def test():
nn_input_size = 3 # actual input length will be +1 due to bias
fixed_point_10bit_precision = FixedPoint.FixedPointFormat(6,10)
nn = NeuralNetwork.createDemoFullyConnectNN(fixed_point_10bit_precision, nn_input_size)
##nn = NeuralNetwork.createDebugNN(fixed_point_10bit_precision, nn_input_size) #DEBUG
NN_on_CPU = NNs_on_CPU.initialize_NN_on_CPU(nn, fixed_point_10bit_precision)
input_vectors = []
input_vectors.append([3]*nn_input_size)
input_vectors.append([1]*nn_input_size)
target_vectors = []
target_vectors.append([1, 0, 0])
target_vectors.append([1, 0, 0])
learning_rate = 0.02
mini_batch_size = 10
# --- CPU ---#
NN_on_CPU.set_SGD_parameters(nn, mini_batch_size, learning_rate)
#--- ReCAM ---#
nn_weights_column = 0
nn_start_row = 0
NN_on_ReCAM = NNs_on_ReCAM.initialize_NN_on_ReCAM(nn_weights_column, nn_start_row)
NN_on_ReCAM.set_SGD_parameters(mini_batch_size, learning_rate)
NN_on_ReCAM.loadNNtoStorage(nn)
for training_iteration in range(1000):
##input_vector = input_vectors[0]
input_vector = input_vectors[training_iteration % 2]
##target_vector = target_vectors[0]
target_vector = target_vectors[training_iteration % 2]
#--- ReCAM ---#
(ReCAM_activations, ReCAM_deltas) = NN_on_ReCAM.SGD_train(nn, fixed_point_10bit_precision, nn_input_size, input_vector, target_vector) #DEBUG
##NN_on_ReCAM.SGD_train(nn, fixed_point_10bit_precision, nn_input_size, input_vector, target_output)
##ReCAM_pds = NN_on_ReCAM.get_NN_matrices(nn, NN_on_ReCAM.BP_partial_derivatives_column, nn_start_row) #DEBUG
ReCAM_weights = NN_on_ReCAM.get_NN_matrices(nn, nn_weights_column, nn_start_row)
print("Finished ReCAM Execution", training_iteration)
#--- CPU ---#
(CPU_pds, CPU_activations, CPU_deltas) = NN_on_CPU.SGD_train(nn, input_vector, target_vector) #DEBUG
###NN_on_CPU.SGD_train(nn, input_vector, target_output)
print("Finished CPU Execution", training_iteration)
# --- Verify weights match ---#
compare_activation_vectors(ReCAM_activations, CPU_activations, "activations") #DEBUG
compare_activation_vectors(ReCAM_deltas, CPU_deltas, "deltas") #DEBUG
#compare_NN_matrices(ReCAM_pds, CPU_pds, "partial derivatives") #DEBUG
compare_NN_matrices(ReCAM_weights, nn.weightsMatrices, "weights") #DEBUG
#storage.printArray()
#################################
#### Execute ####
#################################
#test()
def NN_on_ReCAM_test():
NN_Manager = initialize_NN_on_ReCAM()
nn_input_size = 3 # actual input length will be +1 due to bias
fixed_point_10bit = FixedPoint.FixedPointFormat(6,10)
nn = NeuralNetwork.createDemoFullyConnectNN(fixed_point_10bit, nn_input_size)
nn_weights_column = 0
nn_start_row = 0
NN_Manager.loadNNtoStorage(nn, nn_start_row, nn_weights_column)
input_column = 1
FP_MUL_column = 2
FP_accumulation_column = 3
input_start_row = nn_start_row
NN_Manager.loadInputToStorage(fixed_point_10bit, nn_input_size, input_column, input_start_row)
target_output = [1,2]
NN_Manager.loadTargetOutputToStorage(target_output, nn_start_row + nn.totalNumOfNetWeights, fixed_point_10bit, nn_weights_column)
FP_output = NN_Manager.feedforward(nn, nn_weights_column, nn_start_row, input_column, FP_MUL_column, FP_accumulation_column)
BP_output_column = FP_output[1]
BP_partial_derivatives_column = FP_MUL_column
activations_column = 1 if BP_output_column==3 else 3
BP_deltas_column = 4
BP_next_deltas_column = 5
NN_Manager.backPropagation(nn, nn_start_row, nn_weights_column, BP_output_column, BP_partial_derivatives_column,
activations_column, BP_deltas_column, BP_next_deltas_column)
############################################################
###### Execute
############################################################
#NN_on_ReCAM_test()
