def main(paramFile="",num_epochs=10):
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
network = build_rcnn(input_var)
#jin
if paramFile=="":
print("Train a new network!")
else:
print("Load well trained parameters from "+paramFile)
f = file(paramFile,'rb')
params = cPickle.load(f)
f.close()
lasagne.layers.set_all_param_values(network,params)
# Create a loss expression for validation/testing. The crucial difference
# here is that we do a deterministic forward pass through the network,
# disabling dropout layers.
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_fer = T.mean(T.neq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Compile a second function computing the validation loss and accuracy:
frames = T.argmax(test_prediction, axis=1)
val_fn = theano.function([input_var, target_var], [test_loss, test_fer, frames])
# Finally, launch the training loop.
print("Starting Testing Phoneme Error Rate...")
# We iterate over epochs:
#jin
# TEST Phoneme Error Rate
dirpath = os.getcwd()
print('dirpath = '+dirpath)
test_dirpath = dirpath + '/test'
# change current directory
os.chdir(test_dirpath)
# After training, we compute and print the test error:
test_ferr = 0
test_loss = 0
total_phn_num = 0
test_batches = 0
test_per = 0
for batch in loadArray(test_dirpath):
inputs, targets, batchNum = batch
loss, ferr, out_frame = val_fn(inputs, targets)
test_ferr += ferr*batchNum
test_loss += loss*batchNum
test_batches += batchNum
out_phn = PER.phn2targetseq(out_frame,39)
label_phn = PER.phn2targetseq(targets,39)
phn_num = len(label_phn)
perr = PER.per(out_phn,label_phn)
total_phn_num += phn_num
test_per += perr
print("frame length = "+str(batchNum))
print("output phoneme = "+str(out_phn))
print("label phoneme = "+str(label_phn))
print("label phoneme length = "+str(phn_num))
print(" phoneme err num = "+str(perr))
print(" FER = "+str(ferr))
print(" PER = "+str(float(perr)/phn_num))
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(test_loss / test_batches))
print(" test Frame Error Rate:\t\t{:.2f} %".format(test_ferr / test_batches * 100))
print( "test_per = "+str(test_per))
print( "total_phn_num = "+str(total_phn_num))
test_per = float(test_per)/total_phn_num
print(" test Phoneme Error Rate:\t\t{:.2f} %".format(test_per * 100))
def main(paramFile="",num_epochs=5):
# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
#y = T.matrix()
label = T.vector()
blank_symbol = T.scalar()
# Create neural network model (depending on first command line parameter)
print("Building model and compiling functions...")
network = build_rcnn(input_var)
#jin
if paramFile=="":
print("Train a new network!")
else:
print("Load well trained parameters from "+paramFile)
f = file(paramFile,'rb')
params = cPickle.load(f)
f.close()
lasagne.layers.set_all_param_values(network,params)
# Create a loss expression for training, i.e., a scalar objective we want
# to minimize the objective function:
y = lasagne.layers.get_output(network)
ctc_cost = CTC.cost(y, label, blank_symbol)
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.nesterov_momentum(
ctc_cost, params, learning_rate=1e-5, momentum=0.9)
train_fn = theano.function([input_var, label, blank_symbol],
ctc_cost, updates=updates,allow_input_downcast=True)
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_var)
# Create a loss expression for validation/testing.
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,
target_var)
test_loss = test_loss.mean()
# As a bonus, also create an expression for the classification accuracy:
test_fer = T.mean(T.neq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
# Compile a second function computing the validation loss and accuracy:
test_frames = T.argmax(test_prediction, axis=1)
val_fn = theano.function([input_var, target_var], [test_loss, test_fer, test_frames])
# Finally, launch the training loop.
print("Starting training...")
# We iterate over epochs:
#jin
# train set and validation set
dirpath = os.getcwd()
print('dirpath = '+dirpath)
train_dirpath = dirpath + '/train'
test_dirpath = dirpath + '/test'
total = len(os.listdir(train_dirpath)) / 2
train_total_num = int(0.9 * total)
validation_total_num = total - train_total_num
print('Train num = ' + str(train_total_num))
print('Validation num = '+str(validation_total_num))
blank_symbol_num = 39
for epoch in range(num_epochs):
# change current directory
os.chdir(train_dirpath)
# In each epoch, we do a full pass over the training data:
start_time = time.time()
counter = 0
# And a full pass over the validation data:
val_ferr = 0
val_loss = 0
total_phn_num = 0
val_batches = 0
val_per = 0
#for batch in loadArray(train_dirpath):
for batch in loadArray(train_dirpath):
inputs, targets, batchNum = batch
print('spectro shape:')
print(inputs.shape)
print('label shape:')
print(targets.shape)
label_without_blank = PER.phn2targetseq(targets,blank_symbol_num)
#label_without_blank = label_without_blank[0,:]
#print('noblanklabel shape = '+str(label_without_blank.shape))
counter += 1
if counter < train_total_num:
ctc_loss = train_fn(inputs, label_without_blank, blank_symbol_num)
print(' Train set completed : '+str(float(counter)/train_total_num*100))
else:
inputs, targets, batchNum = batch
loss, ferr, out_frame = val_fn(inputs, targets)
val_ferr += ferr*batchNum
val_loss += loss*batchNum
val_batches += batchNum
out_phn = PER.phn2targetseq(out_frame,39)
label_phn = PER.phn2targetseq(targets,39)
phn_num = len(label_phn)
perr = PER.per(out_phn,label_phn)
total_phn_num += phn_num
val_per += perr
print("frame length = "+str(batchNum))
print("output phoneme = "+str(out_phn))
print("label phoneme = "+str(label_phn))
print("label phoneme length = "+str(phn_num))
print(" phoneme err num = "+str(perr))
print(" FER = "+str(ferr))
print(" PER = "+str(float(perr)/phn_num))
print(' Validation set completed : '+str(float(counter-train_total_num)/validation_total_num*100))
# Then we print the results for this epoch:
print("Validation results:")
print(" val loss:\t\t\t{:.6f}".format(val_loss / val_batches))
print(" val Frame Error Rate:\t\t{:.2f} %".format(val_ferr / val_batches * 100))
print( "val_per = "+str(val_per))
print( "total_phn_num = "+str(total_phn_num))
val_per = float(val_per)/total_phn_num
print(" val Phoneme Error Rate:\t\t{:.2f} %".format(val_per * 100))
# change current directory
os.chdir(dirpath)
# store parameters
print(" should store epoch {}".format(epoch+1))
pythonName,suffix = os.path.splitext(__file__)
param2store = lasagne.layers.get_all_param_values(network)
storename = pythonName+"_"+str((epoch+1))+"_per="+str(val_per * 100)+".save"
with file(storename,'wb') as f:
cPickle.dump(param2store,f)
# change current directory
os.chdir(test_dirpath)
# After training, we compute and print the test error:
test_ferr = 0
test_loss = 0
total_phn_num = 0
test_batches = 0
test_per = 0
for batch in loadArray(test_dirpath):
inputs, targets, batchNum = batch
loss, ferr, out_frame = val_fn(inputs, targets)
test_ferr += ferr*batchNum
test_loss += loss*batchNum
test_batches += batchNum
out_phn = PER.phn2targetseq(out_frame,39)
label_phn = PER.phn2targetseq(targets,39)
phn_num = len(label_phn)
perr = PER.per(out_phn,label_phn)
total_phn_num += phn_num
test_per += perr
print("frame length = "+str(batchNum))
print("output phoneme = "+str(out_phn))
print("label phoneme = "+str(label_phn))
print("label phoneme length = "+str(phn_num))
print(" phoneme err num = "+str(perr))
print(" FER = "+str(ferr))
print(" PER = "+str(float(perr)/phn_num))
print("Final results:")
print(" test loss:\t\t\t{:.6f}".format(test_loss / test_batches))
print(" test Frame Error Rate:\t\t{:.2f} %".format(test_ferr / test_batches * 100))
print( "test_per = "+str(test_per))
print( "total_phn_num = "+str(total_phn_num))
test_per = float(test_per)/total_phn_num
print(" test Phoneme Error Rate:\t\t{:.2f} %".format(test_per * 100))
