model = Chain(Dense(input=768, output=128, activation=Sigm()),
	      Dense(input=128, output=10, activation=nothing))

loss(model, x, y) = nll(model(x), y)

using Knet, KnetLayers
import Knet: Data
#Data
include(Knet.dir("data","mnist.jl"))
dtrn,dtst = mnistdata(xsize=(784,:)); # dtrn and dtst = [ (x1,y1), (x2,y2), ... ] where xi,yi are

#Model
HIDDEN_SIZES = [100,50]
(m::MLP)(x,y) = nll(m(x),y)
(m::MLP)(d::Data) = mean(m(x,y) for (x,y) in d)
model = MLP(784,HIDDEN_SIZES...,10)

#Train
EPOCH=10
progress!(sgd(model,repeat(dtrn,EPOCH)))

#Test
@show 100accuracy(model, dtst)

using KnetLayers

#Instantiate an MLP model with random parameters
mlp = MLP(100,50,20; activation=Sigm()) # input size=100, hidden=50 and output=20

#Do a prediction with the mlp model
prediction = mlp(randn(Float32,100,1))

#Instantiate a convolutional layer with random parameters
cnn = Conv(height=3, width=3, inout=3=>10, padding=1, stride=1) # A conv layer

#Filter your input with the convolutional layer
output = cnn(randn(Float32,224,224,3,1))

#Instantiate an LSTM model
lstm = LSTM(input=100, hidden=100, embed=50)

#You can use integers to represent one-hot vectors.
#Each integer corresponds to vocabulary index of corresponding element in your data.

#For example a pass over 5-Length sequence
rnnoutput = lstm([3,2,1,4,5];hy=true,cy=true)

#After you get the output, you may acces to hidden states and
#intermediate hidden states produced by the lstm model
rnnoutput.y
rnnoutput.hidden
rnnoutput.memory

#You can also use normal array inputs for low-level control
#One iteration of LSTM with a random input
rnnoutput = lstm(randn(100,1);hy=true,cy=true)

#Pass over a random 10-length sequence:
rnnoutput = lstm(randn(100,1,10);hy=true,cy=true)

#Pass over a mini-batch data which includes unequal length sequences
rnnoutput = lstm([[1,2,3,4],[5,6]];sorted=true,hy=true,cy=true)

#To see and modify rnn params in a structured view
lstm.gatesview