def main():
data = load_av_letters('data/allData_mouthROIs.mat')
# create the necessary variable mappings
data_matrix = data['dataMatrix']
data_matrix_len = data_matrix.shape[0]
targets_vec = data['targetsVec']
vid_len_vec = data['videoLengthVec']
iter_vec = data['iterVec']
indexes = create_split_index(data_matrix_len, vid_len_vec, iter_vec)
# split the data
train_data = data_matrix[indexes == True]
train_targets = targets_vec[indexes == True]
test_data = data_matrix[indexes == False]
test_targets = targets_vec[indexes == False]
idx = [i for i, elem in enumerate(test_targets) if elem == 20]
print(train_data.shape)
print(test_data.shape)
print(sum([train_data.shape[0], test_data.shape[0]]))
# resize the input data to 40 x 30
train_data_resized = resize_images(train_data).astype(np.float32)
# normalize the inputs [0 - 1]
train_data_resized = normalize_input(train_data_resized, centralize=True)
test_data_resized = resize_images(test_data).astype(np.float32)
test_data_resized = normalize_input(test_data_resized, centralize=True)
dic = {}
dic['trainDataResized'] = train_data_resized
dic['testDataResized'] = test_data_resized
"""second experiment: overcomplete sigmoid encoder/decoder, squared loss"""
encode_size = 2500
sigma = 0.5
# to get tied weights in the encoder/decoder, create this shared weightMatrix
# 1200 x 2000
w1, layer1 = build_encoder_layers(1200, 2500, sigma)
ae1 = NeuralNet(
layers=layer1,
max_epochs=50,
objective_loss_function=squared_error,
update=adadelta,
regression=True,
verbose=1
)
load = True
save = False
if load:
print('[LOAD] layer 1...')
ae1.load_params_from('layer1.dat')
else:
print('[TRAIN] layer 1...')
ae1.fit(train_data_resized, train_data_resized)
# save params
if save:
print('[SAVE] layer 1...')
ae1.save_params_to('layer1.dat')
train_encoded1 = ae1.get_output('encoder', train_data_resized) # 12293 x 2000
w2, layer2 = build_encoder_layers(2500, 1250)
ae2 = NeuralNet(
layers=layer2,
max_epochs=50,
objective_loss_function=squared_error,
update=adadelta,
regression=True,
verbose=1
)
load2 = True
if load2:
print('[LOAD] layer 2...')
ae2.load_params_from('layer2.dat')
else:
print('[TRAIN] layer 2...')
ae2.fit(train_encoded1, train_encoded1)
save2 = False
if save2:
print('[SAVE] layer 2...')
ae2.save_params_to('layer2.dat')
train_encoded2 = ae2.get_output('encoder', train_encoded1) # 12293 x 1250
w3, layer3 = build_encoder_layers(1250, 600)
ae3 = NeuralNet(
layers=layer3,
max_epochs=100,
objective_loss_function=squared_error,
update=adadelta,
regression=True,
verbose=1
)
load3 = True
if load3:
print('[LOAD] layer 3...')
ae3.load_params_from('layer3.dat')
else:
ae3.fit(train_encoded2, train_encoded2)
save3 = False
if save3:
print('[SAVE] layer 3...')
ae3.save_params_to('layer3.dat')
train_encoded3 = ae3.get_output('encoder', train_encoded2) # 12293 x 1250
w4, layer4 = build_bottleneck_layer(600, 100)
ae4 = NeuralNet(
layers=layer4,
max_epochs=100,
objective_loss_function=squared_error,
update=adadelta,
regression=True,
verbose=1
)
load4 = False
if load4:
print('[LOAD] layer 4...')
ae4.load_params_from('layer4.dat')
else:
print('[TRAIN] layer 4...')
ae4.fit(train_encoded3, train_encoded3)
save4 = True
if save4:
print('[SAVE] layer 4...')
ae4.save_params_to('layer4.dat')
test_enc1 = ae1.get_output('encoder', test_data_resized)
test_enc2 = ae2.get_output('encoder', test_enc1)
test_enc3 = ae3.get_output('encoder', test_enc2)
test_enc4 = ae4.get_output('encoder', test_enc3)
decoder4 = create_decoder(100, 600, w4.T)
decoder4.initialize()
decoder3 = create_decoder(600, 1250, w3.T)
decoder3.initialize()
decoder2 = create_decoder(1250, 2500, w2.T)
decoder2.initialize() # initialize the net
decoder1 = create_decoder(2500, 1200, w1.T)
decoder1.initialize()
test_dec3 = decoder4.predict(test_enc4)
test_dec2 = decoder3.predict(test_dec3)
test_dec1 = decoder2.predict(test_dec2)
X_pred = decoder1.predict(test_dec1)
# plot_loss(ae3)
# plot_loss(ae2)
# plot_loss(ae1)
tile_raster_images(X_pred[4625:4650, :], (30, 40), (5, 5), tile_spacing=(1, 1))
plt.title('reconstructed')
tile_raster_images(test_data_resized[4625:4650, :], (30, 40), (5, 5), tile_spacing=(1, 1))
plt.title('original')
plt.show()
"""
def main():
data = load_av_letters('data/allData_mouthROIs.mat')
# create the necessary variable mappings
data_matrix = data['dataMatrix']
data_matrix_len = data_matrix.shape[0]
targets_vec = data['targetsVec']
vid_len_vec = data['videoLengthVec']
iter_vec = data['iterVec']
indexes = create_split_index(data_matrix_len, vid_len_vec, iter_vec)
# split the data
train_data = data_matrix[indexes == True]
train_targets = targets_vec[indexes == True]
test_data = data_matrix[indexes == False]
test_targets = targets_vec[indexes == False]
idx = [i for i, elem in enumerate(test_targets) if elem == 20]
print(train_data.shape)
print(test_data.shape)
print(sum([train_data.shape[0], test_data.shape[0]]))
# resize the input data to 40 x 30
train_data_resized = resize_images(train_data).astype(np.float32)
# normalize the inputs [0 - 1]
train_data_resized = normalize_input(train_data_resized, centralize=True)
test_data_resized = resize_images(test_data).astype(np.float32)
test_data_resized = normalize_input(test_data_resized, centralize=True)
dic = {}
dic['trainDataResized'] = train_data_resized
dic['testDataResized'] = test_data_resized
"""second experiment: overcomplete sigmoid encoder/decoder, squared loss"""
encode_size = 2500
sigma = 0.5
# to get tied weights in the encoder/decoder, create this shared weightMatrix
# 1200 x 2000
w1, layer1 = build_encoder_layers(1200, 2500, sigma)
ae1 = NeuralNet(layers=layer1,
max_epochs=50,
objective_loss_function=squared_error,
update=adadelta,
regression=True,
verbose=1)
load = True
save = False
if load:
print('[LOAD] layer 1...')
ae1.load_params_from('layer1.dat')
else:
print('[TRAIN] layer 1...')
ae1.fit(train_data_resized, train_data_resized)
# save params
if save:
print('[SAVE] layer 1...')
ae1.save_params_to('layer1.dat')
train_encoded1 = ae1.get_output('encoder',
train_data_resized) # 12293 x 2000
w2, layer2 = build_encoder_layers(2500, 1250)
ae2 = NeuralNet(layers=layer2,
max_epochs=50,
objective_loss_function=squared_error,
update=adadelta,
regression=True,
verbose=1)
load2 = True
if load2:
print('[LOAD] layer 2...')
ae2.load_params_from('layer2.dat')
else:
print('[TRAIN] layer 2...')
ae2.fit(train_encoded1, train_encoded1)
save2 = False
if save2:
print('[SAVE] layer 2...')
ae2.save_params_to('layer2.dat')
train_encoded2 = ae2.get_output('encoder', train_encoded1) # 12293 x 1250
w3, layer3 = build_encoder_layers(1250, 600)
ae3 = NeuralNet(layers=layer3,
max_epochs=100,
objective_loss_function=squared_error,
update=adadelta,
regression=True,
verbose=1)
load3 = True
if load3:
print('[LOAD] layer 3...')
ae3.load_params_from('layer3.dat')
else:
ae3.fit(train_encoded2, train_encoded2)
save3 = False
if save3:
print('[SAVE] layer 3...')
ae3.save_params_to('layer3.dat')
train_encoded3 = ae3.get_output('encoder', train_encoded2) # 12293 x 1250
w4, layer4 = build_bottleneck_layer(600, 100)
ae4 = NeuralNet(layers=layer4,
max_epochs=100,
objective_loss_function=squared_error,
update=adadelta,
regression=True,
verbose=1)
load4 = False
if load4:
print('[LOAD] layer 4...')
ae4.load_params_from('layer4.dat')
else:
print('[TRAIN] layer 4...')
ae4.fit(train_encoded3, train_encoded3)
save4 = True
if save4:
print('[SAVE] layer 4...')
ae4.save_params_to('layer4.dat')
test_enc1 = ae1.get_output('encoder', test_data_resized)
test_enc2 = ae2.get_output('encoder', test_enc1)
test_enc3 = ae3.get_output('encoder', test_enc2)
test_enc4 = ae4.get_output('encoder', test_enc3)
decoder4 = create_decoder(100, 600, w4.T)
decoder4.initialize()
decoder3 = create_decoder(600, 1250, w3.T)
decoder3.initialize()
decoder2 = create_decoder(1250, 2500, w2.T)
decoder2.initialize() # initialize the net
decoder1 = create_decoder(2500, 1200, w1.T)
decoder1.initialize()
test_dec3 = decoder4.predict(test_enc4)
test_dec2 = decoder3.predict(test_dec3)
test_dec1 = decoder2.predict(test_dec2)
X_pred = decoder1.predict(test_dec1)
# plot_loss(ae3)
# plot_loss(ae2)
# plot_loss(ae1)
tile_raster_images(X_pred[4625:4650, :], (30, 40), (5, 5),
tile_spacing=(1, 1))
plt.title('reconstructed')
tile_raster_images(test_data_resized[4625:4650, :], (30, 40), (5, 5),
tile_spacing=(1, 1))
plt.title('original')
plt.show()
"""
