Yeah, the syntax has changed in T2.0. Here’s the correct piece: tf.compat.v1.GraphDef() # -> instead of tf.GraphDef() tf.compat.v2.io.gfile.GFile() # -> instead of tf.gfile.GFile()
y_true and y_pred The tensor y_true is the true data (or target, ground truth) you pass to the fit method. It’s a conversion of the numpy array y_train into a tensor. The tensor y_pred is the data predicted (calculated, output) by your model. Usually, both y_true and y_pred have exactly the same shape. A few … Read more
I have the following understanding of this function keras.backend.function. I will explain it with the help of a code snippet from this. The part of code snippet is as follows final_conv_layer = get_output_layer(model, “conv5_3”) get_output = K.function([model.layers[0].input], [final_conv_layer.output, model.layers[-1].output]) [conv_outputs, predictions] = get_output([img]) In this code, there is a model from which conv5_3 layer is … Read more
All the optimizers have a private variable that holds the value of a learning rate. In adagrad and gradient descent it is called self._learning_rate. In adam it is self._lr. So you will just need to print sess.run(optimizer._lr) to get this value. Sess.run is needed because they are tensors.
To make sure that you have “at least steps_per_epoch * epochs batches“, set the steps_per_epoch to steps_per_epoch = len(X_train)//batch_size validation_steps = len(X_test)//batch_size # if you have validation data You can see the maximum number of batches that model.fit() can take by the progress bar when the training interrupts: 5230/10000 [==============>……………] – ETA: 2:05:22 – loss: … Read more
Run the following command in the terminal: export CUDA_HOME=/usr/local/cuda-X.X Where you replace X.X by the first two digits of your version number (can be found out e.g. via nvcc –version).
For starters, a neural network can model any function (not just linear functions) Have a look at this – http://neuralnetworksanddeeplearning.com/chap4.html. A Neural Network has got non linear activation layers which is what gives the Neural Network a non linear element. The function for relating the input and the output is decided by the neural network … Read more
This was caused by a Python version issue for me. I had the absl package installed on my Python 2.x, but my Python 3.x didn’t have it. So I just made sure that both Pythons on my machine had the package installed: pip install absl-py pip3 install absl-py
I think I can help you with providing a cudnn64_8.dll file (this is the download link: https://www.dll-files.com/cudnn64_8.dll.html). When you get the file, you can just put in your bin directory. For example, usually in windows platform, you can put it into C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.3\bin.