def __init__(self,
datafile,
desired_error=0.0000000001,
iterations_between_reports=1000):
self.datafile = datafile
self.desired_error = desired_error
self.iterations_between_reports = iterations_between_reports
f = open(datafile + ".train", 'r')
firstline = f.readline()
f.close
l = string.split(firstline)
self.num_input = int(l[1])
self.num_output = int(l[2])
self.breeding = False
self.stage = 0
self.netsTried = 0
self.maxMutations = 18
self.populationSize = 12
self.trainingData = libfann.training_data()
self.trainingData.read_train_from_file(datafile + ".train")
self.testData = libfann.training_data()
self.testData.read_train_from_file(datafile + ".test")
self.flist = [
libfann.LINEAR, libfann.SIGMOID, libfann.SIGMOID_STEPWISE,
libfann.SIGMOID_SYMMETRIC, libfann.SIGMOID_SYMMETRIC_STEPWISE,
libfann.GAUSSIAN, libfann.GAUSSIAN_SYMMETRIC, libfann.ELLIOT,
libfann.ELLIOT_SYMMETRIC, libfann.LINEAR_PIECE,
libfann.LINEAR_PIECE_SYMMETRIC, libfann.SIN_SYMMETRIC,
libfann.COS_SYMMETRIC
]
self.mutationlist = [
"change_connection_rate", "change_learning_rate",
"change_num_neurons_hidden", "change_num_layers_hidden",
"change_max_iterations", "change_training_algorithm",
"change_activation_function_hidden",
"change_activation_function_output", "change_learning_momentum",
"change_activation_steepness_hidden",
"change_activation_steepness_output", "change_training_param"
]
self.trmutlist = [
"change_connection_type",
"change_quickprop_decay",
"change_quickprop_mu",
"change_rprop_increase_factor",
"change_rprop_decrease_factor",
"change_rprop_delta_min",
"change_rprop_delta_max",
# "change_rprop_delta_zero"
]
def train(self, inputs, outputs, params):
self.p = inputs.shape[1] #number of input features
self.n_r = outputs.shape[1] #size of output grid in rows
self.n_c = outputs.shape[2] #size of output grid in cols
self.out_min = outputs.min()
self.out_max = outputs.max()
d = self.out_max - self.out_min
self.out_min -= d / 98
self.out_max -= d / 98
outputs = (outputs - self.out_min) / (self.out_max - self.out_min)
assert inputs.shape[0] == outputs.shape[0]
nn = libfann.neural_net()
#nn.create_standard_array((self.p, 50, 50, self.n_r*self.n_c))
nn.create_shortcut_array((self.p, self.n_r*self.n_c))
nn.set_learning_rate(.7)
nn.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC)
nn.set_activation_function_output(libfann.SIGMOID)
data = libfann.training_data()
data.set_train_data(inputs, outputs.reshape((-1, self.n_r*self.n_c)))
#nn.train_on_data(data, 500, 10, .001)
nn.cascadetrain_on_data(data, 15, 1, .001)
nn.save('nn.net')
nn.destroy()
def test(self, ann_file, test_file):
"""Test an artificial neural network."""
if not os.path.isfile(ann_file):
raise IOError("Cannot open %s (no such file)" % ann_file)
if not os.path.isfile(test_file):
raise IOError("Cannot open %s (no such file)" % test_file)
# Get the prefix for the classification columns.
try:
dependent_prefix = self.config.data.dependent_prefix
except:
dependent_prefix = OUTPUT_PREFIX
self.ann = libfann.neural_net()
self.ann.create_from_file(ann_file)
self.test_data = TrainData()
try:
self.test_data.read_from_file(test_file, dependent_prefix)
except IOError as e:
logging.error("Failed to process the test data: %s" % e)
exit(1)
logging.info("Testing the neural network...")
fann_test_data = libfann.training_data()
fann_test_data.set_train_data(self.test_data.get_input(),
self.test_data.get_output())
self.ann.test_data(fann_test_data)
mse = self.ann.get_MSE()
logging.info("Mean Square Error on test data: %f" % mse)
def train(self, train_data):
self.set_train_data(train_data)
hidden_layers = [self.hidden_neurons] * self.hidden_layers
layers = [self.train_data.num_input]
layers.extend(hidden_layers)
layers.append(self.train_data.num_output)
sys.stderr.write("Network layout:\n")
sys.stderr.write("* Neuron layers: %s\n" % layers)
sys.stderr.write("* Connection rate: %s\n" % self.connection_rate)
if self.training_algorithm not in ('TRAIN_RPROP',):
sys.stderr.write("* Learning rate: %s\n" % self.learning_rate)
sys.stderr.write("* Activation function for the hidden layers: %s\n" % self.activation_function_hidden)
sys.stderr.write("* Activation function for the output layer: %s\n" % self.activation_function_output)
sys.stderr.write("* Training algorithm: %s\n" % self.training_algorithm)
self.ann = libfann.neural_net()
self.ann.create_sparse_array(self.connection_rate, layers)
self.ann.set_learning_rate(self.learning_rate)
self.ann.set_activation_function_hidden(getattr(libfann, self.activation_function_hidden))
self.ann.set_activation_function_output(getattr(libfann, self.activation_function_output))
self.ann.set_training_algorithm(getattr(libfann, self.training_algorithm))
fann_train_data = libfann.training_data()
fann_train_data.set_train_data(self.train_data.get_input(), self.train_data.get_output())
self.ann.train_on_data(fann_train_data, self.epochs, self.iterations_between_reports, self.desired_error)
return self.ann
def main():
# setting the prediction parameters
known_days = 7
predict_days = 1
verify_days = 30
# setting up the parameters of the network
connection_rate = 1
learning_rate = 0.1
num_input = known_days * 2
num_hidden = 60
num_output = predict_days
# setting up the parameters of the network, continued
desired_error = 0.000040
max_iterations = 10000
iteration_between_reports = 100
# setting up the network
net = libfann.neural_net()
net.create_sparse_array(connection_rate, (num_input, num_hidden, num_output))
net.set_learning_rate(learning_rate)
net.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
# read the input file and format data
fin = open("cw3.in")
lines = fin.readlines()
fin.close()
rawdata = list(map(float, lines))[-1000:]
datain0 = rawdata[0::2]
datain1 = rawdata[1::2]
n0 = max(datain0) * 1.4
n1 = max(datain1) * 1.4
datain0 = list(map(lambda x: x / n0, datain0))
datain1 = list(map(lambda x: x / n1, datain1))
# train the network
data = libfann.training_data()
drange = range(len(datain0) - known_days - verify_days)
data.set_train_data(
map(lambda x: datain0[x:][:known_days] + datain1[x:][:known_days], drange),
map(lambda x: datain0[x + known_days:][:predict_days], drange)
)
net.train_on_data(data, max_iterations, iteration_between_reports, desired_error)
#
result = []
for i in range(verify_days):
dtest = datain0[-known_days - verify_days + i:][:known_days] + datain1[-known_days - verify_days + i:][:known_days]
result += [net.run(dtest)[0] * n0]
plot.plot(list(map(lambda x: x * n0, datain0[-verify_days: -verify_days])) + result, "r")
plot.plot(map(lambda x: x * n0, datain0[-verify_days:]), "b")
#plot.plot(list(map(lambda x: x * n0, datain0[-verify_days * 2: -verify_days])) + result, "r")
#plot.plot(map(lambda x: x * n0, datain0[-verify_days * 2:]), "b")
plot.show()
# net.train_on_file("cw3.in", max_iterations, iteration_between_reports, desired_error)
#print(net.run([1,1]))
print("hehe")
return
def test(self, ann_file, test_file):
"""Test an artificial neural network."""
if not os.path.isfile(ann_file):
raise IOError("Cannot open %s (no such file)" % ann_file)
if not os.path.isfile(test_file):
raise IOError("Cannot open %s (no such file)" % test_file)
# Get the prefix for the classification columns.
try:
dependent_prefix = self.config.data.dependent_prefix
except:
dependent_prefix = OUTPUT_PREFIX
self.ann = libfann.neural_net()
self.ann.create_from_file(ann_file)
self.test_data = TrainData()
try:
self.test_data.read_from_file(test_file, dependent_prefix)
except IOError as e:
logging.error("Failed to process the test data: %s" % e)
exit(1)
logging.info("Testing the neural network...")
fann_test_data = libfann.training_data()
fann_test_data.set_train_data(self.test_data.get_input(),
self.test_data.get_output())
self.ann.test_data(fann_test_data)
mse = self.ann.get_MSE()
logging.info("Mean Square Error on test data: %f" % mse)
def __init__(self,
datafile,
desired_error = 0.0000000001,
iterations_between_reports = 1000):
self.datafile = datafile
self.desired_error = desired_error
self.iterations_between_reports = iterations_between_reports
f = open(datafile+".train", 'r')
firstline = f.readline()
f.close
l = string.split(firstline)
self.num_input = int(l[1])
self.num_output = int(l[2])
self.breeding = False
self.stage = 0
self.netsTried = 0
self.maxMutations = 18
self.populationSize = 12
self.trainingData = libfann.training_data()
self.trainingData.read_train_from_file(datafile+".train")
self.testData = libfann.training_data()
self.testData.read_train_from_file(datafile+".test")
self.flist = [libfann.FANN_LINEAR,libfann.FANN_SIGMOID,libfann.FANN_SIGMOID_STEPWISE,libfann.FANN_SIGMOID_SYMMETRIC,libfann.FANN_SIGMOID_SYMMETRIC_STEPWISE,
libfann.FANN_GAUSSIAN,libfann.FANN_GAUSSIAN_SYMMETRIC,libfann.FANN_ELLIOT,libfann.FANN_ELLIOT_SYMMETRIC,libfann.FANN_LINEAR_PIECE,
libfann.FANN_LINEAR_PIECE_SYMMETRIC,libfann.FANN_SIN_SYMMETRIC,libfann.FANN_COS_SYMMETRIC]
self.mutationlist = ["change_connection_rate",
"change_learning_rate",
"change_num_neurons_hidden",
"change_num_layers_hidden",
"change_max_iterations",
"change_training_algorithm",
"change_activation_function_hidden",
"change_activation_function_output",
"change_learning_momentum",
"change_activation_steepness_hidden",
"change_activation_steepness_output",
"change_training_param"]
self.trmutlist = ["change_connection_type",
"change_quickprop_decay",
"change_quickprop_mu",
"change_rprop_increase_factor",
"change_rprop_decrease_factor",
"change_rprop_delta_min",
"change_rprop_delta_max",
# "change_rprop_delta_zero"
]
def load_data_prefix(prefix):
inp = numpy.loadtxt(prefix + "_i.txt")
inp = check_matrix(inp)
out = numpy.loadtxt(prefix + "_o.txt")
out = check_matrix(out)
data = fann.training_data()
data.set_train_data(inp, out)
return data
def testNet():
data = libfann.training_data()
data.read_train_from_file(test_file);
ann = libfann.neural_net()
ann.create_from_file(nn_file)
ann.reset_MSE()
ann.test_data(data)
print("Mean square error: {0}".format(ann.get_MSE()));
def load_data_prefix(prefix): inp = numpy.loadtxt(prefix + "_i.txt") inp = check_matrix(inp) out = numpy.loadtxt(prefix + "_o.txt") out = check_matrix(out) data = fann.training_data() data.set_train_data(inp,out) return data
def test(self, test_data):
self.set_test_data(test_data)
fann_test_data = libfann.training_data()
fann_test_data.set_train_data(self.test_data.get_input(), self.test_data.get_output())
self.ann.reset_MSE()
self.ann.test_data(fann_test_data)
return self.ann.get_MSE()
def test(self):
print "Creating network."
train_data = libfann.training_data()
train_data.read_train_from_file(tfile)
ann = libfann.neural_net()
ann.create_sparse_array(
connection_rate, (len(train_data.get_input()[0]), num_neurons_hidden, len(train_data.get_output()[0]))
)
ann.set_learning_rate(learning_rate)
ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC_STEPWISE)
ann.set_activation_function_output(libfann.SIGMOID_STEPWISE)
ann.set_training_algorithm(libfann.TRAIN_INCREMENTAL)
ann.train_on_data(train_data, max_iterations, iterations_between_reports, desired_error)
print "Testing network"
test_data = libfann.training_data()
test_data.read_train_from_file(test_file)
ann.reset_MSE()
ann.test_data(test_data)
print "MSE error on test data: %f" % ann.get_MSE()
def load_data(filename, in_outs): a = numpy.loadtxt(filename) inp = numpy.compress(numpy.ones(in_outs[0]), a, axis=1) inp = check_matrix(inp) out = numpy.compress(numpy.concatenate([numpy.zeros(in_outs[0]), numpy.ones(in_outs[1])]), a, axis=1) out = check_matrix(out) data = fann.training_data() data.set_train_data(inp,out) return data
def test(self, test_data):
self.set_test_data(test_data)
fann_test_data = libfann.training_data()
fann_test_data.set_train_data(self.test_data.get_input(),
self.test_data.get_output())
self.ann.reset_MSE()
self.ann.test_data(fann_test_data)
return self.ann.get_MSE()
def test(self):
print "Creating network."
train_data = libfann.training_data()
train_data.read_train_from_file(tfile)
ann = libfann.neural_net()
ann.create_sparse_array(
connection_rate,
(len(train_data.get_input()[0]), num_neurons_hidden,
len(train_data.get_output()[0])))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC_STEPWISE)
ann.set_activation_function_output(libfann.SIGMOID_STEPWISE)
ann.set_training_algorithm(libfann.TRAIN_INCREMENTAL)
ann.train_on_data(train_data, max_iterations,
iterations_between_reports, desired_error)
print "Testing network"
test_data = libfann.training_data()
test_data.read_train_from_file(test_file)
ann.reset_MSE()
ann.test_data(test_data)
print "MSE error on test data: %f" % ann.get_MSE()
def load_data(self, data_file, val_file=None):
# create training data, and ann object
print "Loading data"
self.train_data = libfann.training_data()
self.train_data.read_train_from_file(data_file)
self.dim_input = self.train_data.num_input_train_data()
self.dim_output = self.train_data.num_output_train_data()
input = self.train_data.get_input()
target = self.train_data.get_output()
data_lo_hi = [0, 0]
for i in range(len(input)):
if target[i][0] < 0.5:
data_lo_hi[0] += 1
elif target[i][0] > 0.5:
data_lo_hi[1] += 1
print "\t Train data is %d low and %d high" % tuple(data_lo_hi)
if (val_file and os.path.exists(val_file)):
print "Loading validation data"
self.do_validation = True
self.val_data = libfann.training_data()
self.val_data.read_train_from_file(val_file)
input = self.val_data.get_input()
target = self.val_data.get_output()
data_lo_hi = [0, 0]
for i in range(len(input)):
if target[i][0] < 0.5:
data_lo_hi[0] += 1
elif target[i][0] > 0.5:
data_lo_hi[1] += 1
print "\t Validation data is %d low and %d high" % tuple(
data_lo_hi)
else:
self.val_data = self.train_data
self.do_validation = False
def load_data(self, data_file,val_file=None):
# create training data, and ann object
print "Loading data"
self.train_data = libfann.training_data()
self.train_data.read_train_from_file(data_file)
self.dim_input=self.train_data.num_input_train_data()
self.dim_output=self.train_data.num_output_train_data()
input=self.train_data.get_input()
target=self.train_data.get_output()
data_lo_hi=[0,0]
for i in range(len(input)):
if target[i][0]<0.5:
data_lo_hi[0]+=1
elif target[i][0]>0.5:
data_lo_hi[1]+=1
print "\t Train data is %d low and %d high" % tuple(data_lo_hi)
if (val_file and os.path.exists(val_file)):
print "Loading validation data"
self.do_validation=True
self.val_data=libfann.training_data()
self.val_data.read_train_from_file(val_file)
input=self.val_data.get_input()
target=self.val_data.get_output()
data_lo_hi=[0,0]
for i in range(len(input)):
if target[i][0]<0.5:
data_lo_hi[0]+=1
elif target[i][0]>0.5:
data_lo_hi[1]+=1
print "\t Validation data is %d low and %d high" % tuple(data_lo_hi)
else:
self.val_data=self.train_data
self.do_validation=False
def load_data(filename, in_outs):
a = numpy.loadtxt(filename)
inp = numpy.compress(numpy.ones(in_outs[0]), a, axis=1)
inp = check_matrix(inp)
out = numpy.compress(numpy.concatenate(
[numpy.zeros(in_outs[0]),
numpy.ones(in_outs[1])]),
a,
axis=1)
out = check_matrix(out)
data = fann.training_data()
data.set_train_data(inp, out)
return data
def mainLoop():
n_iter = 0
last_save = 0
min_test_MSE = 1.0
max_iters_after_save = 50
try:
while True:
n_iter += 1
print "Iteration: %5d " % (n_iter),
seg_copy = map(lambda (c, seg): (c, cv.CloneImage(seg)), segments)
seg_copy = map(lambda (c, seg): (c, spoil(seg)), seg_copy)
shuffle(seg_copy)
f = open(train_file, "w")
f.write("%d %d %d\n" % (len(segments), num_input, num_output))
for c, image in seg_copy:
image = adjustSize(image, (segW, segH))
for y in range(image.height):
for x in range(image.width):
n = image[y, x] / 159.375 - 0.8
f.write("%f " % n)
f.write("\n")
n = charset.index(c)
f.write("-1 " * n + "1" + " -1" * (num_output - n - 1) + "\n")
f.close()
train = libfann.training_data()
train.read_train_from_file(train_file)
ann.train_epoch(train)
train.destroy_train()
print "Train MSE: %f " % (ann.get_MSE()),
print "Train bit fail: %5d " % (ann.get_bit_fail()),
ann.test_data(test)
mse = ann.get_MSE()
print "Test MSE: %f " % (mse),
print "Test bit fail: %5d " % (ann.get_bit_fail()),
if mse < min_test_MSE:
min_test_MSE = mse
ann.save(ann_file)
last_save = n_iter
print "saved",
if n_iter - last_save > max_iters_after_save: break
print
except KeyboardInterrupt:
print "Interrupted by user."
def mainLoop():
n_iter = 0
last_save = 0
min_test_MSE = 1.0
max_iters_after_save = 50
try:
while True:
n_iter += 1
print "Iteration: %5d " % (n_iter),
seg_copy = map(lambda (c, seg): (c, cv.CloneImage(seg)), segments)
seg_copy = map(lambda (c, seg): (c, spoil(seg)), seg_copy)
shuffle(seg_copy)
f = open(train_file, "w")
f.write("%d %d %d\n" % (len(segments), num_input, num_output))
for c, image in seg_copy:
image = adjustSize(image, (segW, segH))
for y in range(image.height):
for x in range(image.width):
n = image[y, x] / 159.375 - 0.8
f.write("%f " % n)
f.write("\n")
n = charset.index(c)
f.write("-1 " * n + "1" + " -1" * (num_output - n - 1) + "\n")
f.close()
train = libfann.training_data()
train.read_train_from_file(train_file)
ann.train_epoch(train)
train.destroy_train()
print "Train MSE: %f " % (ann.get_MSE()),
print "Train bit fail: %5d " % (ann.get_bit_fail()),
ann.test_data(test)
mse = ann.get_MSE()
print "Test MSE: %f " % (mse),
print "Test bit fail: %5d " % (ann.get_bit_fail()),
if mse < min_test_MSE:
min_test_MSE = mse
ann.save(ann_file)
last_save = n_iter
print "saved",
if n_iter - last_save > max_iters_after_save: break
print
except KeyboardInterrupt: print "Interrupted by user."
def train_my_net(data_file, net=None):
desired_error = 0.01
max_iter = 100000
report_time = 100
if net is None:
network = new_net()
else:
network = net
data = libfann.training_data()
data.read_train_from_file(data_file)
network.train_on_data(data, max_iter, report_time, desired_error)
return network
def initNet():
learning_rate = 0.3
num_neurons_hidden = num_input / 3
#desired_error = 0.015
#max_iterations = 10000
#iterations_between_reports = 10
global ann
ann = libfann.neural_net()
ann.create_standard_array((num_input, num_neurons_hidden, num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC_STEPWISE)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
train = libfann.training_data()
train.read_train_from_file(train_file)
ann.init_weights(train)
train.destroy_train()
def TestOnData(nn, testdata):
ann = libfann.neural_net()
ann.create_from_file(nn)
testData = libfann.training_data()
testData.read_train_from_file(testdata)
ann.reset_MSE()
if args.full:
inputs = testData.get_input()
outputs = testData.get_output()
missed_goodbuys = 0
missed_badbuys = 0
correct_goodbuys = 0
correct_badbuys = 0
print "#Row\tCorrect\tCalc\tFail"
for i in xrange(len(inputs)):
nn_out = ann.run(inputs[i])[0]
c_out = outputs[i][0]
s = ' '
if c_out == 1.0 and nn_out < 0.8:
s = 'B'
missed_badbuys += 1
if c_out == 0.0 and nn_out >= 0.8:
s = 'G'
missed_goodbuys += 1
if c_out == 1.0 and nn_out >= 0.8:
correct_badbuys += 1
if c_out == 0.0 and nn_out < 0.8:
correct_goodbuys += 1
print "%5u\t%1.3f\t%1.3f\t%s" % (i+1, outputs[i][0], ann.run(inputs[i])[0], s)
print "Missed %u bad buys of %u (%2.1f%%)" % (missed_badbuys, correct_badbuys+missed_badbuys,
float(missed_badbuys)/(correct_badbuys+missed_badbuys)*100)
print "Missed %u good buys of %u (%2.1f%%)" % (missed_goodbuys, correct_goodbuys+missed_goodbuys,
float(missed_goodbuys)/(correct_goodbuys+missed_goodbuys)*100)
else:
ann.test_data(testData)
print "Bit Fail: " + str(ann.get_bit_fail())
print "Mean Squared Error: " + str(ann.get_MSE())
def initNet():
learning_rate = 0.3
num_neurons_hidden = num_input / 3
#desired_error = 0.015
#max_iterations = 10000
#iterations_between_reports = 10
global ann
ann = libfann.neural_net()
ann.create_standard_array((num_input, num_neurons_hidden, num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC_STEPWISE)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
train = libfann.training_data()
train.read_train_from_file(train_file)
ann.init_weights(train)
train.destroy_train()
def XY_to_fann_train_data(X, Y):
if len(X) != len(Y):
raise ValueError("X and Y must have the same number of lines.")
train_data = libfann.training_data()
if len(X):
dim_X, dim_Y = len(X[0]), len(Y[0])
tmp = tempfile.NamedTemporaryFile(delete=False)
with tmp:
tmp.write("%d %d %d\n"%(len(X), dim_X, dim_Y))
for i in xrange(len(X)):
for line in [ X[i], Y[i] ]:
tmp.write("%s\n"% ' '.join( str(float(val)) for val in line ))
train_data.read_train_from_file(tmp.name)
tmp.unlink(tmp.name)
return train_data
def train(self, train_data):
self.set_train_data(train_data)
hidden_layers = [self.hidden_neurons] * self.hidden_layers
layers = [self.train_data.num_input]
layers.extend(hidden_layers)
layers.append(self.train_data.num_output)
sys.stderr.write("Network layout:\n")
sys.stderr.write("* Neuron layers: %s\n" % layers)
sys.stderr.write("* Connection rate: %s\n" % self.connection_rate)
if self.training_algorithm not in ('TRAIN_RPROP', ):
sys.stderr.write("* Learning rate: %s\n" % self.learning_rate)
sys.stderr.write("* Activation function for the hidden layers: %s\n" %
self.activation_function_hidden)
sys.stderr.write("* Activation function for the output layer: %s\n" %
self.activation_function_output)
sys.stderr.write("* Training algorithm: %s\n" %
self.training_algorithm)
self.ann = libfann.neural_net()
self.ann.create_sparse_array(self.connection_rate, layers)
self.ann.set_learning_rate(self.learning_rate)
self.ann.set_activation_function_hidden(
getattr(libfann, self.activation_function_hidden))
self.ann.set_activation_function_output(
getattr(libfann, self.activation_function_output))
self.ann.set_training_algorithm(
getattr(libfann, self.training_algorithm))
fann_train_data = libfann.training_data()
fann_train_data.set_train_data(self.train_data.get_input(),
self.train_data.get_output())
self.ann.train_on_data(fann_train_data, self.epochs,
self.iterations_between_reports,
self.desired_error)
return self.ann
def __init__(self,xdat,ydat,idxs):
if shape(xdat)[0] != shape(ydat)[0]:
raise Exception('dimension mismatch b/w x, y')
nt = len(xdat)
ny = shape(ydat)[1]
nx = shape(xdat)[1]
num_input = nx;
num_output = ny;
num_layers = 3;
num_neurons_hidden = 3;
desired_error = 0.2;
max_epochs =2000;
epochs_between_reports = 1000;
net = fann.neural_net()
net.create_standard_array([num_layers, num_input, num_neurons_hidden, num_output]);
net.set_activation_function_hidden( fann.SIGMOID_SYMMETRIC);
net.set_activation_function_output( fann.SIGMOID_SYMMETRIC);
t = fann.training_data()
t.set_train_data(xdat,ydat)
nt = net.train_on_data(t,max_epochs,epochs_between_reports,desired_error)
out = net.save( "xor_float.net");
print net.get_training_algorithm()
raise Exception()
fann.train_on_file( "xor.data", max_epochs, epochs_between_reports, desired_error);
out = net.save( "xor_float.net");
net.destroy();
def test(self, data):
"""Test the trained neural network.
Expects an instance of :class:`~nbclassify.data.TrainData`. Returns the
mean square error on the test data `data`.
"""
if not isinstance(data, TrainData):
raise ValueError("Training data must be an instance of TrainData")
if not self.ann:
raise ValueError("No neural network was trained yet")
if data.num_input != self.train_data.num_input:
raise ValueError("Number of inputs of test data must be same as " \
"train data")
if data.num_output != self.train_data.num_output:
raise ValueError("Number of output of test data must be same as " \
"train data")
fann_test_data = libfann.training_data()
fann_test_data.set_train_data(data.get_input(), data.get_output())
self.ann.reset_MSE()
self.ann.test_data(fann_test_data)
return self.ann.get_MSE()
def test(self, data):
"""Test the trained neural network.
Expects an instance of :class:`~nbclassify.data.TrainData`. Returns the
mean square error on the test data `data`.
"""
if not isinstance(data, TrainData):
raise ValueError("Training data must be an instance of TrainData")
if not self.ann:
raise ValueError("No neural network was trained yet")
if data.num_input != self.train_data.num_input:
raise ValueError("Number of inputs of test data must be same as " \
"train data")
if data.num_output != self.train_data.num_output:
raise ValueError("Number of output of test data must be same as " \
"train data")
fann_test_data = libfann.training_data()
fann_test_data.set_train_data(data.get_input(), data.get_output())
self.ann.reset_MSE()
self.ann.test_data(fann_test_data)
return self.ann.get_MSE()
def train(self, data):
"""Train a neural network on training data `data`.
Returns a FANN structure.
"""
self.set_train_data(data)
# Check some values.
if not self.train_type in ('ordinary','cascade'):
raise ValueError("Unknown training type `%s`" % self.train_type)
if self.train_type == 'cascade':
if not self.training_algorithm in ('TRAIN_RPROP','TRAIN_QUICKPROP'):
raise ValueError("Expected TRAIN_RPROP or TRAIN_QUICKPROP "\
"as the training algorithm")
# Get FANN train data object.
fann_train_data = libfann.training_data()
fann_train_data.set_train_data(self.train_data.get_input(),
self.train_data.get_output())
if self.train_type == 'ordinary':
hidden_layers = [self.hidden_neurons] * self.hidden_layers
layers = [self.train_data.num_input]
layers.extend(hidden_layers)
layers.append(self.train_data.num_output)
self.ann = libfann.neural_net()
self.ann.create_sparse_array(self.connection_rate, layers)
# Set training parameters.
self.ann.set_learning_rate(self.learning_rate)
self.ann.set_activation_function_hidden(
getattr(libfann, self.activation_function_hidden))
self.ann.set_activation_function_output(
getattr(libfann, self.activation_function_output))
self.ann.set_training_algorithm(
getattr(libfann, self.training_algorithm))
sys.stderr.write("Ordinary training...\n")
self.ann.print_parameters()
# Ordinary training.
self.ann.train_on_data(fann_train_data, self.epochs,
self.iterations_between_reports, self.desired_error)
if self.train_type == 'cascade':
# This algorithm adds neurons to the neural network while training,
# which means that it needs to start with an ANN without any hidden
# layers.
layers = [self.train_data.num_input, self.train_data.num_output]
self.ann = libfann.neural_net()
self.ann.create_shortcut_array(layers)
# Set training parameters.
self.ann.set_training_algorithm(
getattr(libfann, self.training_algorithm))
self.ann.set_activation_function_hidden(
getattr(libfann, self.activation_function_hidden))
self.ann.set_activation_function_output(
getattr(libfann, self.activation_function_output))
self.ann.set_cascade_activation_steepnesses(
self.cascade_activation_steepnesses)
self.ann.set_cascade_num_candidate_groups(
self.cascade_num_candidate_groups)
sys.stderr.write("Cascade training...\n")
self.ann.print_parameters()
# Cascade training.
self.ann.cascadetrain_on_data(fann_train_data, self.max_neurons,
self.neurons_between_reports, self.desired_error)
return self.ann
ann.set_activation_function_output(libfann.SIGMOID)
#Set the activation function for the output layer.
#ann.set_activation_function_output(libfann.LINEAR)
# start training the network
print "Training network"
ann.train_on_file("diabetes.train", max_iterations, iterations_between_reports, desired_error)
#Trains on an entire dataset, for a period of time
#from a file
ann.save("diabetes.net") #Save the entire network to a configuration file.
# test outcome
print "Testing network"
ann_train = libfann.training_data()
ann_train.read_train_from_file("diabetes.test")
#ann.create_from_file("diabetes.net") #Constructs a backpropagation neural network
#from a configuration file, which have been
#saved by save.
ann.reset_MSE()
ann.test_data(ann_train)
print "MSE error on test data: %f" % ann.get_MSE()
ann.save("diabetes_net.net")
def train(self, data):
"""Train a neural network on training data `data`.
Returns a FANN structure.
"""
self.set_train_data(data)
# Check some values.
if not self.train_type in ('ordinary','cascade'):
raise ValueError("Unknown training type `%s`" % self.train_type)
if self.train_type == 'cascade':
if not self.training_algorithm in ('TRAIN_RPROP','TRAIN_QUICKPROP'):
raise ValueError("Expected TRAIN_RPROP or TRAIN_QUICKPROP "\
"as the training algorithm")
# Get FANN train data object.
fann_train_data = libfann.training_data()
fann_train_data.set_train_data(self.train_data.get_input(),
self.train_data.get_output())
if self.train_type == 'ordinary':
hidden_layers = [self.hidden_neurons] * self.hidden_layers
layers = [self.train_data.num_input]
layers.extend(hidden_layers)
layers.append(self.train_data.num_output)
self.ann = libfann.neural_net()
self.ann.create_sparse_array(self.connection_rate, layers)
# Set training parameters.
self.ann.set_learning_rate(self.learning_rate)
self.ann.set_activation_function_hidden(
getattr(libfann, self.activation_function_hidden))
self.ann.set_activation_function_output(
getattr(libfann, self.activation_function_output))
self.ann.set_training_algorithm(
getattr(libfann, self.training_algorithm))
sys.stderr.write("Ordinary training...\n")
self.ann.print_parameters()
# Ordinary training.
self.ann.train_on_data(fann_train_data, self.epochs,
self.iterations_between_reports, self.desired_error)
if self.train_type == 'cascade':
# This algorithm adds neurons to the neural network while training,
# which means that it needs to start with an ANN without any hidden
# layers.
layers = [self.train_data.num_input, self.train_data.num_output]
self.ann = libfann.neural_net()
self.ann.create_shortcut_array(layers)
# Set training parameters.
self.ann.set_training_algorithm(
getattr(libfann, self.training_algorithm))
self.ann.set_activation_function_hidden(
getattr(libfann, self.activation_function_hidden))
self.ann.set_activation_function_output(
getattr(libfann, self.activation_function_output))
self.ann.set_cascade_activation_steepnesses(
self.cascade_activation_steepnesses)
self.ann.set_cascade_num_candidate_groups(
self.cascade_num_candidate_groups)
sys.stderr.write("Cascade training...\n")
self.ann.print_parameters()
# Cascade training.
self.ann.cascadetrain_on_data(fann_train_data, self.max_neurons,
self.neurons_between_reports, self.desired_error)
return self.ann
#!/usr/bin/python
from pyfann import libfann
num_neurons_hidden = 4
num_output = 1
desired_error = 0.0001
max_neurons = 40
neurons_between_reports = 1
steepnesses = [0.1, 0.2, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1]
train_data = libfann.training_data()
train_data.read_train_from_file("../../benchmarks/datasets/two-spiral.train")
test_data = libfann.training_data()
test_data.read_train_from_file("../../benchmarks/datasets/two-spiral.test")
train_data.scale_train_data(0, 1)
test_data.scale_train_data(0, 1)
ann = libfann.neural_net()
ann.create_shortcut_array(
[len(train_data.get_input()[0]),
len(train_data.get_output()[0])])
ann.set_training_algorithm(libfann.TRAIN_RPROP)
ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC)
ann.set_activation_function_output(libfann.LINEAR_PIECE)
ann.set_activation_steepness_hidden(0.5)
ann.set_activation_steepness_output(0.5)
def fit_NN(tr_movies, tr_responses, val_movies, val_responses, n_lag):
p_movs = [
np.pad(m, ((0, 0), (0, 0), (n_lag - 1, 0)), mode='constant')
for m in tr_movies
]
tr_data = tr_responses.data
mean_rsps = []
for i in range(tr_data.shape[1]):
mean_rsps.append(tr_data[:, i, :, :].mean())
tr_data[:, i, :, :] -= tr_data[:, i, :, :].mean()
LY, LX, T = tr_movies[0].shape
S, N, T, R = tr_data.shape
num_input = LY * LX * n_lag
num_output = N
num_hidden = 1000
epochs = 50
epochs_between_reports = 1
desired_error = 0.25
learning_rate = 0.7
connection_rate = 0.1
train_DS = []
val_DS = []
for i_s in range(S):
# Training data
for i_tr in range(R):
for i_t in range(T):
inp_tr = p_movs[i_s][:, :, i_t:i_t + n_lag].flatten()
out_tr = tr_data[i_s, :, i_t, i_tr]
train_DS.append([inp_tr, out_tr])
X_tr, Y_tr = zip(*train_DS)
p_movs = [
np.pad(m, ((0, 0), (0, 0), (n_lag - 1, 0)), mode='constant')
for m in val_movies
]
val_data = val_responses.data
for i in range(N):
val_data[:, i, :, :] -= mean_rsps[i]
S, N, T, R = val_data.shape
for i_s in range(S):
# Validation data
for i_tr in range(R):
for i_t in range(T):
inp_val = p_movs[i_s][:, :, i_t:i_t + n_lag].flatten()
out_val = val_data[i_s, :, i_t, i_tr]
val_DS.append([inp_val, out_val])
X_val, Y_val = zip(*val_DS)
train_data = fann.training_data()
train_data.set_train_data(X_tr, Y_tr)
net = fann.neural_net()
net.create_sparse_array(connection_rate,
(num_input, num_hidden, num_output))
net.set_learning_rate(learning_rate)
net.set_activation_function_output(fann.LINEAR)
net.set_activation_function_hidden(fann.SIGMOID_SYMMETRIC_STEPWISE)
net.train_on_data(train_data, epochs, epochs_between_reports,
desired_error)
pred = np.zeros((len(Y_val), N))
for i in range(len(Y_val)):
pred[i, :] = net.run(X_val[i])
print mean_absolute_error(np.array(Y_val), pred)
return net
#!/usr/bin/python from pyfann import libfann as fann import numpy as np from matplotlib import pyplot as plt X = np.random.random((500, 2)) Y = (np.dot(X, [1,1]) + 6.0).reshape((500,1)) X_tr = X[:400,:] Y_tr = Y[:400,:] X_te = X[400:,:] Y_te = Y[400:,:] train_data = fann.training_data() test_data = fann.training_data() train_data.set_train_data(X_tr, Y_tr) test_data.set_train_data(X_te, Y_te) connection_rate = 1 learning_rate = 0.7 num_input = 2 num_output = 1 num_hidden = 4 desired_error = 0.0001 max_iterations = 100000 iterations_between_reports = 1000 nn = fann.neural_net() nn.create_sparse_array(connection_rate, (num_input, num_hidden, num_output)) nn.set_learning_rate(learning_rate)
class AnnColorizer(object):
def __init__(self,
learning_rate=0.9,
num_neurons_hidden=30,
hidden_layers=2,
max_iterations=5000,
iterations_between_reports=100,
train_pct=0.6,
desired_error=0.0006,
train_count=0,
window_size=10):
self.learning_rate = learning_rate
self.num_input = 3
self.num_neurons_hidden = num_neurons_hidden
self.num_output = 3
self.desired_error = desired_error
self.train_pct = train_pct
self.train_count = train_count
self.max_iterations = max_iterations
self.iterations_between_reports = iterations_between_reports
self.ann = libfann.neural_net()
self.hidden_layers = hidden_layers
ann_layout = []
ann_layout.append(self.num_input)
for i in range(self.hidden_layers):
ann_layout.append(self.num_neurons_hidden)
ann_layout.append(self.num_output)
self.ann.create_standard_array(ann_layout)
self.ann.set_learning_rate(self.learning_rate)
self.ann.set_training_algorithm(libfann.TRAIN_RPROP)
self.ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC)
self.ann.set_activation_function_output(libfann.LINEAR)
self.ann.set_train_error_function(libfann.STOPFUNC_MSE)
self.window_size = window_size
# print "ANN Setup with learning_rate:%0.1f neurons_hidden:%d hidden_layers:%d max_iterations:%d train_pct:%0.1f train_cnt:%d" % (learning_rate, num_neurons_hidden, hidden_layers, max_iterations, train_pct, train_count)
print(
"ANN Setup with learning_rate:%.1f neurons_hidden:%d hidden_layers:%d max_iterations:%d train_pct:%.1f train_cnt:%d window_size:%d"
% (learning_rate, num_neurons_hidden, hidden_layers,
max_iterations, train_pct, train_count, window_size))
#self.ann.print_parameters()
def __trainFannV2(self, img, image_name):
training_filename = image_name + "_train.dat"
trainf = open(training_filename, 'w')
l, a, b = cv2.split(cv2.cvtColor(img, cv.CV_BGR2Lab))
xdim, ydim = l.shape
if (self.train_count == 0):
max_train_count = int(math.floor(xdim * ydim * self.train_pct))
else:
max_train_count = self.train_count
print("Training pixels %d" % (max_train_count))
#max_train_count=3000
num_input = self.ann.get_num_input()
num_output = self.ann.get_num_output()
dims = [max_train_count, num_input, num_output]
print(*dims, sep=' ', file=trainf)
print("Image Dimensions " + str(l.shape))
f = trainf
count = 1
for k in xrange(max_train_count):
#choose random pixel in training image
try:
i = int(np.random.uniform(xdim))
j = int(np.random.uniform(ydim))
features = self.__get_features(l, xdim, ydim, i, j)
print(*features, sep=' ', file=f)
#BGR values
output = [
float(img[i, j, 0]),
float(img[i, j, 1]),
float(img[i, j, 2])
]
print(*output, sep=' ', file=f)
count = count + 1
except Exception, e:
print("Exception when training %s" % (e))
continue
#for i in range(xdim):
# for j in range(ydim):
# features = self.__get_features(l, xdim, ydim, i, j)
# print(*features, sep=' ', file=f)
# #BGR values
# output=[ float(img[i,j,0]), float(img[i,j,1]), float(img[i,j,2])]
# print(*output, sep=' ', file=f)
# count = count + 1
trainf.close()
data = libfann.training_data()
data.read_train_from_file(training_filename)
#data.shuffle_train_data()
train_data = data
self.ann.set_scaling_params(train_data, -1.0, 1.01, -1.0, 1.01)
self.ann.scale_train(train_data)
self.ann.train_on_data(train_data, self.max_iterations,
self.iterations_between_reports,
self.desired_error)
print("Training ANN done ")
self.ann.reset_MSE()
os.remove(training_filename)
ann.set_learning_rate(learning_rate) #Set the learning rate
ann.set_activation_function_output(libfann.SIGMOID)
#Set the activation function for the output layer.
#ann.set_activation_function_output(libfann.LINEAR)
# start training the network
print "Training network"
ann.train_on_file("diabetes.train", max_iterations,
iterations_between_reports, desired_error)
#Trains on an entire dataset, for a period of time
#from a file
ann.save("diabetes.net") #Save the entire network to a configuration file.
# test outcome
print "Testing network"
ann_train = libfann.training_data()
ann_train.read_train_from_file("diabetes.test")
#ann.create_from_file("diabetes.net") #Constructs a backpropagation neural network
#from a configuration file, which have been
#saved by save.
ann.reset_MSE()
ann.test_data(ann_train)
print "MSE error on test data: %f" % ann.get_MSE()
ann.save("diabetes_net.net")
def loadTest():
global test
test = libfann.training_data()
test.read_train_from_file(test_file)
return False
return True
if __name__ == "__main__":
num = 25
if len(sys.argv) > 1:
num = int(sys.argv[1])
print "Using %d ANNs" % num
a = Ensemble(num)
layers = [2, 4, 1]
a.create_standard_array(layers)
a.set_learning_rate(0.6)
a.set_activation_function_hidden(libfann.SIGMOID)
a.set_activation_function_output(libfann.SIGMOID)
data = [([0, 0], [0]), ([0, 1], [1]), ([1, 0], [1]), ([1, 1], [0])]
tdata = libfann.training_data()
tdata.read_train_from_file('xor.data')
for cur_in, cur_out in data:
res = a.run(cur_in)
print "(%d, %d) -> %d" % (cur_in[0], cur_in[1], res[0])
a.train_on_data(tdata, 20, 0, 0.0001)
for cur_in, cur_out in data:
res = a.run(cur_in)
print "(%.2f, %.2f) -> %.2f" % (cur_in[0], cur_in[1], res[0])
def run_fann( num_hidden = 4, fname = "ann_ws496.net", fname_data_prefix = '', n_iter = 100, disp = True, graph = True):
print "num_hidden =", num_hidden
fname_data_train = fname_data_prefix + "train_in.data"
fname_data_test = fname_data_prefix + "test_in.data"
connection_rate = 1
learning_rate = 0.7
num_input = 1024
#num_hidden = 40
num_output = 1
desired_error = 0.0001
max_iterations = 1
iterations_between_reports = 1
ann = libfann.neural_net()
ann.create_sparse_array(connection_rate, (num_input, num_hidden, num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC)
ann.set_activation_function_output(libfann.LINEAR)
# train_data is loaded
train_data = libfann.training_data()
train_data.read_train_from_file( fname_data_train)
# test_data is loaded
test_data = libfann.training_data()
test_data.read_train_from_file( fname_data_test)
train_mse = list()
test_mse = list()
for ii in range(n_iter):
# Training is performed with training data
ann.reset_MSE()
ann.train_on_data(train_data, max_iterations, iterations_between_reports, desired_error)
# Testing is performed with test data
ann.reset_MSE()
ann.test_data(train_data)
mse_train = ann.get_MSE(); train_mse.append( mse_train)
# Testing is performed with test data
ann.reset_MSE()
ann.test_data(test_data)
mse_test = ann.get_MSE(); test_mse.append( mse_test)
if disp:
print ii, "MSE of train, test", mse_train, mse_test
ann.save( fname)
# We show the results of ANN training with validation.
if graph:
plot( train_mse, label = 'train')
plot( test_mse, label = 'test')
legend( loc = 1)
xlabel('iteration')
ylabel('MSE')
grid()
show()
return train_mse, test_mse
def create_net (group_id, num_neurons_hidden):
from pyfann import libfann
from network_functions import save_tests
from network_functions import create_net_record
import MySQLdb as mdb
print "Create Net - " + str(group_id) + ", " + str(num_neurons_hidden)
# To Do
# Needs to querry Net_Group for training file name, test filename, ticker, input_nurons, output_neurons
con = mdb.connect('localhost', 'root', 'fil1202job', 'network')
sql = "select a.ticker, a.train_data, a.test_data, b.no_input, b.no_output from network.net_group a, network.net_type b where a.type = b.id and a.id =" + str(group_id)
cursor = con.cursor()
cursor.execute(sql)
for row in cursor.fetchall():
ticker = str(row[0])
train_file = str(row[1])
test_file = str(row[2])
num_input = row[3]
num_output = row[4]
# disconnect from server
#con.close()
#Parameters that will be passed in
#group_id = 191
# create empty net record and get number
net_id = create_net_record(group_id)
print "Net ID = " + str(net_id)
#train_file = "/home/user/Documents/TrainandTestdata/trainingdataFANN-ACN_out-train.dat"
#test_file = "/home/user/Documents/TrainandTestdata/testdataFANN-ACN_out-train.dat"
#ticker = "ACN"
###
# create file name as ticker_netgroup_netnumber.net
net_name = "/home/user/Documents/Networks/" + str(ticker) + "_" + str(group_id) + "_" + str(net_id) + ".net"
sql2 = "UPDATE `network`.`network` SET `net_file` = \"" + net_name + "\" where id = " + str(net_id)
#print sql2
cursor2 = con.cursor()
cursor2.execute(sql2)
con.commit()
connection_rate = 1
learning_rate = 0.7
#num_input = 7
#num_neurons_hidden = 7
#num_output = 1
desired_error = 0.0001
max_iterations = 100000
iterations_between_reports = 10000
ann = libfann.neural_net()
#ann.create_sparse_array(connection_rate, (num_input, num_neurons_hidden, num_output))
ann.create_standard_array([num_input, num_neurons_hidden, num_neurons_hidden, num_output])
ann.set_learning_rate(learning_rate)
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
train_data = libfann.training_data()
train_data.read_train_from_file(train_file)
test_data = libfann.training_data()
test_data.read_train_from_file(test_file)
ann.train_on_file(train_file, max_iterations, iterations_between_reports, desired_error)
print "\nTrain error: %f, Test error: %f\n\n" %( ann.test_data(train_data),ann.test_data(test_data))
#print "Testing network again"
ann.reset_MSE()
vinput = test_data.get_input()
output = test_data.get_output()
for i in range(len(vinput)):
#print "###"
#print ann.test(vinput[i], output[i])
#print "###'"
outval = ann.test(vinput[i], output[i])
outval = str(outval)
outval = outval.translate(None, '[]')
targetout = str(output[i])
#print "###"
#print targetout
targetout = targetout.translate(None, '[]')
#print targetout
# Store test output in net_test table as [netNumber, record(test row number), prediction, target]
# Stillinserts into temp table net_tests2
save_tests(net_id, i + 1, outval, targetout)
#printingstring = 'Output number ' + str(i) + ' is '+ outval + 'Should have been: ' + targetout
#print printingstring
#print "MSE error on test data: %f" % ann.get_MSE()
ann.save(net_name)
from pyfann import libfann import pickle import numpy import sys import gzip import time from pprint import pprint from vision import * ph_corr = 4 mirror = True # nacitame data set do pamate. train_data_set = libfann.training_data() train_input = [] train_output = [] num_input = 0 for f in sys.argv[1:]: with gzip.open(f, "r") as input_file: tmp_input = [] tmp_output = [] try: while True: in_values, out_values = pickle.load(input_file) tmp_input.append(in_values) tmp_output.append(out_values)
if not exists(output_dir):
os.makedirs(output_dir)
states_files = args.states_files
if len(states_files) == 1:
states_files = glob(states_files[0])
# Convert the files and move them to the build path
if args.fast:
n_max = 200
else:
n_max = inf
convert_two_particle_hdf5_to_fann(states_files, output_dir, train_ratio=0.85, n_max=n_max, min_distance=args.min_distance, max_distance=args.max_distance)
# Load data
train_data = libfann.training_data()
validate_data = libfann.training_data()
test_data = libfann.training_data()
train_data_filename = str(join(output_dir, "train.fann"))
validate_data_filename = str(join(output_dir, "validate.fann"))
test_data_filename = str(join(output_dir, "test.fann"))
print "Loading data:\n", train_data_filename, "\n", validate_data_filename, "\n", test_data_filename
train_data.read_train_from_file(train_data_filename)
validate_data.read_train_from_file(validate_data_filename)
test_data.read_train_from_file(test_data_filename)
# Create and train networks
best_test_result = inf
def fit_NN(tr_movies, tr_responses, val_movies, val_responses, n_lag):
p_movs = [np.pad(m, ((0,0), (0,0), (n_lag-1,0)), mode='constant')
for m in tr_movies]
tr_data = tr_responses.data
mean_rsps = []
for i in range(tr_data.shape[1]):
mean_rsps.append(tr_data[:,i,:,:].mean())
tr_data[:,i,:,:] -= tr_data[:,i,:,:].mean()
LY, LX, T = tr_movies[0].shape
S, N, T, R = tr_data.shape
num_input = LY*LX*n_lag
num_output = N
num_hidden = 1000
epochs = 50
epochs_between_reports = 1
desired_error = 0.25
learning_rate = 0.7
connection_rate = 0.1
train_DS = []
val_DS = []
for i_s in range(S):
# Training data
for i_tr in range(R):
for i_t in range(T):
inp_tr = p_movs[i_s][:,:,i_t:i_t+n_lag].flatten()
out_tr = tr_data[i_s,:,i_t,i_tr]
train_DS.append([inp_tr, out_tr])
X_tr, Y_tr = zip(*train_DS)
p_movs = [np.pad(m, ((0,0), (0,0), (n_lag-1,0)), mode='constant')
for m in val_movies]
val_data = val_responses.data
for i in range(N):
val_data[:,i,:,:] -= mean_rsps[i]
S, N, T, R = val_data.shape
for i_s in range(S):
# Validation data
for i_tr in range(R):
for i_t in range(T):
inp_val = p_movs[i_s][:,:,i_t:i_t+n_lag].flatten()
out_val = val_data[i_s,:,i_t,i_tr]
val_DS.append([inp_val, out_val])
X_val, Y_val = zip(*val_DS)
train_data = fann.training_data()
train_data.set_train_data(X_tr, Y_tr)
net = fann.neural_net()
net.create_sparse_array(connection_rate, (num_input,num_hidden,num_output))
net.set_learning_rate(learning_rate)
net.set_activation_function_output(fann.LINEAR)
net.set_activation_function_hidden(fann.SIGMOID_SYMMETRIC_STEPWISE)
net.train_on_data(train_data, epochs, epochs_between_reports, desired_error)
pred = np.zeros((len(Y_val), N))
for i in range(len(Y_val)):
pred[i,:] = net.run(X_val[i])
print mean_absolute_error(np.array(Y_val), pred)
return net
def test_ann(ann_path, test_data_path, output_path=None, conf_path=None, error=0.01):
"""Test an artificial neural network."""
for path in (ann_path, test_data_path, conf_path):
if path and not os.path.exists(path):
logging.error("Cannot open %s (no such file or directory)" % path)
return 1
if output_path and not conf_path:
raise ValueError("Argument `conf_path` must be set when `output_path` is set")
if conf_path:
yml = open_yaml(conf_path)
if not yml:
return 1
if 'classes' not in yml:
logging.error("Classes are not set in the YAML file. Missing object 'classes'.")
return 1
# Get the prefix for the classification columns.
dependent_prefix = "OUT:"
if 'data' in yml:
dependent_prefix = getattr(yml.data, 'dependent_prefix', dependent_prefix)
ann = libfann.neural_net()
ann.create_from_file(ann_path)
test_data = common.TrainData()
try:
test_data.read_from_file(test_data_path, dependent_prefix)
except ValueError as e:
logging.error("Failed to process the test data: %s" % e)
exit(1)
logging.info("Testing the neural network...")
fann_test_data = libfann.training_data()
fann_test_data.set_train_data(test_data.get_input(), test_data.get_output())
ann.test_data(fann_test_data)
mse = ann.get_MSE()
logging.info("Mean Square Error on test data: %f" % mse)
if not output_path:
return
out_file = open(output_path, 'w')
out_file.write( "%s\n" % "\t".join(['ID','Class','Classification','Match']) )
# Get codeword for each class.
codewords = get_codewords(yml.classes)
total = 0
correct = 0
for label, input, output in test_data:
total += 1
row = []
if label:
row.append(label)
else:
row.append("")
if len(codewords) != len(output):
logging.error("Codeword length (%d) does not match the number of classes. "
"Please make sure the correct classes are set in %s" % (len(output), conf_path))
exit(1)
class_e = get_classification(codewords, output, error)
assert len(class_e) == 1, "The codeword for a class can only have one positive value"
row.append(class_e[0])
codeword = ann.run(input)
try:
class_f = get_classification(codewords, codeword, error)
except ValueError as e:
logging.error("Classification failed: %s" % e)
return 1
row.append(", ".join(class_f))
# Check if the first items of the classifications match.
if len(class_f) > 0 and class_f[0] == class_e[0]:
row.append("+")
correct += 1
else:
row.append("-")
out_file.write( "%s\n" % "\t".join(row) )
fraction = float(correct) / total
out_file.write( "%s\n" % "\t".join(['','','',"%.3f" % fraction]) )
out_file.close()
logging.info("Correctly classified: %.1f%%" % (fraction*100))
logging.info("Testing results written to %s" % output_path)
def get_training_data(data_file):
data = libfann.training_data()
data.read_train_from_file(data_file)
return data
def loadTest():
global test
test = libfann.training_data()
test.read_train_from_file(test_file)
#!/usr/bin/python from pyfann import libfann as fann import numpy as np from matplotlib import pyplot as plt X = np.random.random((500, 2)) Y = (np.dot(X, [1, 1]) + 6.0).reshape((500, 1)) X_tr = X[:400, :] Y_tr = Y[:400, :] X_te = X[400:, :] Y_te = Y[400:, :] train_data = fann.training_data() test_data = fann.training_data() train_data.set_train_data(X_tr, Y_tr) test_data.set_train_data(X_te, Y_te) connection_rate = 1 learning_rate = 0.7 num_input = 2 num_output = 1 num_hidden = 4 desired_error = 0.0001 max_iterations = 100000 iterations_between_reports = 1000 nn = fann.neural_net() nn.create_sparse_array(connection_rate, (num_input, num_hidden, num_output)) nn.set_learning_rate(learning_rate)
def test(self):
"""Test's the BPN Network."""
print "Creating network."
db = dbm.open('config.dat', 'c')
connection_rate = float(db['Connection Rate'])
learning_rate = float(db['Learning Rate'])
desired_error = float(db['Desired Error'])
max_iterations = int(db['Maximum Iterations'])
iterations_between_reports = int(db['Iteration Between Reports'])
ol_act_fun = db['Output Layer Activation Function']
db.close()
hidden_neurons_list = [num_input]
lay_neurons = tuple(
num_neurons_hidden.split(",")) #Hidden Neurons in String
for hid_neuron in lay_neurons:
hidden_neurons_list.append(int(hid_neuron))
hidden_neurons_list.append(num_output)
hnt = tuple(hidden_neurons_list)
hiddenn = num_neurons_hidden.split(",")
train_data = libfann.training_data()
train_data.read_train_from_file(tfile)
ann = libfann.neural_net()
if bpn_type == "SPR":
ann.create_sparse_array(connection_rate, hnt)
elif bpn_type == "STD":
ann.create_standard_array(hnt)
elif bpn_type == "SRT":
ann.create_shortcut_array(hnt)
ann.set_learning_rate(learning_rate)
if talgo == "FANN_TRAIN_INCREMENTAL":
self.ann.set_training_algorithm(libfann.TRAIN_INCREMENTAL)
elif talgo == "FANN_TRAIN_BATCH":
self.ann.set_training_algorithm(libfann.TRAIN_BATCH)
elif talgo == "FANN_TRAIN_RPROP":
self.ann.set_training_algorithm(libfann.TRAIN_RPROP)
try:
db = dbm.open('config.dat', 'c')
inc_factor = float(db['Increase Factor'])
dec_factor = float(db['Decrease Factor'])
delta_min = float(db['Delta Minimum'])
delta_max = float(db['Delta Maximum'])
delta_zero = float(db['Delta Zero'])
db.close()
except KeyError:
pass
else:
self.ann.set_rprop_increase_factor(inc_factor)
self.ann.set_rprop_decrease_factor(dec_factor)
self.ann.set_rprop_delta_min(delta_min)
self.ann.set_rprop_delta_max(delta_max)
elif talgo == "FANN_TRAIN_QUICKPROP":
self.ann.set_training_algorithm(libfann.TRAIN_QUICKPROP)
try:
db = dbm.open('config.dat', 'c')
decay_val = float(db['Decay Value'])
mu_val = float(db['Mu Value'])
db.close()
except KeyError:
pass
else:
self.ann.set_quickprop_decay(decay_val)
self.ann.set_quickprop_mu(mu_val)
self.ann.set_learning_rate(learning_rate)
if ol_act_fun == "LINEAR":
self.ann.set_activation_function_output(libfann.LINEAR)
elif ol_act_fun == "THRESHOLD":
self.ann.set_activation_function_output(libfann.THRESHOLD)
elif ol_act_fun == "THRESHOLD SYMMETRIC":
self.ann.set_activation_function_output(
libfann.THRESHOLD_SYMMETRIC)
elif ol_act_fun == "SIGMOID":
self.ann.set_activation_function_output(libfann.SIGMOID)
elif ol_act_fun == "SIGMOID STEPWISE":
self.ann.set_activation_function_output(libfann.SIGMOID_STEPWISE)
elif ol_act_fun == "SIGMOID SYMMETRIC":
self.ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC)
elif ol_act_fun == "GAUSSIAN":
self.ann.set_activation_function_output(libfann.GAUSSIAN)
elif ol_act_fun == "GAUSSIAN SYMMETRIC":
self.ann.set_activation_function_output(libfann.GAUSSIAN_SYMMETRIC)
elif ol_act_fun == "ELLIOT":
self.ann.set_activation_function_output(libfann.ELLIOT)
elif ol_act_fun == "ELLIOT SYMMETRIC":
self.ann.set_activation_function_output(libfann.ELLIOT_SYMMETRIC)
elif ol_act_fun == "LINEAR PIECE":
self.ann.set_activation_function_output(libfann.LINEAR_PIECE)
elif ol_act_fun == "LINEAR PIECE SYMMETRIC":
self.ann.set_activation_function_output(
libfann.LINEAR_PIECE_SYMMETRIC)
elif ol_act_fun == "SIN SYMMETRIC":
self.ann.set_activation_function_output(libfann.SIN_SYMMETRIC)
elif ol_act_fun == "COS SYMMETRIC":
self.ann.set_activation_function_output(libfann.COS_SYMMETRIC)
elif ol_act_fun == "SIN":
self.ann.set_activation_function_output(libfann.SIN)
elif ol_act_fun == "COS":
self.ann.set_activation_function_output(libfann.COS)
elif ol_act_fun == "SIGMOID SYMMETRIC STEPWISE":
self.ann.set_activation_function_output(
libfann.SIGMOID_SYMMETRIC_STEPWISE)
#For Advanced Parameters related to Fixed Topology
try:
db = dbm.open('config.dat', 'c')
lmomentum = float(db['Learning Momentum'])
af_neuron_number = db['AF for Neuron']
af_n = db['AF Neuron']
af_layer_number = int(db['AF for layer'])
af_l = db['AF Layer']
asn = db['Activation Steepness for Neuron']
asl = db['Activation Steepness for layer']
tef = db['Train Error Function']
tsf = db['Train Stop Function']
bfl = float(db['Bit Fail Limit'])
db.close()
except KeyError:
pass
else:
self.ann.set_learning_momentum(lmomentum)
temp_list = af_neuron_number.split(",")
layer_no = int(temp_list[0])
neuron_no = int(temp_list[1])
steepness_list = asn.split(",")
svalue = float(steepness_list[0])
layer = int(steepness_list[1])
neuron = int(steepness_list[2])
steep_layer_list = asl.split(",")
vsteep = float(steep_layer_list[0])
vslayer = int(steep_layer_list[1])
if af_n == "LINEAR":
self.ann.set_activation_function(libfann.LINEAR, layer_no,
neuron_no)
elif af_n == "THRESHOLD":
self.ann.set_activation_function(libfann.THRESHOLD, layer_no,
neuron_no)
elif af_n == "THRESHOLD SYMMETRIC":
self.ann.set_activation_function(libfann.THRESHOLD_SYMMETRIC,
layer_no, neuron_no)
elif af_n == "SIGMOID":
self.ann.set_activation_function(libfann.SIGMOID, layer_no,
neuron_no)
elif af_n == "SIGMOID STEPWISE":
self.ann.set_activation_function(libfann.SIGMOID_STEPWISE,
layer_no, neuron_no)
elif af_n == "SIGMOID SYMMETRIC":
self.ann.set_activation_function(libfann.SIGMOID_SYMMETRIC,
layer_no, neuron_no)
elif af_n == "GAUSSIAN":
self.ann.set_activation_function(libfann.GAUSSIAN, layer_no,
neuron_no)
elif af_n == "GAUSSIAN SYMMETRIC":
self.ann.set_activation_function(libfann.GAUSSIAN_SYMMETRIC,
layer_no, neuron_no)
elif af_n == "ELLIOT":
self.ann.set_activation_function(libfann.ELLIOT, layer_no,
neuron_no)
elif af_n == "ELLIOT SYMMETRIC":
self.ann.set_activation_function(libfann.ELLIOT_SYMMETRIC,
layer_no, neuron_no)
elif af_n == "LINEAR PIECE":
self.ann.set_activation_function(libfann.LINEAR_PIECE,
layer_no, neuron_no)
elif af_n == "LINEAR PIECE SYMMETRIC":
self.ann.set_activation_function(
libfann.LINEAR_PIECE_SYMMETRIC, layer_no, neuron_no)
elif af_n == "SIN SYMMETRIC":
self.ann.set_activation_function(libfann.SIN_SYMMETRIC,
layer_no, neuron_no)
elif af_n == "COS SYMMETRIC":
self.ann.set_activation_function(libfann.COS_SYMMETRIC,
layer_no, neuron_no)
elif af_n == "SIN":
self.ann.set_activation_function(libfann.SIN, layer_no,
neuron_no)
elif af_n == "COS":
self.ann.set_activation_function(libfann.COS, layer_no,
neuron_no)
if af_l == "LINEAR":
self.ann.set_activation_function_layer(libfann.LINEAR,
af_layer_number)
elif af_l == "THRESHOLD":
self.ann.set_activation_function(libfann.THRESHOLD, layer_no,
neuron_no)
elif af_l == "THRESHOLD SYMMETRIC":
self.ann.set_activation_function(libfann.THRESHOLD_SYMMETRIC,
layer_no, neuron_no)
elif af_l == "SIGMOID":
self.ann.set_activation_function(libfann.SIGMOID, layer_no,
neuron_no)
elif af_l == "SIGMOID STEPWISE":
self.ann.set_activation_function(libfann.SIGMOID_STEPWISE,
layer_no, neuron_no)
elif af_l == "SIGMOID SYMMETRIC":
self.ann.set_activation_function(libfann.SIGMOID_SYMMETRIC,
layer_no, neuron_no)
elif af_l == "GAUSSIAN":
self.ann.set_activation_function(libfann.GAUSSIAN, layer_no,
neuron_no)
elif af_l == "GAUSSIAN SYMMETRIC":
self.ann.set_activation_function(libfann.GAUSSIAN_SYMMETRIC,
layer_no, neuron_no)
elif af_l == "ELLIOT":
self.ann.set_activation_function(libfann.ELLIOT, layer_no,
neuron_no)
elif af_l == "ELLIOT SYMMETRIC":
self.ann.set_activation_function(libfann.ELLIOT_SYMMETRIC,
layer_no, neuron_no)
elif af_l == "LINEAR PIECE":
self.ann.set_activation_function(libfann.LINEAR_PIECE,
layer_no, neuron_no)
elif af_l == "LINEAR PIECE SYMMETRIC":
self.ann.set_activation_function(
libfann.LINEAR_PIECE_SYMMETRIC, layer_no, neuron_no)
elif af_l == "SIN SYMMETRIC":
self.ann.set_activation_function(libfann.SIN_SYMMETRIC,
layer_no, neuron_no)
elif af_l == "COS SYMMETRIC":
self.ann.set_activation_function(libfann.COS_SYMMETRIC,
layer_no, neuron_no)
elif af_l == "SIN":
self.ann.set_activation_function(libfann.SIN, layer_no,
neuron_no)
elif af_l == "COS":
self.ann.set_activation_function(libfann.COS, layer_no,
neuron_no)
self.ann.set_activation_steepness(svalue, layer, neuron)
self.ann.set_activation_steepness_layer(vsteep, vslayer)
if tef == "LINEAR":
self.ann.set_train_error_function(libfann.ERRORFUNC_LINEAR)
elif tef == "TANH ERROR FUNCTION":
self.ann.set_train_error_function(libfann.ERRORFUNC_TANH)
if tsf == "MSE":
self.ann.set_train_stop_function(libfann.STOPFUNC_MSE)
elif tsf == "BIT FAIL":
self.ann.set_train_stop_function(libfann.FANN_STOPFUNC_BIT)
self.ann.set_bit_fail_limit(bfl)
finally:
db.close()
#Find Out Whether it is Evolving topology or Fixed Topology
try:
db = dbm.open('config.dat', 'c')
max_neurons = db['Maximum Neurons']
ncascade = True
db.close()
except KeyError:
ncascade = False
finally:
db.close()
if ncascade:
db = dbm.open('config.dat', 'c')
max_neurons = int(db['Maximum Neurons'])
neurons_between_reports = int(db['Neurons Between Reports'])
cdesired_error = float(db['Desired Error'])
db.close()
#For Advanced Cascade Parameters
try:
db = dbm.open('config.dat', 'c')
ocf = db['Output Change Fraction']
db.close()
tcascade = True
except KeyError:
tcascade = False
if tcascade:
db = dbm.open('config.dat', 'c')
ocf = float(db['Output Change Fraction'])
ose = int(db['Output Stagnation Epochs'])
ccf = float(db['Candidate Change Fraction'])
cse = int(db['Candidate Stagnation Epochs'])
wm = float(db['Weight Multiplier'])
cl = float(db['Candidate Limit'])
max_oe = int(db['Maximum Out Epochs'])
min_oe = int(db['Minimum Out Epochs'])
max_ce = int(db['Maximum Candidate Epochs'])
min_ce = int(db['Minimum Candidate Epochs'])
num_cgroup = int(db['Number Candidate Groups'])
db.close()
self.ann.set_cascade_output_change_fraction(ocf)
self.ann.set_cascade_output_stagnation_epochs(ose)
self.ann.set_cascade_candidate_change_fraction(ccf)
self.ann.set_cascade_candidate_stagnation_epochs(cse)
self.ann.set_cascade_weight_multiplier(wm)
self.ann.set_cascade_candidate_limit(cl)
self.ann.set_cascade_max_out_epochs(max_oe)
#self.ann.set_cascade_min_out_epochs(min_oe)
self.ann.set_cascade_max_cand_epochs(max_ce)
#self.ann.set_cascade_min_cand_epochs(min_ce)
self.ann.set_cascade_num_candidate_groups(num_cgroup)
if ncascade:
self.ann.cascadetrain_on_file(tfile, max_neurons,
neurons_between_reports,
cdesired_error)
else:
self.ann.train_on_file(tfile, max_iterations,
iterations_between_reports, desired_error)
print "Testing network"
train_data = libfann.training_data()
train_data.read_train_from_file(tfile)
test_data = libfann.training_data()
test_data.read_train_from_file(test_file)
print "\nTrain error: %f \nTest error: %f\n\n" % (
ann.test_data(train_data), ann.test_data(test_data))
if opts.output_activation == "SIGMOID_SYMMETRIC_STEPWISE":
ann.set_activation_function_output(libfann.SIGMOID_SYMMETRIC_STEPWISE)
elif opts.output_activation == "GAUSSIAN":
ann.set_activation_function_output(libfann.GAUSSIAN)
elif opts.output_activation == "GAUSSIAN_SYMMETRIC":
ann.set_activation_function_output(libfann.GAUSSIAN_SYMMETRIC)
elif opts.output_activation == "SIGMOID":
ann.set_activation_function_output(libfann.SIGMOID)
else:
ann.set_activation_function_output(libfann.SIGMOID_STEPWISE)
ann.set_activation_steepness_output(opts.steep_out)
########## Import training data #####################
print "Getting training data : %s" % opts.training_file
train_data = libfann.training_data()
train_data.read_train_from_file(opts.training_file.replace(".pat",".ann"))
#train_data.scale_train_data(0.0,1.0)
########## GA Training #####################
print "Setting GA training parameters"
genome = G1DConnections.G1DConnections()
genome.evaluator.set(GAnnEvaluators.evaluateMSE)
genome.setParams(rangemin=opts.range_min, rangemax=opts.range_max, layers=layers, bias=bias, gauss_mu=opts.gauss_mu, gauss_sigma=opts.gauss_sigma)
#genome.mutator.set(GAnnMutators.G1DConnMutateNodes)
ga = GAnnGA.GAnnGA(genome, ann, train_data)
ga.setMutationRate(opts.mutation_rate)
ga.setPopulationSize(opts.population)
ga.setGenerations(opts.generations)
print "Start running GA"
def run_fann(num_hidden=4,
fname="ann_ws496.net",
fname_data_prefix='',
n_iter=100,
disp=True,
graph=True):
print "num_hidden =", num_hidden
fname_data_train = fname_data_prefix + "train_in.data"
fname_data_test = fname_data_prefix + "test_in.data"
connection_rate = 1
learning_rate = 0.7
num_input = 1024
#num_hidden = 40
num_output = 1
desired_error = 0.0001
max_iterations = 1
iterations_between_reports = 1
ann = libfann.neural_net()
ann.create_sparse_array(connection_rate,
(num_input, num_hidden, num_output))
ann.set_learning_rate(learning_rate)
ann.set_activation_function_hidden(libfann.SIGMOID_SYMMETRIC)
ann.set_activation_function_output(libfann.LINEAR)
# train_data is loaded
train_data = libfann.training_data()
train_data.read_train_from_file(fname_data_train)
# test_data is loaded
test_data = libfann.training_data()
test_data.read_train_from_file(fname_data_test)
train_mse = list()
test_mse = list()
for ii in range(n_iter):
# Training is performed with training data
ann.reset_MSE()
ann.train_on_data(train_data, max_iterations,
iterations_between_reports, desired_error)
# Testing is performed with test data
ann.reset_MSE()
ann.test_data(train_data)
mse_train = ann.get_MSE()
train_mse.append(mse_train)
# Testing is performed with test data
ann.reset_MSE()
ann.test_data(test_data)
mse_test = ann.get_MSE()
test_mse.append(mse_test)
if disp:
print ii, "MSE of train, test", mse_train, mse_test
ann.save(fname)
# We show the results of ANN training with validation.
if graph:
plot(train_mse, label='train')
plot(test_mse, label='test')
legend(loc=1)
xlabel('iteration')
ylabel('MSE')
grid()
show()
return train_mse, test_mse