Fix Python – NaN loss when training regression network

I have a data matrix in “one-hot encoding” (all ones and zeros) with 260,000 rows and 35 columns. I am using Keras to train a simple neural network to predict a continuous variable. The code to make the network is the following:
model = Sequential()
model.add(Dense(1024, input_shape=(n_train,)))

Fix Python – Tensorflow – ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float)

Continuation from previous question: Tensorflow – TypeError: ‘int’ object is not iterable
My training data is a list of lists each comprised of 1000 floats. For example, x_train[0] =
[0.0, 0.0, 0.1, 0.25, 0.5, …]

Here is my model:
model = Sequential()

model.add(LSTM(128, activation=’relu’,
input_shape=(1000, 1), return_sequences….

Fix Python – Deep-Learning Nan loss reasons

Perhaps too general a question, but can anyone explain what would cause a Convolutional Neural Network to diverge?
I am using Tensorflow’s iris_training model with some of my own data and keep getting

ERROR:tensorflow:Model diverged with loss = NaN.

Fix Python – Loading a trained Keras model and continue training

I was wondering if it was possible to save a partly trained Keras model and continue the training after loading the model again.
The reason for this is that I will have more training data in the future and I do not want to retrain the whole model again.
The functions which I am using are:
#Partly train model, first_classes….

Fix Python – 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/] Could not load dynamic library ‘cudart64_101.dll’; dlerror: cudart64_101.dll not found

Is this bad? How do I fix the error?

Fix Python – How to fix ‘Object arrays cannot be loaded when allow_pickle=False’ for imdb.load_data() function?

I’m trying to implement the binary classification example using the IMDb dataset in Google Colab. I have implemented this model before. But when I tried to do it again after a few days, it returned a value error: ‘Object arrays cannot be loaded when allow_pickle=False’ for the load_data() function.
I have already tried solving this, referring to a….

Fix Python – What is the use of verbose in Keras while validating the model?

I’m running the LSTM model for the first time.
Here is my model:
opt = Adam(0.002)
inp = Input(…)
x = Embedding(….)(inp)
x = LSTM(…)(x)
x = BatchNormalization()(x)
pred = Dense(5,activation=’softmax’)(x)

model = Model(inp,pred)

idx = np.random.permutation(X_train.shape[0])[idx], y_train[idx]….

Fix Python – Why is TensorFlow 2 much slower than TensorFlow 1?

It’s been cited by many users as the reason for switching to Pytorch, but I’ve yet to find a justification/explanation for sacrificing the most important practical quality, speed, for eager execution.
Below is code benchmarking performance, TF1 vs. TF2 – with TF1 running anywhere from 47% to 276% faster.
My question is: what is it, at the graph or….

Fix Python – Where do I call the BatchNormalization function in Keras?

If I want to use the BatchNormalization function in Keras, then do I need to call it once only at the beginning?
I read this documentation for it:
I don’t see where I’m supposed to call it. Below is my code attempting to use it:
model = Sequential()
keras.layers.normalization.BatchNormalization(epsilon=1e-06, ….