dir_name : str
Name of the directory to save the images to
"""
for i, w in enumerate(weights):
# Remove bias and normalize on [0, 1]
w = w[1:]
w -= w.min()
w /= (w.max() - w.min())
w_name = dir_name.joinpath(f'feat_{i:02d}.png')
matplotlib.image.imsave(str(w_name),
w.reshape(28, 28, order='F'),
cmap='Greys_r')
# Seed for consistency
np.random.seed(SEED)
# Load best weights back up for each model
auto_clean = Autoencoder(input_size=INPUTS)
for weight_file in sorted(AUTO_CLEAN_MODEL_DIR.iterdir()):
auto_clean.addLayer(file_name=weight_file)
auto_noisy = Autoencoder(input_size=INPUTS)
for weight_file in sorted(AUTO_NOISY_MODEL_DIR.iterdir()):
auto_noisy.addLayer(file_name=weight_file)
# Neurons to check
neuron_count = auto_clean.layers[0].num_neurons
neurons = np.random.choice(np.arange(neuron_count), 20, replace=False)
clean_weights = auto_clean.layers[0].w[neurons]
drawFeatures(clean_weights, AUTO_CLEAN_IMG_DIR)
noisy_weights = auto_noisy.layers[0].w[neurons]
drawFeatures(noisy_weights, AUTO_NOISY_IMG_DIR)
d.reshape(28, 28, order='F'),
cmap='Greys_r')
pred = autoencoder.predict(d, one_hot=False)
p_name = dir_name.joinpath(f'pred_{i}.png')
matplotlib.image.imsave(str(p_name),
pred.reshape(28, 28, order='F'),
cmap='Greys_r')
# Seed for consistency
np.random.seed(SEED)
# Load best weights back up
autoencoder = Autoencoder(input_size=INPUTS)
weight_files = sorted(AUTO_MODEL_DIR.iterdir())
for weight_file in weight_files[:-1]:
autoencoder.addLayer(file_name=weight_file, output=False)
autoencoder.addLayer(file_name=weight_files[-1], output=True)
# Test on all data and draw samples
test_err = autoencoder.eval(test_data, test_data)
print(f'Test loss: {test_err:0.3f}')
sample_title = 'Autoencoder Sample Outputs'
drawSamples(autoencoder, test_data, 8, AUTO_SAMPLE_DIR, sample_title)
# Graph loss by class
print('Testing train set')
train_loss = getLossByClass(autoencoder, train_data, train_labels)
print('Testing test set')
test_loss = getLossByClass(autoencoder, test_data, test_labels)
x = np.arange(len(train_loss))
plt.figure()
rect_width = 0.35
plt.bar(x - rect_width / 2, train_loss, rect_width, label='Train')
import numpy as np
import matplotlib.pyplot as plt
import pathlib
import csv
if __name__ == '__main__':
# Seed for consistency
np.random.seed(SEED)
# Delete old model
for f in AUTO_NOISY_MODEL_DIR.iterdir():
f.unlink()
# Training constants
# Test network
autoencoder = Autoencoder(input_size=INPUTS)
for h in HIDDEN_LAYER_SIZES:
autoencoder.addLayer(neurons=h, output=False, trainable=True)
autoencoder.addLayer(neurons=INPUTS, output=True, trainable=True)
# Train the network
print('* * * Begin training autoencoder * * *')
autoencoder.train(
noisy_train_data,
train_data,
points_per_epoch=AUTO_NOISY_POINTS_PER_EPOCH,
valid_points=AUTO_NOISY_VALID_POINTS,
max_epochs=AUTO_NOISY_MAX_EPOCHS,
eta=AUTO_NOISY_ETA,
alpha=AUTO_NOISY_ALPHA,
decay=AUTO_NOISY_DECAY,
L=AUTO_NOISY_L,
H=AUTO_NOISY_H,
patience=AUTO_NOISY_PATIENCE,
dir_name : str
Name of the directory to save the images to
"""
for i, w in enumerate(weights):
# Remove bias and normalize on [0, 1]
w = w[1:]
w -= w.min()
w /= (w.max() - w.min())
w_name = dir_name.joinpath(f'feat_{i:02d}.png')
matplotlib.image.imsave(str(w_name),
w.reshape(28, 28, order='F'),
cmap='Greys_r')
# Seed for consistency
np.random.seed(SEED)
# Load best weights back up for each model
autoencoder = Autoencoder(input_size=INPUTS)
for weight_file in sorted(AUTO_MODEL_DIR.iterdir()):
autoencoder.addLayer(file_name=weight_file)
classifier = Classifier(input_size=INPUTS)
for weight_file in sorted(CLASS_MODEL_DIR.iterdir()):
classifier.addLayer(file_name=weight_file)
# Neurons to check
neuron_count = classifier.layers[0].num_neurons
neurons = np.random.choice(np.arange(neuron_count), 20, replace=False)
class_weights = classifier.layers[0].w[neurons]
drawFeatures(class_weights, CLASS_FEAT_DIR)
auto_weights = autoencoder.layers[0].w[neurons]
drawFeatures(auto_weights, AUTO_FEAT_DIR)
