator deprecation)
I have got this deprecation warning while using Model.fit_generator in tensorflow: How can I use Model.fit instead of Model.fit_generator? Answer Model.fit_generator is deprecated starting from tensorflow 2.1.0 which is currently is in rc1. You can find the documentation for tf-2.1.0-rc1 here: https://www.tensorflow.org/versions/r2.1/api_docs/python/tf/keras/Model#fit As you can see the first argument of the Model.fit can take a generator so just pass it
CUDA Error: out of memory – Python process utilizes all GPU memory
Even after rebooting the machine, there is >95% of GPU Memory used by python3 process (system-wide interpreter). Note that memory consumption keeps even if there are no running training scripts, and I’ve never used keras/tensorflow in the system environment, only with venv or in docker container. UPDATED: The last activity was the execution of NN test script with the following
How to see the loss of the best epoch from early stopping in Keras?
I have managed to implement early stopping into my Keras model, but I am not sure how I can view the loss of the best epoch. The way I have defined the loss score, means that the returned score comes from the final epoch, not the best epoch. Example: So in this example, I would like to see the loss