def getGeneratorUpdates(self, loss, aLearningRate, aBeta1):
LR = aLearningRate
mlp = self.gen
if self.IS_BINARY:
# W updates
# extract 'binary' parameters only
Wb_list = lasagne.layers.get_all_params(self.gen, binary=True)
for eW in Wb_list:
#print( 'eW:', type(eW), eW )
pass
# Make a list of the gradients w.r.t. the binary parameters
W_grad_list = binary_net.compute_grads(loss, mlp)
#print('W_grad_list', type(W_grad_list), W_grad_list)
# Update function map(OrderedDict) with ADAM learning method
updates_b0 = lasagne.updates.adam(loss_or_grads=W_grad_list,
params=Wb_list,
learning_rate=LR,
beta1=aBeta1)
# clipping & scaling for binarization
updates_b1 = binary_net.clipping_scaling(updates_b0, mlp)
# other parameters updates
Wr_list = lasagne.layers.get_all_params(mlp,
trainable=True,
binary=False)
for Wr in Wr_list:
#print('Wr:', type(Wr), Wr)
pass
# Marging the parameters : binary params + other params
updates = OrderedDict(updates_b1.items() +
lasagne.updates.adam(loss_or_grads=loss,
params=Wr_list,
learning_rate=LR,
beta1=aBeta1).items())
else:
Wr_list = lasagne.layers.get_all_params(mlp, trainable=True)
updates = lasagne.updates.adam(loss_or_grads=loss,
params=Wr_list,
learning_rate=LR,
beta1=aBeta1)
return updates
# the T.invert function seems to react differently depending on theano versions...
train_0_when_0 = T.sum(T.invert(
T.or_(T.argmax(train_output, axis=1), T.argmax(target, axis=1))),
dtype=theano.config.floatX)
# if this does not work, try
# train_0_when_0 = batch_size - T.sum(T.or_(T.argmax(train_output,axis=1),T.argmax(target,axis=1))),dtype=theano.config.floatX)
train_precision = train_0_when_0 / (train_0_when_0 + train_0_when_1
) # TP/(TP+FP)
train_recall = train_0_when_0 / (train_0_when_0 + train_1_when_0
) # TP/(TP+FN)
if binary:
# W updates
W = lasagne.layers.get_all_params(cnn, binary=True)
W_grads = binary_net.compute_grads(loss, cnn)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = binary_net.clipping_scaling(updates, cnn)
# other parameters updates
params = lasagne.layers.get_all_params(cnn,
trainable=True,
binary=False)
updates = OrderedDict(updates.items() + lasagne.updates.adam(
loss_or_grads=loss, params=params, learning_rate=LR).items())
else:
params = lasagne.layers.get_all_params(cnn, trainable=True)
updates = lasagne.updates.adam(loss_or_grads=loss,
cnn = lasagne.layers.BatchNormLayer(
cnn,
epsilon=epsilon,
alpha=alpha)
train_output = lasagne.layers.get_output(cnn, deterministic=False)
# squared hinge loss
loss = T.mean(T.sqr(T.maximum(0.,1.-target*train_output)))
if binary:
# W updates
W = lasagne.layers.get_all_params(cnn, binary=True)
W_grads = binary_net.compute_grads(loss,cnn)
updates = lasagne.updates.adam(loss_or_grads=W_grads, params=W, learning_rate=LR)
updates = binary_net.clipping_scaling(updates,cnn)
# other parameters updates
params = lasagne.layers.get_all_params(cnn, trainable=True, binary=False)
updates = OrderedDict(updates.items() + lasagne.updates.adam(loss_or_grads=loss, params=params, learning_rate=LR).items())
else:
params = lasagne.layers.get_all_params(cnn, trainable=True)
updates = lasagne.updates.adam(loss_or_grads=loss, params=params, learning_rate=LR)
test_output = lasagne.layers.get_output(cnn, deterministic=True)
test_loss = T.mean(T.sqr(T.maximum(0.,1.-target*test_output)))
test_err = T.mean(T.neq(T.argmax(test_output, axis=1), T.argmax(target, axis=1)),dtype=theano.config.floatX)
def main():
# BN parameters
# alpha is the exponential moving average factor
alpha = .1
print("alpha = " + str(alpha))
epsilon = 1e-4
print("epsilon = " + str(epsilon))
# BinaryOut
activation = binary_net.binary_tanh_unit
print("activation = binary_net.binary_tanh_unit")
# activation = binary_net.binary_sigmoid_unit
# print("activation = binary_net.binary_sigmoid_unit")
# BinaryConnect
binary = True
print("binary = " + str(binary))
stochastic = False
print("stochastic = " + str(stochastic))
# (-H,+H) are the two binary values
# H = "Glorot"
H = 1.
print("H = " + str(H))
W_LR_scale = 1.
#W_LR_scale = "Glorot" # "Glorot" means we are using the coefficients from Glorot's paper
print("W_LR_scale = " + str(W_LR_scale))
# Training parameters
num_epochs = 50
print("num_epochs = " + str(num_epochs))
# Decaying LR
LR_start = 0.01
print("LR_start = " + str(LR_start))
LR_fin = 0.000003
# LR_start = 0.01
# print("LR_start = " + str(LR_start))
# LR_fin = 1e-6
print("LR_fin = " + str(LR_fin))
LR_decay = (LR_fin / LR_start)**(1. / num_epochs)
print("LR_decay = " + str(LR_decay))
# BTW, LR decay might good for the BN moving average...
shuffle_parts = 1
print("shuffle_parts = " + str(shuffle_parts))
networkType = 'google'
dataType = 'TCDTIMIT'
#networkType='cifar10'
#dataType='cifar10'
# these batch sizes work for a GTX 1060 (6GB)
if dataType == 'TCDTIMIT':
if networkType == 'google': batch_size = 100
else: batch_size = 24
elif dataType == 'cifar10' and networkType == 'cifar10': batch_size = 400
elif dataType == 'cifar10' and networkType == 'google': batch_size = 1000
model_name = os.path.expanduser('~/TCDTIMIT/lipreading/TCDTIMIT/results/CNN_binaryNet/lipspeakers_') \
+ networkType + "_phoneme39_binary" + "_" + dataType
model_path = model_name + ".npz"
if not os.path.exists(os.path.dirname(model_path)):
os.makedirs(os.path.dirname(model_path))
print('Building the CNN...')
# Prepare Theano variables for inputs and targets
input = T.tensor4('inputs')
target = T.matrix('targets')
LR = T.scalar('LR', dtype=theano.config.floatX)
cnn = buildCNN(networkType, dataType, input, epsilon, alpha, activation,
binary, stochastic, H, W_LR_scale)
# restore network weights
if os.path.exists(model_path):
with np.load(model_path) as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
try:
lasagne.layers.set_all_param_values(cnn, *param_values)
except:
lasagne.layers.set_all_param_values(cnn, param_values)
print("\n\n\t Loaded model " + model_path)
print('Loading ' + dataType + ' dataset...')
X_train, y_train, X_val, y_val, X_test, y_test = loadDataset(dataType)
print("Building Functions...")
train_output = lasagne.layers.get_output(cnn, deterministic=False)
# squared hinge loss
loss = T.mean(T.sqr(T.maximum(0., 1. - target * train_output)))
# W updates
W = lasagne.layers.get_all_params(cnn, binary=True)
W_grads = binary_net.compute_grads(loss, cnn)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = binary_net.clipping_scaling(updates, cnn)
# other parameters updates
params = lasagne.layers.get_all_params(cnn, trainable=True, binary=False)
updates = OrderedDict(updates.items() + lasagne.updates.adam(
loss_or_grads=loss, params=params, learning_rate=LR).items())
test_output = lasagne.layers.get_output(cnn, deterministic=True)
test_loss = T.mean(T.sqr(T.maximum(0., 1. - target * test_output)))
test_err = T.mean(T.neq(T.argmax(test_output, axis=1),
T.argmax(target, axis=1)),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training loss:
train_fn = theano.function([input, target, LR], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input, target], [test_loss, test_err])
print('Training...')
binary_net.train(train_fn,
val_fn,
cnn,
batch_size,
LR_start,
LR_decay,
num_epochs,
X_train,
y_train,
X_val,
y_val,
X_test,
y_test,
save_name=model_name,
shuffle_parts=shuffle_parts,
justTest=justTest)
def run(binary=False, noise=None, nalpha=0, result_path=None):
# BN parameters
batch_size = 128
print("batch_size = " + str(batch_size))
# alpha is the exponential moving average factor
alpha = .1
print("alpha = " + str(alpha))
epsilon = 1e-4
print("epsilon = " + str(epsilon))
# Training parameters
num_epochs = 150
print("num_epochs = " + str(num_epochs))
# Dropout parameters
dropout_in = .2 # default: .2
print("dropout_in = " + str(dropout_in))
dropout_hidden = .5 # default: .5
print("dropout_hidden = " + str(dropout_hidden))
# BinaryOut
if binary:
activation = binary_net.binary_tanh_unit
print("activation = binary_net.binary_tanh_unit")
else:
activation = lasagne.nonlinearities.tanh
print("activation = lasagne.nonlinearities.tanh")
# BinaryConnect
print("binary = " + str(binary))
stochastic = False
print("stochastic = " + str(stochastic))
# (-H,+H) are the two binary values
# H = "Glorot"
H = 1.
print("H = " + str(H))
# W_LR_scale = 1.
W_LR_scale = "Glorot" # "Glorot" means we are using the coefficients from Glorot's paper
print("W_LR_scale = " + str(W_LR_scale))
# Decaying LR
LR_start = 0.005
print("LR_start = " + str(LR_start))
LR_fin = 0.0000005 # 0.0000003
print("LR_fin = " + str(LR_fin))
LR_decay = (LR_fin / LR_start)**(1. / num_epochs)
print("LR_decay = " + str(LR_decay))
# BTW, LR decay might good for the BN moving average...
train_set_size = 40000
shuffle_parts = 1
print("shuffle_parts = " + str(shuffle_parts))
print("noise = " + str(noise))
print("nalpha = " + str(nalpha))
print('Loading CIFAR-10 dataset...')
cifar = CifarReader("./data/cifar-10-batches-py/")
train_X, train_y = cifar.get_train_data(n_samples=train_set_size,
noise=noise,
alpha=nalpha)
valid_X, valid_y = cifar.get_validation_data()
test_X, test_y = cifar.get_test_data()
print("train_set_size = " + str(train_y.shape[0]))
print("validation_set_size = " + str(valid_y.shape[0]))
print("test_set_size = " + str(test_y.shape[0]))
# Log output
with open(result_path + "params.txt", "a+") as l:
print("batch_size = " + str(batch_size), file=l)
print("alpha = " + str(alpha), file=l)
print("epsilon = " + str(epsilon), file=l)
print("num_epochs = " + str(num_epochs), file=l)
print("dropout_in = " + str(dropout_in), file=l)
print("dropout_hidden = " + str(dropout_hidden), file=l)
if binary:
print("activation = binary_net.binary_tanh_unit", file=l)
else:
print("activation = lasagne.nonlinearities.tanh", file=l)
print("binary = " + str(binary), file=l)
print("stochastic = " + str(stochastic), file=l)
print("H = " + str(H), file=l)
print("W_LR_scale = " + str(W_LR_scale), file=l)
print("LR_start = " + str(LR_start), file=l)
print("LR_fin = " + str(LR_fin), file=l)
print("LR_decay = " + str(LR_decay), file=l)
print("shuffle_parts = " + str(shuffle_parts), file=l)
print("noise = " + str(noise), file=l)
print("nalpha = " + str(nalpha), file=l)
print("train_set_size = " + str(train_y.shape[0]), file=l)
print("validation_set_size = " + str(valid_y.shape[0]), file=l)
print("test_set_size = " + str(test_y.shape[0]), file=l)
# bc01 format
# Inputs in the range [-1,+1]
# print("Inputs in the range [-1,+1]")
train_X = np.reshape(np.subtract(np.multiply(2. / 255., train_X), 1.),
(-1, 3, 32, 32))
valid_X = np.reshape(np.subtract(np.multiply(2. / 255., valid_X), 1.),
(-1, 3, 32, 32))
test_X = np.reshape(np.subtract(np.multiply(2. / 255., test_X), 1.),
(-1, 3, 32, 32))
# flatten targets
train_y = np.hstack(train_y)
valid_y = np.hstack(valid_y)
test_y = np.hstack(test_y)
# Onehot the targets
train_y = np.float32(np.eye(10)[train_y])
valid_y = np.float32(np.eye(10)[valid_y])
test_y = np.float32(np.eye(10)[test_y])
# for hinge loss
train_y = 2 * train_y - 1.
valid_y = 2 * valid_y - 1.
test_y = 2 * test_y - 1.
print('Building the CNN...')
# Prepare Theano variables for inputs and targets
input = T.tensor4('inputs')
target = T.matrix('targets')
LR = T.scalar('LR', dtype=theano.config.floatX)
cnn = lasagne.layers.InputLayer(shape=(None, 3, 32, 32), input_var=input)
cnn = lasagne.layers.DropoutLayer(cnn, p=dropout_in)
# 32C3-64C3-P2
cnn = binary_net.Conv2DLayer(cnn,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
num_filters=32,
filter_size=(3, 3),
pad=1,
nonlinearity=lasagne.nonlinearities.identity)
cnn = lasagne.layers.BatchNormLayer(cnn, epsilon=epsilon, alpha=alpha)
cnn = lasagne.layers.NonlinearityLayer(cnn, nonlinearity=activation)
cnn = binary_net.Conv2DLayer(cnn,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
num_filters=64,
filter_size=(3, 3),
pad=1,
nonlinearity=lasagne.nonlinearities.identity)
cnn = lasagne.layers.MaxPool2DLayer(cnn, pool_size=(2, 2))
cnn = lasagne.layers.BatchNormLayer(cnn, epsilon=epsilon, alpha=alpha)
cnn = lasagne.layers.NonlinearityLayer(cnn, nonlinearity=activation)
cnn = lasagne.layers.DropoutLayer(cnn, p=dropout_hidden)
# 128FP-10FP
cnn = binary_net.DenseLayer(cnn,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=128)
cnn = lasagne.layers.BatchNormLayer(cnn, epsilon=epsilon, alpha=alpha)
cnn = lasagne.layers.NonlinearityLayer(cnn, nonlinearity=activation)
cnn = lasagne.layers.DropoutLayer(cnn, p=dropout_hidden)
cnn = binary_net.DenseLayer(cnn,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=10)
cnn = lasagne.layers.BatchNormLayer(cnn, epsilon=epsilon, alpha=alpha)
cnn = lasagne.layers.NonlinearityLayer(
cnn, nonlinearity=lasagne.nonlinearities.softmax)
train_output = lasagne.layers.get_output(cnn, deterministic=False)
# squared hinge loss
loss = T.mean(T.sqr(T.maximum(0., 1. - target * train_output)))
if binary:
# W updates
W = lasagne.layers.get_all_params(cnn, binary=True)
W_grads = binary_net.compute_grads(loss, cnn)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = binary_net.clipping_scaling(updates, cnn)
# other parameters updates
params = lasagne.layers.get_all_params(cnn,
trainable=True,
binary=False)
updates.update(
lasagne.updates.adam(loss_or_grads=loss,
params=params,
learning_rate=LR))
else:
params = lasagne.layers.get_all_params(cnn, trainable=True)
updates = lasagne.updates.adam(loss_or_grads=loss,
params=params,
learning_rate=LR)
test_output = lasagne.layers.get_output(cnn, deterministic=True)
test_loss = T.mean(T.sqr(T.maximum(0., 1. - target * test_output)))
test_err = T.mean(T.neq(T.argmax(test_output, axis=1),
T.argmax(target, axis=1)),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training loss:
train_fn = theano.function([input, target, LR], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input, target], [test_loss, test_err])
print('Training...')
binary_net.train(train_fn,
val_fn,
cnn,
batch_size,
LR_start,
LR_decay,
num_epochs,
train_X,
train_y,
valid_X,
valid_y,
test_X,
test_y,
shuffle_parts=shuffle_parts,
result_path=result_path)
def main():
# BN parameters
batch_size = 200
print("batch_size = " + str(batch_size))
# alpha is the exponential moving average factor
alpha = .1
print("alpha = " + str(alpha))
epsilon = 1e-4
print("epsilon = " + str(epsilon))
# BinaryOut
activation = binary_net.binary_tanh_unit
print("activation = binary_net.binary_tanh_unit")
# activation = binary_net.binary_sigmoid_unit
# print("activation = binary_net.binary_sigmoid_unit")
# BinaryConnect
binary = True
print("binary = " + str(binary))
stochastic = False
print("stochastic = " + str(stochastic))
# (-H,+H) are the two binary values
# H = "Glorot"
H = 1.
print("H = " + str(H))
# W_LR_scale = 1.
W_LR_scale = "Glorot" # "Glorot" means we are using the coefficients from Glorot's paper
print("W_LR_scale = " + str(W_LR_scale))
# Training parameters
num_epochs = 500
print("num_epochs = " + str(num_epochs))
# Decaying LR
LR_start = 0.01
print("LR_start = " + str(LR_start))
LR_fin = 0.0000003
print("LR_fin = " + str(LR_fin))
LR_decay = (LR_fin / LR_start)**(1. / num_epochs)
print("LR_decay = " + str(LR_decay))
# BTW, LR decay might good for the BN moving average...
train_set_size = 45000
print("train_set_size = " + str(train_set_size))
shuffle_parts = 1
print("shuffle_parts = " + str(shuffle_parts))
print('\nLoading CIFAR-10 dataset...')
train_set = CIFAR10(which_set="train", start=0, stop=train_set_size)
valid_set = CIFAR10(which_set="train", start=train_set_size, stop=50000)
test_set = CIFAR10(which_set="test")
# bc01 format
# Inputs in the range [-1,+1]
# print("Inputs in the range [-1,+1]")
train_set.X = np.reshape(
np.subtract(np.multiply(2. / 255., train_set.X), 1.), (-1, 3, 32, 32))
valid_set.X = np.reshape(
np.subtract(np.multiply(2. / 255., valid_set.X), 1.), (-1, 3, 32, 32))
test_set.X = np.reshape(
np.subtract(np.multiply(2. / 255., test_set.X), 1.), (-1, 3, 32, 32))
# flatten targets
train_set.y = np.hstack(train_set.y)
valid_set.y = np.hstack(valid_set.y)
test_set.y = np.hstack(test_set.y)
if oneHot:
# Onehot the targets
train_set.y = np.float32(np.eye(10)[train_set.y])
valid_set.y = np.float32(np.eye(10)[valid_set.y])
test_set.y = np.float32(np.eye(10)[test_set.y])
# for hinge loss
train_set.y = 2 * train_set.y - 1.
valid_set.y = 2 * valid_set.y - 1.
test_set.y = 2 * test_set.y - 1.
else:
train_set.y = np.int32(train_set.y)
valid_set.y = np.int32(valid_set.y)
test_set.y = np.int32(test_set.y)
#import pdb;pdb.set_trace()
print('\nBuilding the CNN...')
# Prepare Theano variables for inputs and targets
input = T.tensor4('inputs')
if oneHot: target = T.matrix('targets')
else: target = T.ivector('targets')
LR = T.scalar('LR', dtype=theano.config.floatX)
cnn = buildCNN(dataType='cifar10',
networkType='cifar10',
oneHot=oneHot,
input=input,
epsilon=epsilon,
alpha=alpha,
activation=activation,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale)
train_output = lasagne.layers.get_output(cnn, deterministic=False)
# squared hinge loss
if oneHot: loss = T.mean(T.sqr(T.maximum(0., 1. - target * train_output)))
else:
loss = LO.categorical_crossentropy(train_output, target)
loss = loss.mean()
# W updates
W = lasagne.layers.get_all_params(cnn, binary=True)
W_grads = binary_net.compute_grads(loss, cnn)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = binary_net.clipping_scaling(updates, cnn)
# other parameters updates
params = lasagne.layers.get_all_params(cnn, trainable=True, binary=False)
updates = OrderedDict(updates.items() + lasagne.updates.adam(
loss_or_grads=loss, params=params, learning_rate=LR).items())
test_output = lasagne.layers.get_output(cnn, deterministic=True)
if oneHot:
test_loss = T.mean(T.sqr(T.maximum(0., 1. - target * test_output)))
test_err = T.mean(T.neq(T.argmax(test_output, axis=1),
T.argmax(target, axis=1)),
dtype=theano.config.floatX)
else:
test_loss = LO.categorical_crossentropy(test_output, target)
test_loss = test_loss.mean()
test_err = T.mean(T.neq(T.argmax(test_output, axis=1),
T.argmax(target)),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training loss:
train_fn = theano.function([input, target, LR], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input, target], [test_loss, test_err])
print('Training...')
binary_net.train(train_fn,
val_fn,
cnn,
batch_size,
LR_start,
LR_decay,
num_epochs,
train_set.X,
train_set.y,
valid_set.X,
valid_set.y,
test_set.X,
test_set.y,
shuffle_parts=shuffle_parts)
def trial(N_HIDDEN_LAYERS, NUM_UNITS, OUTPUT_TYPE, MAIN_LOSS_TYPE, LAMBDA,
FOLD, FINTUNE_SNAPSHOT, FINTUNE_SCALE):
# BN parameters
batch_size = 97
print("batch_size = " + str(batch_size))
# alpha is the exponential moving average factor
# alpha = .15
alpha = .1
print("alpha = " + str(alpha))
epsilon = 1e-4
print("epsilon = " + str(epsilon))
# MLP parameters
#NUM_UNITS = 25
print("NUM_UNITS = " + str(NUM_UNITS))
#N_HIDDEN_LAYERS = 1
print("N_HIDDEN_LAYERS = " + str(N_HIDDEN_LAYERS))
# Training parameters
num_epochs = 1000
print("num_epochs = " + str(num_epochs))
# Dropout parameters
dropout_in = .2 # 0. means no dropout
print("dropout_in = " + str(dropout_in))
dropout_hidden = .5
print("dropout_hidden = " + str(dropout_hidden))
# BinaryOut
activation = binary_net.binary_tanh_unit
print("activation = binary_net.binary_tanh_unit")
# activation = binary_net.binary_sigmoid_unit
# print("activation = binary_net.binary_sigmoid_unit")
# BinaryConnect
binary = True
print("binary = " + str(binary))
stochastic = False
print("stochastic = " + str(stochastic))
# (-H,+H) are the two binary values
# H = "Glorot"
H = 1.
print("H = " + str(H))
# W_LR_scale = 1.
W_LR_scale = "Glorot" # "Glorot" means we are using the coefficients from Glorot's paper
print("W_LR_scale = " + str(W_LR_scale))
# Decaying LR
#LR_start = .003
LR_start = 0.000003
print("LR_start = " + str(LR_start))
#LR_fin = 0.0000003
LR_fin = LR_start
print("LR_fin = " + str(LR_fin))
LR_decay = (LR_fin / LR_start)**(1. / num_epochs)
print("LR_decay = " + str(LR_decay))
# BTW, LR decay might good for the BN moving average...
# replace the dataset
print('Loading SFEW2 dataset...')
[train_x, train_y, val_x, val_y] = SFEW2.load_train_val()
print(train_x.shape)
print(train_y.shape)
print(val_x.shape)
print(val_y.shape)
print('last training minibatch size: ' +
str(train_x.shape[0] - train_x.shape[0] / batch_size * batch_size) +
' / ' + str(batch_size))
print(
'last training minibatch size should not be too small (except 0). try decrease the batch_size, but not add more minibatches.'
)
print('minibatches size: ' + str(batch_size))
print('suggested minibatches size: ' + str(
math.ceil(
float(train_x.shape[0]) /
math.ceil(float(train_x.shape[0]) / 100))))
print('Building the MLP...')
# Prepare Theano variables for inputs and targets
input = T.matrix('inputs')
target = T.matrix('targets')
LR = T.scalar('LR', dtype=theano.config.floatX)
mlp = lasagne.layers.InputLayer(shape=(None, train_x.shape[1]),
input_var=input)
mlp = lasagne.layers.DropoutLayer(mlp, p=dropout_in)
for k in range(N_HIDDEN_LAYERS):
# pretrain-finetune
if (k == 0):
# fixed num_units
mlp = binary_net.DenseLayer(
mlp,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=1500)
# scale down the LR of transfered dense layer
print('scale down the LR of transfered dense layer from',
str(mlp.W_LR_scale))
mlp.W_LR_scale *= np.float32(FINTUNE_SCALE)
print('to', str(mlp.W_LR_scale))
else:
mlp = binary_net.DenseLayer(
mlp,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=NUM_UNITS)
mlp = lasagne.layers.BatchNormLayer(mlp, epsilon=epsilon, alpha=alpha)
mlp = lasagne.layers.NonlinearityLayer(mlp, nonlinearity=activation)
mlp = lasagne.layers.DropoutLayer(mlp, p=dropout_hidden)
# pretrain-finetune
# only restore the first layer group
if (k == 0):
if (FINTUNE_SNAPSHOT != 0):
print('Load ./W-%d.npz' % FINTUNE_SNAPSHOT)
with np.load('./W-%d.npz' % FINTUNE_SNAPSHOT) as f:
param_values = [
f['arr_%d' % i] for i in range(len(f.files))
]
param_values = param_values[0:6]
lasagne.layers.set_all_param_values(mlp, param_values)
mlp = binary_net.DenseLayer(mlp,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=7)
mlp = lasagne.layers.BatchNormLayer(mlp, epsilon=epsilon, alpha=alpha)
# network output BN or SGN
if OUTPUT_TYPE == 'C':
pass #
elif OUTPUT_TYPE == 'D':
mlp = lasagne.layers.NonlinearityLayer(mlp, nonlinearity=activation)
else:
assert (False)
# loss weight nodes
SPARSITY = 0.9
SPARSITY_MAP = (np.float32(train_x == -1)).mean(0)
LOSS_WEIGHT_1 = 1. + input * (2. * SPARSITY - 1)
LOSS_WEIGHT_1 /= 4 * SPARSITY * (1 - SPARSITY
) # fixed 1->-1:5 -1->1:5/9 weights
LOSS_WEIGHT_2 = 1. + input * (2. * SPARSITY_MAP - 1) #
LOSS_WEIGHT_2 /= 4 * SPARSITY_MAP * (
1 - SPARSITY_MAP) # weights considering element's prior probability
# train loss nodes
train_output = lasagne.layers.get_output(mlp, deterministic=False)
if MAIN_LOSS_TYPE == 'SH':
train_loss = T.mean(T.sqr(T.maximum(0., 1. - target * train_output)))
elif MAIN_LOSS_TYPE == 'W1SH':
train_loss = T.mean(
T.sqr(T.maximum(0., (1. - target * train_output))) * LOSS_WEIGHT_1)
elif MAIN_LOSS_TYPE == 'W2SH':
train_loss = T.mean(
T.sqr(T.maximum(0., (1. - target * train_output))) * LOSS_WEIGHT_2)
elif MAIN_LOSS_TYPE == 'H':
train_loss = T.mean(T.maximum(0., 1. - target * train_output))
elif MAIN_LOSS_TYPE == 'W1H':
train_loss = T.mean(
T.maximum(0., (1. - target * train_output)) * LOSS_WEIGHT_1)
elif MAIN_LOSS_TYPE == 'W2H':
train_loss = T.mean(
T.maximum(0., (1. - target * train_output)) * LOSS_WEIGHT_2)
else:
assert (False)
# + sparse penalty
if LAMBDA > 0:
train_pixel_wise_density = T.mean(T.reshape(
(train_output + 1.) / 2.,
[train_output.shape[0], train_output.shape[1] / 10, 10]),
axis=2)
train_penalty = LAMBDA * T.mean(
T.sqr(train_pixel_wise_density - (1. - SPARSITY)))
else:
train_penalty = T.constant(0.)
train_loss = train_loss + train_penalty
# acc
train_acc = T.mean(T.eq(T.argmax(train_output, axis=1),
T.argmax(target, axis=1)),
dtype=theano.config.floatX)
# grad nodes
if binary:
# W updates
W = lasagne.layers.get_all_params(mlp, binary=True)
W_grads = binary_net.compute_grads(train_loss, mlp)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = binary_net.clipping_scaling(updates, mlp)
# other parameters updates
params = lasagne.layers.get_all_params(mlp,
trainable=True,
binary=False)
updates = OrderedDict(updates.items() + lasagne.updates.adam(
loss_or_grads=train_loss, params=params, learning_rate=LR).items())
else:
params = lasagne.layers.get_all_params(mlp, trainable=True)
updates = lasagne.updates.adam(loss_or_grads=train_loss,
params=params,
learning_rate=LR)
# val loss nodes
# must be created after grad nodes
val_output = lasagne.layers.get_output(mlp, deterministic=True)
if MAIN_LOSS_TYPE == 'SH':
val_loss = T.mean(T.sqr(T.maximum(0., 1. - target * val_output)))
elif MAIN_LOSS_TYPE == 'W1SH':
val_loss = T.mean(
T.sqr(T.maximum(0., (1. - target * val_output))) * LOSS_WEIGHT_1)
elif MAIN_LOSS_TYPE == 'W2SH':
val_loss = T.mean(
T.sqr(T.maximum(0., (1. - target * val_output))) * LOSS_WEIGHT_2)
elif MAIN_LOSS_TYPE == 'H':
val_loss = T.mean(T.maximum(0., 1. - target * val_output))
elif MAIN_LOSS_TYPE == 'W1H':
val_loss = T.mean(
T.maximum(0., (1. - target * val_output)) * LOSS_WEIGHT_1)
elif MAIN_LOSS_TYPE == 'W2H':
val_loss = T.mean(
T.maximum(0., (1. - target * val_output)) * LOSS_WEIGHT_2)
# + sparse penalty
if LAMBDA > 0:
val_pixel_wise_density = T.mean(T.reshape(
(val_output + 1.) / 2.,
[val_output.shape[0], val_output.shape[1] / 10, 10]),
axis=2)
val_penalty = LAMBDA * T.mean(
T.sqr(val_pixel_wise_density - (1. - SPARSITY)))
else:
val_penalty = T.constant(0.)
val_loss = val_loss + val_penalty
# acc
val_acc = T.mean(T.eq(T.argmax(val_output, axis=1), T.argmax(target,
axis=1)),
dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training train_loss:
train_fn = theano.function(
[input, target, LR],
[train_loss, train_penalty, train_acc, train_output],
updates=updates)
# Compile a second function computing the validation train_loss and accuracy:
val_fn = theano.function([input, target],
[val_loss, val_penalty, val_acc, val_output])
print('Training...')
train_x = binary_net.MoveParameter(train_x)
binary_net.train(train_fn, val_fn, batch_size, LR_start, LR_decay,
num_epochs, train_x, train_y, val_x, val_y)
def main():
# BN parameters
batch_size = 100
logger_lip.info("batch_size = %s", batch_size)
# alpha is the exponential moving average factor
alpha = .1
logger_lip.info("alpha = %s", alpha)
epsilon = 1e-4
logger_lip.info("epsilon = %s", epsilon)
# BinaryOut
activation = binary_net.binary_tanh_unit
print("activation = binary_tanh_unit")
stochastic = True
print("stochastic = " + str(stochastic))
# (-H,+H) are the two binary values
#H = "Glorot"
H = 1.
print("H = " + str(H))
# W_LR_scale = 1.
W_LR_scale = "Glorot" # "Glorot" means we are using the coefficients from Glorot's paper
print("W_LR_scale = " + str(W_LR_scale))
# Training parameters
num_epochs = 50
logger_lip.info("num_epochs = %s", num_epochs)
# Decaying LR
LR_start = 0.1
logger_lip.info("LR_start = %s", LR_start)
LR_fin = 0.0000003
logger_lip.info("LR_fin = %s", LR_fin)
# LR_decay = (LR_fin / LR_start) ** (1. / num_epochs)
LR_decay = 0.5 # sqrt(0.5)
logger_lip.info("LR_decay = %s", LR_decay)
# BTW, LR decay might good for the BN moving average...
shuffle_parts = 1
logger_lip.info("shuffle_parts = %s", shuffle_parts)
if binary: oneHot = True
else: oneHot = False
##############################################
network_type = "google"
viseme = False # will set nbClasses and store path vis: 6.498.828 phn: 7.176.231
if viseme:
nbClasses = 12
else:
nbClasses = 39
# get the database
# If it's small (lipspeakers) -> generate X_train, y_train etc here
# otherwise we need to load and generate each speaker seperately in the training loop
dataset = "TCDTIMIT"
root_dir = os.path.join(
os.path.expanduser('~/TCDTIMIT/lipreading/' + dataset))
results_dir = root_dir + "/results/CNN_binaryNet"
if not os.path.exists(results_dir): os.makedirs(results_dir)
if viseme:
database_binaryDir = root_dir + '/binaryViseme'
else:
database_binaryDir = root_dir + '/binary'
datasetType = "lipspeakers" # "lipspeakers" #"volunteers" #"volunteers" # lipspeakers or volunteers"
##############################################
if datasetType == "lipspeakers":
loadPerSpeaker = False # only lipspeakers small enough to fit in CPU RAM, generate X_train etc here
storeProcessed = True
processedDir = database_binaryDir + "_allLipspeakersProcessed"
# TODO: prepLip_all can be used to generate pkl containing all the lipspeaker data. Not sure if this stil works, so use with care!
if not oneHot: pkl_path = processedDir + os.sep + datasetType + ".pkl"
else:
pkl_path = processedDir + os.sep + datasetType + "_oneHot" + ".pkl"
if not os.path.exists(pkl_path):
logger_lip.info("dataset not yet processed. Processing...")
code.lipreading.preprocessLipreading.prepLip_all(
data_path=database_binaryDir,
store_path=pkl_path,
trainFraction=0.7,
validFraction=0.1,
testFraction=0.2,
nbClasses=nbClasses,
onehot=oneHot,
type=datasetType,
verbose=True)
datasetFiles = code.lipreading.general_tools.unpickle(pkl_path)
X_train, y_train, X_val, y_val, X_test, y_test = datasetFiles
dtypeX = 'float32'
dtypeY = 'float32'
X_train = X_train.astype(dtypeX)
y_train = y_train.astype(dtypeY)
X_val = X_val.astype(dtypeX)
y_val = y_val.astype(dtypeY)
X_test = X_test.astype(dtypeX)
y_test = y_test.astype(dtypeY)
datasetFiles = [X_train, y_train, X_val, y_val, X_test, y_test]
# These files have been generated with datasetToPkl_fromCombined, so that the train/val/test set are the same as for combinedSR.
# X_train, y_train = unpickle(os.path.expanduser("~/TCDTIMIT/lipreading/TCDTIMIT/binary/allLipspeakersTrain.pkl"))
# X_val, y_val = unpickle(os.path.expanduser("~/TCDTIMIT/lipreading/TCDTIMIT/binary/allLipspeakersVal.pkl"))
# X_test, y_test = unpickle(os.path.expanduser("~/TCDTIMIT/lipreading/TCDTIMIT/binary/allLipspeakersTest.pkl"))
# datasetFiles = [X_train, y_train, X_val, y_val, X_test, y_test]
else: # we need to load and preprocess each speaker before we evaluate, because dataset is too large and doesn't fit in CPU RAM
loadPerSpeaker = True
storeProcessed = True # if you have about 10GB hdd space, you can increase the speed by not reprocessing it each iteration
processedDir = database_binaryDir + "_finalProcessed"
# you can just run this program and it will generate the files the first time it encounters them, or generate them manually with datasetToPkl.py
# just get the names
testVolunteerNumbers = [
"13F", "15F", "21M", "23M", "24M", "25M", "28M", "29M", "30F",
"31F", "34M", "36F", "37F", "43F", "47M", "51F", "54M"
]
testVolunteers = [
str(testNumber) + ".pkl" for testNumber in testVolunteerNumbers
]
lipspeakers = ["Lipspkr1.pkl", "Lipspkr2.pkl", "Lipspkr3.pkl"]
allSpeakers = [
f for f in os.listdir(database_binaryDir)
if os.path.isfile(os.path.join(database_binaryDir, f))
and os.path.splitext(f)[1] == ".pkl"
]
trainVolunteers = [
f for f in allSpeakers
if not (f in testVolunteers or f in lipspeakers)
]
trainVolunteers = [vol for vol in trainVolunteers if vol is not None]
if datasetType == "combined":
trainingSpeakerFiles = trainVolunteers + lipspeakers
testSpeakerFiles = testVolunteers
elif datasetType == "volunteers":
trainingSpeakerFiles = trainVolunteers
testSpeakerFiles = testVolunteers
else:
raise Exception("invalid dataset entered")
datasetFiles = [trainingSpeakerFiles, testSpeakerFiles]
model_name = datasetType + "_" + network_type + "_" + ("viseme" if viseme else "phoneme") + str(nbClasses) \
+ ("_binary" if binary else "")
model_save_name = os.path.join(results_dir, model_name)
# log file
logFile = results_dir + os.sep + model_name + '.log'
# if os.path.exists(logFile):
# fh = logging.FileHandler(logFileT) # append to existing log
# else:
fh = logging.FileHandler(logFile, 'w') # create new logFile
fh.setLevel(logging.DEBUG)
fh.setFormatter(formatter)
logger_lip.addHandler(fh)
logger_lip.info('Building the CNN...')
# Prepare Theano variables for inputs and targets
inputs = T.tensor4('inputs')
if oneHot:
targets = T.matrix('targets')
else:
targets = T.ivector('targets')
LR = T.scalar('LR', dtype=theano.config.floatX)
# get the network structure
l_out = code.lipreading.buildNetworks.build_network_google_binary(
activation, alpha, epsilon, inputs, binary, stochastic, H,
W_LR_scale) # 7176231 params
for layer in L.get_all_layers(l_out):
print(layer)
# print het amount of network parameters
logger_lip.info("Using the %s network", network_type)
logger_lip.info("The number of parameters of this network: %s",
L.count_params(l_out))
logger_lip.info("loading %s", model_save_name + '.npz')
load_model(model_save_name + '.npz', l_out)
logger_lip.info("* COMPILING FUNCTIONS...")
train_output = lasagne.layers.get_output(l_out, deterministic=False)
# squared hinge loss
loss = T.mean(T.sqr(T.maximum(0., 1. - targets * train_output)))
# W updates
W = lasagne.layers.get_all_params(l_out, binary=True)
W_grads = binary_net.compute_grads(loss, l_out)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = binary_net.clipping_scaling(updates, l_out)
# other parameters updates
params = lasagne.layers.get_all_params(l_out, trainable=True, binary=False)
updates = OrderedDict(updates.items() + lasagne.updates.adam(
loss_or_grads=loss, params=params, learning_rate=LR).items())
test_output = lasagne.layers.get_output(l_out, deterministic=True)
out_fn = theano.function([inputs], test_output)
test_loss = T.mean(T.sqr(T.maximum(0., 1. - targets * test_output)))
test_acc = T.mean(T.eq(T.argmax(test_output, axis=1),
T.argmax(targets, axis=1)),
dtype=theano.config.floatX)
k = 3
test_top3_acc = T.zeros((1, ))
topk_acc_fn = theano.function([], test_top3_acc)
val_fn = theano.function([inputs, targets],
[test_loss, test_acc, test_top3_acc])
if debug:
nb = 3
debugX = X_train[0:nb]
debugY = y_train[0:nb]
out = out_fn(debugX)
val = val_fn(debugX, debugY)
import pdb
pdb.set_trace()
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training loss:
train_fn = theano.function([inputs, targets, LR], loss, updates=updates)
logger_lip.info('Training...')
import code.lipreading.train_lipreading
code.lipreading.train_lipreading.train(
train_fn=train_fn,
val_fn=val_fn,
out_fn=out_fn,
topk_acc_fn=topk_acc_fn,
k=k,
network_output_layer=l_out,
batch_size=batch_size,
LR_start=LR_start,
LR_decay=LR_decay,
num_epochs=num_epochs,
dataset=datasetFiles,
database_binaryDir=database_binaryDir,
storeProcessed=storeProcessed,
processedDir=processedDir,
loadPerSpeaker=loadPerSpeaker,
justTest=justTest,
save_name=model_save_name,
shuffleEnabled=True)
def trial(N_HIDDEN_LAYERS, NUM_UNITS, OUTPUT_TYPE, MAIN_LOSS_TYPE, LAMBDA,
FOLD):
# BN parameters
batch_size = 100
print("batch_size = " + str(batch_size))
# alpha is the exponential moving average factor
# alpha = .15
alpha = .1
print("alpha = " + str(alpha))
epsilon = 1e-4
print("epsilon = " + str(epsilon))
# MLP parameters
#NUM_UNITS = 25
print("NUM_UNITS = " + str(NUM_UNITS))
#N_HIDDEN_LAYERS = 1
print("N_HIDDEN_LAYERS = " + str(N_HIDDEN_LAYERS))
# Training parameters
num_epochs = 1000000
print("num_epochs = " + str(num_epochs))
# Dropout parameters
dropout_in = .2 # 0. means no dropout
print("dropout_in = " + str(dropout_in))
dropout_hidden = .5
print("dropout_hidden = " + str(dropout_hidden))
# BinaryOut
activation = binary_net.binary_tanh_unit
print("activation = binary_net.binary_tanh_unit")
# activation = binary_net.binary_sigmoid_unit
# print("activation = binary_net.binary_sigmoid_unit")
# BinaryConnect
binary = True
print("binary = " + str(binary))
stochastic = False
print("stochastic = " + str(stochastic))
# (-H,+H) are the two binary values
# H = "Glorot"
H = 1.
print("H = " + str(H))
# W_LR_scale = 1.
W_LR_scale = "Glorot" # "Glorot" means we are using the coefficients from Glorot's paper
print("W_LR_scale = " + str(W_LR_scale))
# Decaying LR
#LR_start = .003
LR_start = 0.000003
print("LR_start = " + str(LR_start))
#LR_fin = 0.0000003
LR_fin = LR_start
print("LR_fin = " + str(LR_fin))
LR_decay = (LR_fin / LR_start)**(1. / num_epochs)
print("LR_decay = " + str(LR_decay))
# BTW, LR decay might good for the BN moving average...
# replace the dataset
print('Loading SFEW2 dataset...')
[train_x] = SFEW2.load_lfw()
assert (train_x.shape[0] == 26404)
train_x = train_x[0:26400, :]
[val_x, _, _, _] = SFEW2.load_train_val()
print(train_x.shape)
print(val_x.shape)
print('last training minibatch size: ' +
str(train_x.shape[0] - train_x.shape[0] / batch_size * batch_size) +
' / ' + str(batch_size))
print(
'last training minibatch size should not be too small (except 0). try decrease the batch_size, but not add more minibatches.'
)
print('minibatches size: ' + str(batch_size))
print('suggested minibatches size: ' + str(
math.ceil(
float(train_x.shape[0]) /
math.ceil(float(train_x.shape[0]) / 100))))
##############################################################################################
print('Building the MLP...')
# Prepare Theano variables for inputs and targets
input = T.matrix('inputs')
LR = T.scalar('LR', dtype=theano.config.floatX)
mlp = lasagne.layers.InputLayer(shape=(None, train_x.shape[1]),
input_var=input)
mlp = lasagne.layers.DropoutLayer(
mlp, p=0) # train BAE-2: no dropout on input & BAE-1 layer
for k in range(N_HIDDEN_LAYERS):
if (k == 0):
mlp = binary_net.DenseLayer(
mlp,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=NUM_UNITS)
elif (k == 1):
mlp = binary_net.DenseLayer(
mlp,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=NUM_UNITS * 2)
else:
assert (False)
#if(k==0):
# print('scale down the LR of transfered dense layer from', str(mlp.W_LR_scale))
# mlp.W_LR_scale = 0
# print('to', str(mlp.W_LR_scale))
if (k == 0):
# BAE1 encoder: BN
mlp = lasagne.layers.BatchNormLayer(mlp,
epsilon=epsilon,
alpha=alpha)
elif (k == 1):
# BAE2 encoder: do not use BN for encouraging sparsity
pass
else:
# further layer use BN
mlp = lasagne.layers.BatchNormLayer(mlp,
epsilon=epsilon,
alpha=alpha)
# midactivation place before hard tanh
# encoder and decoder should not use BatchNorm
# "l1 reg" on midactivation
if (k == 1):
mlp_midactivation = mlp
mlp = lasagne.layers.NonlinearityLayer(mlp, nonlinearity=activation)
if (k == 0):
mlp = lasagne.layers.DropoutLayer(
mlp, p=0) # train BAE-2: no dropout on input & BAE-1 layer
else:
mlp = lasagne.layers.DropoutLayer(mlp, p=dropout_hidden)
# pretrain-finetune
# only restore the first layer group
if (k == 0):
print('Load ./W-1168.npz')
with np.load('./W-1168.npz') as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
param_values = param_values[0:6]
lasagne.layers.set_all_param_values(mlp, param_values)
mlp_groundtruth = mlp
mlp = binary_net.DenseLayer(mlp,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=1500)
mlp = lasagne.layers.BatchNormLayer(mlp, epsilon=epsilon, alpha=alpha)
# network output BN or SGN
if OUTPUT_TYPE == 'C':
pass #
elif OUTPUT_TYPE == 'D':
mlp = lasagne.layers.NonlinearityLayer(mlp, nonlinearity=activation)
else:
assert (False)
'''
# equal transform validation
# 1 set AE transform to I
# 1 modift AE DenseLayer.get_output_for() use W(0 1) instead of Wb(+1 -1)
# 2 set encoder's dropout=0
# 3 comment out encoder's and decoder's BatchNormLayer, modify set_all_param_values
# will see train loss = 0
pv = lasagne.layers.get_all_param_values(mlp)
pv[2] = np.identity(1500, np.float64)
pv[4] = np.identity(1500, np.float64)
lasagne.layers.set_all_param_values(mlp, pv)
'''
'''
# loss weight nodes
SPARSITY = 0.9
SPARSITY_MAP = (np.float32(train_x==-1)).mean(0)
LOSS_WEIGHT_1 = 1.+input*(2.*SPARSITY-1)
LOSS_WEIGHT_1 /= 4*SPARSITY*(1 - SPARSITY)# fixed 1->-1:5 -1->1:5/9 weights
LOSS_WEIGHT_2 = 1.+input*(2.*SPARSITY_MAP-1)#
LOSS_WEIGHT_2 /= 4*SPARSITY_MAP*(1 - SPARSITY_MAP)# weights considering element's prior probability
'''
# train loss nodes
'''
train_output = lasagne.layers.get_output(mlp, deterministic=False)
if MAIN_LOSS_TYPE=='SH':
train_loss = T.mean(T.sqr(T.maximum(0.,1.-input*train_output)))
elif MAIN_LOSS_TYPE == 'W1SH':
train_loss = T.mean(T.sqr(T.maximum(0., (1. - input * train_output))) * LOSS_WEIGHT_1)
elif MAIN_LOSS_TYPE == 'W2SH':
train_loss = T.mean(T.sqr(T.maximum(0., (1. - input * train_output))) * LOSS_WEIGHT_2)
elif MAIN_LOSS_TYPE == 'H':
train_loss = T.mean(T.maximum(0.,1.-input*train_output))
elif MAIN_LOSS_TYPE == 'W1H':
train_loss = T.mean(T.maximum(0., (1. - input * train_output)) * LOSS_WEIGHT_1)
elif MAIN_LOSS_TYPE == 'W2H':
train_loss = T.mean(T.maximum(0., (1. - input * train_output)) * LOSS_WEIGHT_2)
else:
assert(False)
'''
[
train_output_mlp_groundtruth, train_output_mlp_midactivation,
train_output
] = lasagne.layers.get_output([mlp_groundtruth, mlp_midactivation, mlp],
deterministic=False)
train_loss = T.mean(
T.maximum(0., 1. - train_output_mlp_groundtruth * train_output))
# + sparse penalty
'''
if LAMBDA>0:
train_pixel_wise_density = T.mean(T.reshape((train_output+1.)/2., [train_output.shape[0], train_output.shape[1]/10, 10]), axis=2)
train_penalty = LAMBDA*T.mean(T.sqr(train_pixel_wise_density - (1.-SPARSITY)))
else:
train_penalty = T.constant(0.)
train_loss = train_loss + train_penalty
'''
if LAMBDA > 0:
train_penalty = LAMBDA * T.mean(
T.maximum(0., 1. + train_output_mlp_midactivation))
else:
train_penalty = T.constant(0.)
train_loss = train_loss + train_penalty
# grad nodes
if binary:
# W updates
W = lasagne.layers.get_all_params(mlp, binary=True)
W_grads = binary_net.compute_grads(train_loss, mlp)
# untrainable W1
assert (len(W) == 3)
assert (len(W_grads) == 3)
W = W[1:len(W)]
W_grads = W_grads[1:len(W_grads)]
assert (len(W) == 2)
assert (len(W_grads) == 2)
updates = lasagne.updates.adam(loss_or_grads=W_grads,
params=W,
learning_rate=LR)
updates = binary_net.clipping_scaling(updates, mlp)
# other parameters updates
params = lasagne.layers.get_all_params(mlp,
trainable=True,
binary=False)
# untrainable b1 bn1
assert (len(params) == 7)
assert (params[0].name == 'b') # fix
assert (params[1].name == 'beta') # fix
assert (params[2].name == 'gamma') # fix
assert (params[3].name == 'b')
assert (params[4].name == 'b')
assert (params[5].name == 'beta')
assert (params[6].name == 'gamma')
params = params[3:len(params)]
assert (len(params) == 4)
updates = OrderedDict(updates.items() + lasagne.updates.adam(
loss_or_grads=train_loss, params=params, learning_rate=LR).items())
else:
params = lasagne.layers.get_all_params(mlp, trainable=True)
updates = lasagne.updates.adam(loss_or_grads=train_loss,
params=params,
learning_rate=LR)
##############################################################################################
# val loss nodes
# must be created after grad nodes
'''
val_output = lasagne.layers.get_output(mlp, deterministic=True)
if MAIN_LOSS_TYPE=='SH':
val_loss = T.mean(T.sqr(T.maximum(0.,1.-input*val_output)))
elif MAIN_LOSS_TYPE == 'W1SH':
val_loss = T.mean(T.sqr(T.maximum(0., (1. - input * val_output))) * LOSS_WEIGHT_1)
elif MAIN_LOSS_TYPE == 'W2SH':
val_loss = T.mean(T.sqr(T.maximum(0., (1. - input * val_output))) * LOSS_WEIGHT_2)
elif MAIN_LOSS_TYPE == 'H':
val_loss = T.mean(T.maximum(0.,1.-input*val_output))
elif MAIN_LOSS_TYPE == 'W1H':
val_loss = T.mean(T.maximum(0., (1. - input * val_output)) * LOSS_WEIGHT_1)
elif MAIN_LOSS_TYPE == 'W2H':
val_loss = T.mean(T.maximum(0., (1. - input * val_output)) * LOSS_WEIGHT_2)
'''
[val_output_mlp_groundtruth, val_output_mlp_midactivation, val_output
] = lasagne.layers.get_output([mlp_groundtruth, mlp_midactivation, mlp],
deterministic=True)
val_loss = T.mean(
T.maximum(0., 1. - val_output_mlp_groundtruth * val_output))
# + sparse penalty
'''
if LAMBDA>0:
val_pixel_wise_density = T.mean(T.reshape((val_output + 1.) / 2., [val_output.shape[0], val_output.shape[1] / 10, 10]), axis=2)
val_penalty = LAMBDA*T.mean(T.sqr(val_pixel_wise_density - (1. - SPARSITY)))
else:
val_penalty = T.constant(0.)
val_loss = val_loss + val_penalty
'''
if LAMBDA > 0:
val_penalty = LAMBDA * T.mean(
T.maximum(0., 1. + val_output_mlp_midactivation))
else:
val_penalty = T.constant(0.)
val_loss = val_loss + val_penalty
##############################################################################################
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training train_loss:
train_fn = theano.function([input, LR], [
train_loss, train_penalty, train_output_mlp_groundtruth,
train_output_mlp_midactivation, train_output
],
updates=updates)
##############################################################################################
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input], [
val_loss, val_penalty, val_output_mlp_groundtruth,
val_output_mlp_midactivation, val_output
])
##############################################################################################
print('Training...')
train_x = binary_net.MoveParameter(train_x)
binary_net.train(train_fn, val_fn, batch_size, LR_start, LR_decay,
num_epochs, train_x, val_x, mlp)
print('Save W')
np.savez('./W.npz', *lasagne.layers.get_all_param_values(
mlp)) # W b BN BN BN BN W b BN BN BN BN
def run(binary=False, noise=None, nalpha=0, result_path=None):
# BN parameters
batch_size = 128 # default: 100
print("batch_size = " + str(batch_size))
# alpha is the exponential moving average factor
alpha = .1 # default: .1
print("alpha = " + str(alpha))
epsilon = 1e-4 # default: 1e-4
print("epsilon = " + str(epsilon))
# MLP parameters
num_units = 300 # default: 4096
print("num_units = " + str(num_units))
n_hidden_layers = 1 # default: 3
print("n_hidden_layers = " + str(n_hidden_layers))
# Training parameters
num_epochs = 500 # default: 1000
print("num_epochs = " + str(num_epochs))
# Dropout parameters
dropout_in = .2 # default: .2
print("dropout_in = " + str(dropout_in))
dropout_hidden = .5 # default: .5
print("dropout_hidden = " + str(dropout_hidden))
# BinaryOut
if binary:
activation = binary_net.binary_tanh_unit
print("activation = binary_net.binary_tanh_unit")
else:
activation = lasagne.nonlinearities.tanh
print("activation = lasagne.nonlinearities.tanh")
# BinaryConnect
print("binary = " + str(binary))
stochastic = False # default: False
print("stochastic = " + str(stochastic))
# (-H,+H) are the two binary values
# H = "Glorot"
H = 1. # default: 1.
print("H = " + str(H))
# W_LR_scale = 1.
W_LR_scale = "Glorot" # default: "Glorot"
print("W_LR_scale = " + str(W_LR_scale))
# Decaying LR
LR_start = 0.005 # default: .003
print("LR_start = " + str(LR_start))
LR_fin = 0.0000005 # default: 0.0000003
print("LR_fin = " + str(LR_fin))
LR_decay = (LR_fin / LR_start) ** (1. / num_epochs)
print("LR_decay = " + str(LR_decay))
# BTW, LR decay might good for the BN moving average...
save_path = None # default: "mnist_parameters.npz"
print("save_path = " + str(save_path))
# Load the dataset (https://github.com/mnielsen/neural-networks-and-deep-learning)
print('Loading MNIST dataset...')
mnist = MnistReader("./data/mnist.pkl.gz")
shuffle_parts = 1 # default: 1
print("shuffle_parts = " + str(shuffle_parts))
print("noise = " + str(noise))
print("nalpha = " + str(nalpha))
train_set_size = 50000 # default: 50000
train_X, train_y = mnist.get_train_data(n_samples=train_set_size, noise=noise, alpha=nalpha)
validation_X, validation_y = mnist.get_validation_data()
test_X, test_y = mnist.get_test_data()
print("train_set_size = "+str(train_y.shape[0]))
print("validation_set_size = "+str(validation_y.shape[0]))
print("test_set_size = "+str(test_y.shape[0]))
# Log output
with open(result_path + "params.txt", "a+") as l:
print("batch_size = " + str(batch_size), file=l)
print("alpha = " + str(alpha), file=l)
print("epsilon = " + str(epsilon), file=l)
print("num_units = " + str(num_units), file=l)
print("n_hidden_layers = " + str(n_hidden_layers), file=l)
print("num_epochs = " + str(num_epochs), file=l)
print("dropout_in = " + str(dropout_in), file=l)
print("dropout_hidden = " + str(dropout_hidden), file=l)
if binary:
print("activation = binary_net.binary_tanh_unit", file=l)
else:
print("activation = lasagne.nonlinearities.tanh", file=l)
print("binary = " + str(binary), file=l)
print("stochastic = " + str(stochastic), file=l)
print("H = " + str(H), file=l)
print("W_LR_scale = " + str(W_LR_scale), file=l)
print("LR_start = " + str(LR_start), file=l)
print("LR_fin = " + str(LR_fin), file=l)
print("LR_decay = " + str(LR_decay), file=l)
print("save_path = " + str(save_path), file=l)
print("shuffle_parts = " + str(shuffle_parts), file=l)
print("noise = " + str(noise), file=l)
print("nalpha = " + str(nalpha), file=l)
print("train_set_size = "+str(train_y.shape[0]), file=l)
print("validation_set_size = "+str(validation_y.shape[0]), file=l)
print("test_set_size = "+str(test_y.shape[0]), file=l)
# bc01 format
# Inputs in the range [-1,+1]
# print("Inputs in the range [-1,+1]")
train_X = 2 * train_X.reshape(-1, 1, 28, 28) - 1.
validation_X = 2 * validation_X.reshape(-1, 1, 28, 28) - 1.
test_X = 2 * test_X.reshape(-1, 1, 28, 28) - 1.
# flatten targets
train_y = np.hstack(train_y)
validation_y = np.hstack(validation_y)
test_y = np.hstack(test_y)
# Onehot the targets
train_y = np.float32(np.eye(10)[train_y])
validation_y = np.float32(np.eye(10)[validation_y])
test_y = np.float32(np.eye(10)[test_y])
# for hinge loss
train_y = 2 * train_y - 1.
validation_y = 2 * validation_y - 1.
test_y = 2 * test_y - 1.
print('Building the MLP...')
# Prepare Theano variables for inputs and targets
input = T.tensor4('inputs')
target = T.matrix('targets')
LR = T.scalar('LR', dtype=theano.config.floatX)
mlp = lasagne.layers.InputLayer(
shape=(None, 1, 28, 28),
input_var=input)
mlp = lasagne.layers.DropoutLayer(
mlp,
p=dropout_in)
for k in range(n_hidden_layers):
mlp = binary_net.DenseLayer(
mlp,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=num_units)
mlp = lasagne.layers.BatchNormLayer(
mlp,
epsilon=epsilon,
alpha=alpha)
mlp = lasagne.layers.NonlinearityLayer(
mlp,
nonlinearity=activation)
mlp = lasagne.layers.DropoutLayer(
mlp,
p=dropout_hidden)
mlp = binary_net.DenseLayer(
mlp,
binary=binary,
stochastic=stochastic,
H=H,
W_LR_scale=W_LR_scale,
nonlinearity=lasagne.nonlinearities.identity,
num_units=10)
mlp = lasagne.layers.BatchNormLayer(
mlp,
epsilon=epsilon,
alpha=alpha)
train_output = lasagne.layers.get_output(mlp, deterministic=False)
# squared hinge loss
loss = T.mean(T.sqr(T.maximum(0., 1. - target * train_output)))
if binary:
# W updates
W = lasagne.layers.get_all_params(mlp, binary=True)
W_grads = binary_net.compute_grads(loss, mlp)
updates = lasagne.updates.adam(loss_or_grads=W_grads, params=W, learning_rate=LR)
updates = binary_net.clipping_scaling(updates, mlp)
# other parameters updates
params = lasagne.layers.get_all_params(mlp, trainable=True, binary=False)
updates.update(lasagne.updates.adam(loss_or_grads=loss, params=params, learning_rate=LR))
else:
params = lasagne.layers.get_all_params(mlp, trainable=True)
updates = lasagne.updates.adam(loss_or_grads=loss, params=params, learning_rate=LR)
test_output = lasagne.layers.get_output(mlp, deterministic=True)
test_loss = T.mean(T.sqr(T.maximum(0., 1. - target * test_output)))
test_err = T.mean(T.neq(T.argmax(test_output, axis=1), T.argmax(target, axis=1)), dtype=theano.config.floatX)
# Compile a function performing a training step on a mini-batch (by giving the updates dictionary)
# and returning the corresponding training loss:
train_fn = theano.function([input, target, LR], loss, updates=updates)
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input, target], [test_loss, test_err])
print('Training...')
binary_net.train(
train_fn, val_fn,
mlp,
batch_size,
LR_start, LR_decay,
num_epochs,
train_X, train_y,
validation_X, validation_y,
test_X, test_y,
save_path,
shuffle_parts,
result_path)
loss = T.mean(T.sqr(T.maximum(0.0, 1.0 - target * train_output))) #XXX
#
# update expression の構築
#
if binary:
# W updates
# binary なパラメータだけを取り出す
Wb_list = lasagne.layers.get_all_params(mlp, binary=True)
for eW in Wb_list:
print('eW:', type(eW), eW)
# binary なパラメータのみに対する勾配を求めてリストアップ
W_grad_list = binary_net.compute_grads(loss, mlp)
print('W_grad_list', type(W_grad_list), W_grad_list)
# ADAM学習則による更新式マップ(OrderedDict)
updates_b0 = lasagne.updates.adam(loss_or_grads=W_grad_list,
params=Wb_list,
learning_rate=LR)
# バイナリ化のためのクリッピング＆スケーリング
updates_b1 = binary_net.clipping_scaling(updates_b0, mlp)
# other parameters updates
# 非バイナリパラメータの更新則
Wr_list = lasagne.layers.get_all_params(mlp,
trainable=True,
binary=False)