# Prepare Theano variables for inputs and targets
input_var = T.tensor4('inputs')
# restore network
network = build_mlp(input_var)
network_parameters = np.load(FILE)
lasagne.layers.set_all_param_values(network, network_parameters['arr_0'])
# prepare prediction
test_prediction = lasagne.layers.get_output(network, deterministic=True)
predict_fn = theano.function([input_var], T.argmax(test_prediction, axis=1))
complete_x = list()
complete_y = list()
for position, fft, c in traindata_mix.test_data_iterator(
traindata_mix.RING_02_TEST_DATA):
try:
complete_x.append(predict_fn([[[fft]]]) * 10000)
except:
print position, fft
complete_x.append(0)
complete_x.append(0)
rate, data = read(traindata_mix.RING_02_TEST_DATA + ".wav")
plot(range(0, len(data)), data)
plot(range(0, len(complete_x)), complete_x)
show()
dtype=theano.config.floatX)
# prepare prediction
test_prediction = lasagne.layers.get_output(network, deterministic=True)
predict_fn = theano.function([input_var], T.argmax(test_prediction, axis=1))
# Compile a second function computing the validation loss and accuracy:
val_fn = theano.function([input_var, target_var], [test_loss, test_acc], allow_input_downcast=True)
complete_x = list()
complete_y = list()
a = 0
correct = 0
wrong = 0
for position, fft, c in traindata_mix.test_data_iterator(traindata_mix.RING_01_TEST_DATA):
try:
r = predict_fn([[[fft]]])
if c > 0:
a += 1
if r > 0 and c > 0:
correct += 1
if c > 0 >= r:
wrong += 1
complete_x.append(r * 10000)
except:
print position, fft
complete_x.append(0)
complete_x.append(0)
