tensorboardcolab | A library make TensorBoard working in Colab Google | Machine Learning library

Im trying to import the latest rc2 version of Tensorflow (2.2.0rc2 at this date) in Google Colab, but cant do it when installed from my setup.py install script.

When i install Tensorflow manually using !pip install tensorflow==2.2.0rc2 from a Colab cell, everything is ok and im able to import Tensorflow.

The next is how i have my dependencies installation setup in Google Colab:

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 to the train one and that is the main Reason it gave me very bad results.

I have a GRU model which has 12 features as inputs and I'm trying to predict output power. I really do not understand though whether I choose

• 1 layer or 5 layers
• 50 neurons or 512 neuron
• 10 epochs with a small batch size or 100 eopochs with a large batch size
• Different optimizers and activation functions
• Dropput and L2 regurlarization
• Adding more dense layer.
• Increasing and Decreasing learning rate

My results are always the same and doesn't make any sense, my loss and val_loss loss is very steep in first 2 epochs and then for the rest it becomes constant with small fluctuations in val_loss

Here is my code and a figure of losses, and my dataframes if needed:

Dataframe1: https://drive.google.com/file/d/1I6QAU47S5360IyIdH2hpczQeRo9Q1Gcg/view Dataframe2: https://drive.google.com/file/d/1EzG4TVck_vlh0zO7XovxmqFhp2uDGmSM/view

So I build a GRU model and I'm comparing 3 different datasets on the same model. I was just running the first dataset and set the number of epochs to 25, but I have noticed that my validation loss is increasing just after the 6th epoch, doesn't that indicate overfitting, am I doing something wrong?

I'm training a classifier to get a factor for an optimization. My data-set contains 800 samples as beginning (some are similar with just few modification).

I developed my model with TensorFlow using GoogleColab environment.

I have used a simple MLP for this problem, with 3 hidden layers each one has 256 nodes as first stage. I have also 64 classes .

I have variable length inputs and I had fixed this problem with "-1" padding.

with my actual features I know that I will get bad accuracy, but I did not expect zero accuracy and the very big loss.

This was my data-set after omitting some features that I have noticed that influence negatively the accuracy :

I'm trying to run a simple Keras script and use Google Colab with TensorBoard. Here's my code: