for l in f:
mapping = l.strip(' \n').split('\t') # mapping = 0 ~ 1942, aa ~ z, aa ~ z
phone_map_1943i_48i[i] = phone_map_48s_48i[mapping[1]]
phone_map_1943i_48s[i] = mapping[1]
i += 1
f.close()
###################
# Build Model #
###################
x = T.matrix(dtype=theano.config.floatX)
classifier = MLP(input=x,
n_in=INPUT_DIM,
n_hidden=NEURONS_PER_LAYER,
n_out=OUTPUT_DIM,
n_layers=HIDDEN_LAYERS)
classifier.load_model(args.model_in)
test_model = theano.function(inputs=[x], outputs=(classifier.output))
'''
#####################
# Probing Train #
#####################
f_y = args.train_in + ".unshuffled.y"
with open(f_y, "rb") as f:
y_out = cPickle.load(f)
f_idx = args.train_in + ".idx"
with open(f_idx, "rb") as f:
print >> sys.stderr, "After loading: %f" % (time.time() - start_time)
###############
# Build Model #
###############
# symbolic variables
index = T.lscalar()
x = T.matrix(dtype=theano.config.floatX)
y = T.ivector()
# construct MLP class
classifier = MLP(input=x,
n_in=INPUT_DIM,
n_hidden=NEURONS_PER_LAYER,
n_out=OUTPUT_DIM,
n_layers=HIDDEN_LAYERS)
# cost + regularization terms; cost is symbolic
cost = (classifier.negative_log_likelihood(y) + L1_REG * classifier.L1 +
L2_REG * classifier.L2_sqr)
# compile "dev model" function
dev_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: val_x[index * BATCH_SIZE:(index + 1) * BATCH_SIZE].T,
y: val_y[index * BATCH_SIZE:(index + 1) * BATCH_SIZE].T,
})
mapping = l.strip(' \n').split('\t') # mapping = 0 ~ 1942, aa ~ z, aa ~ z
phone_map_1943i_48i[i] = phone_map_48s_48i[mapping[1]]
phone_map_1943i_48s[i] = mapping[1]
i += 1
f.close()
###################
# Build Model #
###################
x = T.matrix(dtype=theano.config.floatX)
classifier = MLP(
input=x,
n_in=INPUT_DIM,
n_hidden=NEURONS_PER_LAYER,
n_out=OUTPUT_DIM,
n_layers=HIDDEN_LAYERS
)
classifier.load_model(args.model_in)
test_model = theano.function(
inputs=[x],
outputs=(classifier.output)
)
'''
#####################
# Probing Train #
#####################
print >> sys.stderr, "After loading: %f" % (time.time()-start_time)
###############
# Build Model #
###############
# symbolic variables
index = T.lscalar()
x = T.matrix(dtype=theano.config.floatX)
y = T.ivector()
# construct MLP class
classifier = MLP(
input=x,
n_in=INPUT_DIM,
n_hidden=NEURONS_PER_LAYER,
n_out=OUTPUT_DIM,
n_layers=HIDDEN_LAYERS
)
# cost + regularization terms; cost is symbolic
cost = (
classifier.negative_log_likelihood(y) +
L1_REG * classifier.L1 +
L2_REG * classifier.L2_sqr
)
# compile "dev model" function
dev_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
