def perform_experiments(n_runs=10,
n_points=1000,
n_epochs=200,
run_best=False,
verbose=False):
"""
Perform experiments for 5 different neural network architectures and losses.
To run all experiments call this function with default params
:param n_runs: number of runs for which experiment should be repeated
:param n_points: number of training and testing data points used in the experiments
:param n_epochs: number of epochs every architecture should be trained on
:param run_best: If True only the best architecture (Siamese Network with auxiliary loss) is trained
:param verbose: If True, print training and validation loss every epoch
:returns: dictionary containing history of training (training, validation loss and accuracy)
"""
history_mlp_net = []
history_conv_net = []
history_conv_net_aux = []
history_siamese = []
history_siamese_aux = []
for n_run in range(n_runs):
data_set = generate_pair_sets(n_points)
MAX_VAL = 255.0
TRAIN_INPUT = Variable(data_set[0]) / MAX_VAL
TRAIN_TARGET = Variable(data_set[1])
TRAIN_CLASSES = Variable(data_set[2])
TEST_INPUT = Variable(data_set[3]) / MAX_VAL
TEST_TARGET = Variable(data_set[4])
TEST_CLASSES = Variable(data_set[5])
if not run_best:
##############################################################################
# Creates Multilayer Perceptron Network with ReLU activationss
mlp_net = MLPNet(in_features=392,
out_features=2,
n_layers=3,
n_hidden=16)
# Set train flag on (for dropouts)
mlp_net.train()
# Train the model and append the history
history_mlp_net.append(
train_model(mlp_net,
train_input=TRAIN_INPUT.view((n_points, -1)),
train_target=TRAIN_TARGET,
val_input=TEST_INPUT.view((n_points, -1)),
val_target=TEST_TARGET,
n_epochs=n_epochs,
verbose=verbose))
# Set train flag to False for getting accuracies on validation data
mlp_net.eval()
acc = get_accuracy(mlp_net, TEST_INPUT.view(
(n_points, -1)), TEST_TARGET) * 100.0
print("Run: {}, Mlp_net Test Accuracy: {:.3f} %".format(
n_run, acc))
##############################################################################
# Create ConvNet without auxiliary outputs
conv_net = ConvNet(n_classes=2, n_layers=3, n_features=16)
# Set train flag on (for dropouts)
conv_net.train()
# Train the model and append the history
history_conv_net.append(
train_model(conv_net,
train_input=TRAIN_INPUT,
train_target=TRAIN_TARGET,
val_input=TEST_INPUT,
val_target=TEST_TARGET,
n_epochs=n_epochs,
verbose=verbose))
# Set train flag to False for getting accuracies on validation data
conv_net.eval()
acc = get_accuracy(conv_net, TEST_INPUT, TEST_TARGET) * 100.0
print("Run: {}, ConvNet Test Accuracy: {:.3f} %".format(
n_run, acc))
##############################################################################
# Create ConvNet with auxiliary outputs
conv_net_aux = ConvNet(n_classes=22, n_layers=3, n_features=16)
# Set train flag on (for dropouts)
conv_net_aux.train()
# Train the model and append the history
history_conv_net_aux.append(
train_model(conv_net_aux,
train_input=TRAIN_INPUT,
train_target=TRAIN_TARGET,
aux_param=1.0,
train_classes=TRAIN_CLASSES,
val_input=TEST_INPUT,
val_target=TEST_TARGET,
val_classes=TEST_CLASSES,
n_epochs=n_epochs,
verbose=verbose))
# Set train flag to False for getting accuracies on validation data
conv_net_aux.eval()
acc = get_accuracy(conv_net_aux, TEST_INPUT, TEST_TARGET) * 100.0
print("Run: {}, ConvNet Auxilary Test Accuracy: {:.3f} %".format(
n_run, acc))
##############################################################################
# Create Siamese Network without auxiliary outputs
conv_net = BlockConvNet()
conv_net_siamese = DeepSiameseNet(conv_net)
# Set train flag on (for dropouts)
conv_net.train()
conv_net_siamese.train()
# Train the model and append the history
history_siamese.append(
train_model(conv_net_siamese,
train_input=TRAIN_INPUT,
train_target=TRAIN_TARGET,
val_input=TEST_INPUT,
val_target=TEST_TARGET,
n_epochs=n_epochs,
verbose=verbose))
# Set train flag to False for getting accuracies on validation data
conv_net.eval()
conv_net_siamese.eval()
acc = get_accuracy(conv_net_siamese, TEST_INPUT,
TEST_TARGET) * 100.0
print("Run: {}, Siamese Test Accuracy: {:.3f} %".format(
n_run, acc))
##############################################################################
# Create Siamese Network with auxiliary outputs
conv_net = BlockConvNet()
conv_net_siamese_aux = DeepSiameseNet(conv_net)
# Set train flag on (for dropouts)
conv_net.train()
conv_net_siamese_aux.train()
# Train the model and append the history
history_siamese_aux.append(
train_model(conv_net_siamese_aux,
train_input=TRAIN_INPUT,
train_target=TRAIN_TARGET,
train_classes=TRAIN_CLASSES,
val_input=TEST_INPUT,
val_target=TEST_TARGET,
val_classes=TEST_CLASSES,
aux_param=3.0,
n_epochs=n_epochs,
verbose=verbose))
# Set train flag to False for getting accuracies on validation data
conv_net.eval()
conv_net_siamese_aux.eval()
acc = get_accuracy(conv_net_siamese_aux, TEST_INPUT,
TEST_TARGET) * 100.0
print("Run: {}, Siamese Auxilary Test Accuracy: {:.3f} %".format(
n_run, acc))
##############################################################################
return {
'history_mlp_net': history_mlp_net,
'history_conv_net': history_conv_net,
'history_conv_net_aux': history_conv_net_aux,
'history_siamese': history_siamese,
'history_siamese_aux': history_siamese_aux
}
frame_step,
num_mel_bins=40,
lower_frequency=20,
upper_frequency=4000,
num_coefficients=10,
mfcc=True)
strides = [2, 1]
train_ds = signal_generator.make_dataset(train_data, True)
test_ds = signal_generator.make_dataset(test_data, False)
val_ds = signal_generator.make_dataset(val_data, False)
#Train the MultiLayer Perceptron
mlp = MLP()
mlp.train(train_ds, val_ds, 20)
filename = 'Model_mlp'
mlp._model().save(filename)
#Train the Convolutional NN
cnn = ConvNet(strides)
cnn.train(train_ds, val_ds, 20)
filename = 'Model_cnn'
cnn._model().save(filename)
#Train the DS Convolutional NN
dscnn = DS_CNN(strides)
dscnn.train(train_ds, val_ds, 20)
filename = 'Model_dscnn'
dscnn._model().save(filename)
