jupyter notebook online how to import keras or tensorflow

((trainX, trainY), (testX, testY)) = cifar10.load_data() We then perform our standard routine of loading the CIFAR-10 dataset from disk, scaling the pixel intensities to the range, and then one-hot encoding the labels: # load the training and testing data, then scale it into the The only command line argument we need is -weights, the path to the output directory that will store our serialized models during the training process. Next, let’s parse our command line arguments: # construct the argument parse and parse the argumentsĪp.add_argument('-w', '-weights', required=True, Take note of the ModelCheckpoint class imported on Line 4 - this class will enable us to checkpoint and serialize our networks to disk whenever we find an incremental improvement in model performance.

Lines 2-8 import our required Python packages. To get started, open a new file, name it cifar10_checkpoint_improvements.py, and insert the following code: # import the necessary packagesįrom sklearn.preprocessing import LabelBinarizerįrom pyimagesearch.nn.conv import MiniVGGNetįrom import ModelCheckpointįrom import SGDįrom import cifar10 In this example, we’ll be training the MiniVGGNet architecture on the CIFAR-10 dataset and then serializing our network weights to disk each time model performance improves. We define an “improvement” to be either a decrease in loss or an increase in accuracy - we’ll set this parameter inside the actual Keras callback. Looking for the source code to this post? Jump Right To The Downloads Section How to use the ModelCheckpoint callback with Keras and TensorFlowĪ good application of checkpointing is to serialize your network to disk each time there is an improvement during training.

Jupyter notebook online how to import keras or tensorflow