DianoiaML

DianoiaML is an experimental Keras-like deep learning framework.

The user guide and the To-Do list can be found here.

Environment: Julia 1.6.1

Features:

• Network
  • Sequential
  • GAN
  • ResNet
• Layer
  • Flatten
  • Constructive
  • Dense
  • Convolutional2D
  • MaxPooling2D
  • UpSampling2D
  • Transposed Convolutional2D
  • Dropout
  • Residual
• Activation Function
  • ReLU
  • Sigmoid
  • Softmax
  • tanh
• Loss Function
  • Quadratic Loss
  • Categorical Cross Entropy Loss
  • Binary Cross Entropy Loss
  • Mean Squared Error
• Monitor
  • Absolute
  • Classification
• Optimizer
  • Minibatch Gradient Descent
  • Stochastic Gradient Descent
  • Adam
  • AdaBelief
  • Genetic Algorithm
• Tools
  • Model Management
  • One Hot

An example of Sequential network:

using MLDatasets, DianoiaML

train_x, train_y = MNIST.traindata()
test_x, test_y = MNIST.testdata()
dict = Dict{Int64, Int64}(1=>1, 2=>2, 3=>3, 4=>4, 5=>5, 6=>6, 7=>7, 8=>8, 9=>9, 0=>10)

model = Sequential()
model.add_layer(model, Conv2D; filter=32, input_shape=(28,28,1), kernel_size=(3,3), activation_function=ReLU)
model.add_layer(model, MaxPooling2D; pool_size=(2,2))
model.add_layer(model, Conv2D; filter=64, kernel_size=(3,3), activation_function=ReLU)
model.add_layer(model, MaxPooling2D; pool_size=(2,2))
model.add_layer(model, Flatten;)
model.add_layer(model, Dense; layer_size=128, activation_function=ReLU)
model.add_layer(model, Dense; layer_size=10, activation_function=Softmax_CEL)

Adam.fit(model=model, input_data=Array{Float32}(reshape(train_x, 28,28,1,60000)), output_data=oneHot(train_y, 10, dict),
        loss_function=Categorical_Cross_Entropy_Loss, monitor=Classification, epochs=10, batch=128)

An example of GAN:

using MLDatasets, DianoiaML

train_x, train_y = MNIST.traindata()
test_x, test_y = MNIST.testdata()
dict = Dict{Int64, Int64}(1=>1, 2=>2, 3=>3, 4=>4, 5=>5, 6=>6, 7=>7, 8=>8, 9=>9, 0=>10)

function noise()
    return reshape([rand(1.0f0:1.0f0:10.0f0), rand(Float32)], (2,1))
end

model = GAN(noise)

model.add_Glayer(model, Dense; input_shape=(2,), layer_size=16, activation_function=ReLU)
model.add_Glayer(model, Dense; layer_size=64, activation_function=ReLU)
model.add_Glayer(model, Constructive; shape=(8,8,1))
model.add_Glayer(model, UpSampling2D; size=(2,2), activation_function=None)
model.add_Glayer(model, Flatten;)
model.add_Glayer(model, Dense; layer_size=256, activation_function=ReLU)
model.add_Glayer(model, Dense; layer_size=784, activation_function=ReLU)
model.add_Glayer(model, Constructive; shape=(28,28,1))

model.add_Dlayer(model, Conv2D; filter=16, kernel_size=(3,3), activation_function=ReLU)
model.add_Dlayer(model, Conv2D; filter=32, kernel_size=(3,3), activation_function=ReLU)
model.add_Dlayer(model, MaxPooling2D; kernel_size=(2,2), activation_function=None)
model.add_Dlayer(model, Flatten;)
model.add_Dlayer(model, Dense; layer_size=128, activation_function=ReLU)
model.add_Dlayer(model, Dense; layer_size=64, activation_function=ReLU)
model.add_Dlayer(model, Dense; layer_size=2, activation_function=Sigmoid)

SGD.fit(model=model, input_data=Array{Float32}(reshape(train_x, 28,28,1,60000)), output_data=oneHot(train_y, 10, dict),
        loss_function=Binary_Cross_Entropy_Loss, monitor=Classification, epochs=50, batch=128)

An example of ResNet:

using MLDatasets, DianoiaML

train_x, train_y = MNIST.traindata()
test_x, test_y = MNIST.testdata()
dict = Dict{Int64, Int64}(1=>1, 2=>2, 3=>3, 4=>4, 5=>5, 6=>6, 7=>7, 8=>8, 9=>9, 0=>10)


resnet = ResNet()
resnet.add_layer(resnet, Dense; input_shape=(128,), layer_size=64, activation_function=ReLU)
resnet.add_layer(resnet, Dense; layer_size=64, activation_function=ReLU)
resnet.add_layer(resnet, Dense; layer_size=128, activation_function=ReLU)

model = Sequential()
model.add_layer(model, Conv2D; filter=32, input_shape=(28,28,1), kernel_size=(3,3), activation_function=ReLU)
model.add_layer(model, MaxPooling2D; pool_size=(2,2))
model.add_layer(model, Conv2D; filter=64, kernel_size=(3,3), activation_function=ReLU)
model.add_layer(model, MaxPooling2D; pool_size=(2,2))
model.add_layer(model, Flatten;)
model.add_layer(model, Dropout; rate=0.5)
model.add_layer(model, Dense; layer_size=128, activation_function=ReLU)
model.add_layer(model, Residual; resnet=resnet)
model.add_layer(model, Dense; layer_size=10, activation_function=Softmax_CEL)

Adam.fit(model=model, input_data=Array{Float32}(reshape(train_x, 28,28,1,60000)), output_data=oneHot(train_y, 10, dict),
        loss_function=Categorical_Cross_Entropy_Loss, monitor=Classification, epochs=10, batch=128)

Please feel free to leave comments, trouble-shootings or advice (which are very valuable for me).