model_constraint = getattr(models, yamlConfig['KerasModelRetrain'])
# Instantiate new model with added custom constraints
if 'L1RegR' in yamlConfig:
keras_model = model_constraint(Input(shape=X_train_val.shape[1:]),
y_train_val.shape[1],
l1Reg=yamlConfig['L1Reg'],
l1RegR=yamlConfig['L1RegR'],
h5fName=options.dropWeights)
else:
keras_model = model_constraint(Input(shape=X_train_val.shape[1:]),
y_train_val.shape[1],
l1Reg=yamlConfig['L1Reg'],
h5fName=options.dropWeights)
print_model_to_json(keras_model,
options.outputDir + '/' + 'KERAS_model.json')
startlearningrate = 0.0001
adam = Adam(lr=startlearningrate)
keras_model.compile(optimizer=adam,
loss=[yamlConfig['KerasLoss']],
metrics=['accuracy'])
# Load pre-trained weights!
keras_model.load_weights(options.inputModel, by_name=True)
callbacks = all_callbacks(stop_patience=1000,
lr_factor=0.5,
lr_patience=10,
lr_epsilon=0.000001,
lr_cooldown=2,
#converted_w = convert_kernel(original_w)
converted_w = original_w
layer.set_weights(converted_w)
print 'Summary:'
totalDropped = sum(
[len(droppedPerLayer[layer.name]) for layer in model.layers])
for layer in model.layers:
print '%i weights dropped from %s out of %i weights' % (len(
droppedPerLayer[layer.name]), layer.name, layer.count_params())
print '%i total weights dropped out of %i total weights' % (
totalDropped, model.count_params())
print '%.1f%% compression' % (100. * totalDropped / model.count_params())
model.save(options.outputModel)
model.save_weights(options.outputModel.replace('.h5', '_weights.h5'))
print_model_to_json(model, options.outputModel.replace('.h5', '.json'))
# save binary tensor in h5 file
h5f = h5py.File(options.outputModel.replace('.h5', '_drop_weights.h5'),
'w')
for layer, binary_tensor in binaryTensorPerLayer.iteritems():
h5f.create_dataset('%s' % layer, data=binaryTensorPerLayer[layer])
h5f.close()
# plot the distribution of weights
if options.relative_weight_percentile is not None:
your_percentile = options.relative_weight_percentile
else:
your_percentile = stats.percentileofscore(allWeightsArray,
relative_weight_max)
#percentiles = [5,16,50,84,95,your_percentile]