I found it tedious to rewrite the basic Keras code over and over again. Thats why I built this code snippets to save time.
This extension will solve the issue this issue with a simple ! + the name of whatever you are looking for.
Commands
Main Commands
Command
**Description **
!setup
Keras 2.4.0 initial setup
!datasetimg
Load custom image dataset from directory
!datasettext
Load custom textual dataset from directory
Commands for using Keras Datasets
Command
**Description **
!mnist
Loads the dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images.
!fashion
Loads the dataset of 60,000 28x28 grayscale images of 10 fashion categories, along with a test set of 10,000 images.
!cifar10
Loads the dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 10 categories
!cifar100
Loads the dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 100 fine-grained classes that are grouped into 20 coarse-grained classes.
!imbd
Loads the dataset of 50,000 32x32 color training images and 10,000 test images, labeled over 100 fine-grained classes that are grouped into 20 coarse-grained classes.
!boston or !house
Loads the dataset samples containing 13 attributes of houses at different locations around the Boston suburbs in the late 1970s.
Commands for using Keras Optimizers
Command
**Description **
!sgd
Keras 2.4.0 SGD
!adam
Keras 2.4.0 Adam
!rmsprop
Keras 2.4.0 RMSprop
!adadelta
Keras 2.4.0 Adadelta
!adagrad
LKeras 2.4.0 Adagrad
!adamax
Keras 2.4.0 Adamax
!nadam
Keras 2.4.0 Nadam
!ftrl
Keras 2.4.0 Ftrl
Commands for using Keras Losses
Command
**Description **
!sparse
SparseCategoricalCrossentropy
!bin
BinaryCrossentropy
!cat
CategoricalCrossentropy
!poisson
Poisson
!binloss
binary_crossentropy
!catloss
categorical_crossentropy
!sparseloss
sparse_categorical_crossentropy
!poiloss
poisson
!kld
KLDivergence
!kld
kullback_leibler_divergence
Commands for using Keras Accuracy
Command
**Description **
!acc
Accuracy
!binaryacc
BinaryAccuracy
!catagoricalacc
CategoricalAccuracy
!topkacc
TopKCategoricalAccuracy
!sparseacc
SparseTopKCategoricalAccuracy
Commands for using Keras deep learning algorithms
Command
**Description **
!vgg16
Extract features with VGG16
!vgg19
Extract features from an arbitrary intermediate layer with VGG19
!resnet50
Classifies ImageNet classes with ResNet50
!inceptionv3
Fine-tune InceptionV3 on a new set of classes
!inceptionv3custom
Build InceptionV3 over a custom input tensor
Commands for using Keras Transfer Learning Boiler plate of different algorithms
