Tag Archives: TensorFlow

Visualizing a MLP Neural Network with TensorBoard

The Multi-Layer Perceptron model is supported in Keras as a form of Sequential model container as MLP in its predefined layer type. For visualization of the training results, TensorBoard is handy with only a few line of code to add to the Python program.

log_dir="logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)

Finally add callbacks to the corresponding fitting model command to collect model information.

history = model.fit(X_train, Y_train, validation_split=0.2,
epochs=100, batch_size=10
,callbacks=[tensorboard_callback])

tfb1

Once the training is completed, start the TensorBoard and point browser to the designated port number.

Click on the Graph tab to see a detailed visualization of the model.
tfb2

Click on the Distributions tab to check the layer output.
tfb3

Click on the Histograms tab for a 3D visualization of the dense layers.
tfb4

 

 

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Experiencing Deep Learning with Jupyter and Anaconda

Most of the time my work with deep learning is done in command line interface with Python and TensorFlow. The clean and efficient syntax of the Python language and package design of TensorFlow almost eliminated the need of a complex Integrated Development Environment (IDE). But after trying out the free Google Colab service that provide a web based interface in Jupyter, I am going to set up one on my desktop that sports an Nvidia RTX2060 GPU.

Installation is easy, but be sure to run Anaconda console as Administrator on Windows platform. For running TensorFlow with GPU:

conda create -n tensorflow_gpuenv tensorflow-gpu
conda activate tensorflow_gpuenv

Managing multiple packages is much easier with Anaconda as it separate configurations into environments that can be customized. On my development machine, I can simply create a TensorFlow environment with GPU and then install Jupyter to enjoy its graphical interface.

Finally to activate Jupyter:

jupyter notebook

jupyterconsole.PNG

To see how Anaconda with Jupyter is flexible on the same machine, a comparison of a simple image pattern recognition program runs under Jupyter with and without GPU support.

jupytergpu
jupytercpu