def example_0():
# learns to repeat simple sequence from random inputs
np.random.seed(3)
# Number of iterations or epochs
nEpochs = 100;
# Internal cell widths
cellWidth = 100
# Number of random input numbers for each output
xSize = 50
## Initialise parameters
# Containg weights and derivatives of loss function wrt weights)
weights = LstmWeights(cellWidth, xSize)
## Prepare target outputs
outData = [0.5, 0.2, 0.1, 0.5]
# number of unfolded cells
nCells = len(outData)
# Initialise LSTM
trainLSTM = LstmNetwork(weights, nCells, cellWidth, xSize)
# Input data
inData = np.random.random([nCells, xSize]) # [4, 50]
# Train and sample at the same time
for epoch in range(nEpochs):
#
# Train model
#
# Input data and propagate forwards through time
trainLSTM.fwdProp(inData, weights)
# Evaluate loss function and back propagate through time
loss, grads = trainLSTM.bptt(outData, ToyLossLayer, weights)
# Clear inputs to start afresh for next epoch
trainLSTM.gotoStartCell()
# Collect the gradients
# Apply weight update
weights.update(grads, lr=0.1)
#weights.weightUpdate(lr=0.1)
#
# Test model and print logging information
#
# Sample from new model configured with the trained weights
testLSTM = LstmNetwork(weights, nCells, cellWidth, xSize)
testLSTM.fwdProp(inData, weights)
state = testLSTM.sample()
#pdb.set_trace()
# Print logging information
for ind in range(nCells):
print " Input %d rand. Target = %1.3f. Output = %1.3f. Delta = %1.3f" % (xSize, float(outData[ind]), float(state[ind]), outData[ind]-state[ind])
print "Epoch: %3d. loss: %5.10f\n" % (epoch, loss)
