"Efficient Distributed Training with Determined AI"
1- Video audio quality is very low use earphones or headphones
cifar10_cnn_keras_tensor.py
This code uses TensorFlow to load the CIFAR-10 dataset, normalize the pixel values, define a CNN model, compile the model with an optimizer and loss function, train the model on the training data, evaluate the model on the test data, and plot the accuracy and loss curves for both the training and validation data. The model architecture consists of three convolutional layers followed by max pooling, a flatten layer, and two dense layers. The model is trained for up to 50 epochs or until a validation accuracy of 90% is achieved, whichever comes first. After training, the model is evaluated on the test data and the test accuracy is printed. Note that this code assumes that TensorFlow and Keras are already installed in your JupyterLab environment.