theta, input_size, hidden_size_l1, lamb, sparsity_param, beta, train_data)
sae1_opt_theta, loss, d = (scipy.optimize.fmin_l_bfgs_b(fn,
sae1_theta,
maxfun=maxfun,
iprint=1))
if DISPLAY:
W1, W2, b1, b2 = autoencoder.unflatten(sae1_opt_theta, input_size,
hidden_size_l1)
util.display_network(W1.T)
# === Step 3: Train the second sparse autoencoder ===
#
# Train the second sparse autoencoder on the first autoencoder features.
sae1_features = autoencoder.feedforward_autoencoder(sae1_opt_theta,
hidden_size_l1, input_size,
train_data)
# Randomly initialize the parameters
sae2_theta = autoencoder.initialize_parameters(hidden_size_l2, hidden_size_l1)
fn = lambda theta: autoencoder.sparse_autoencoder_loss(
theta, hidden_size_l1, hidden_size_l2, lamb, sparsity_param, beta,
sae1_features)
sae2_opt_theta, loss, d = (scipy.optimize.fmin_l_bfgs_b(fn,
sae2_theta,
maxfun=maxfun,
iprint=1))
if DISPLAY:
W11, W21, b11, b21 = autoencoder.unflatten(sae1_opt_theta, input_size,
theta = autoencoder.initialize_parameters(hidden_size, input_size)
# The single-parameter function to minimize
fn = lambda theta: autoencoder.sparse_autoencoder_loss(
theta, input_size, hidden_size,lamb, sparsity_param, beta, unlabeled_data)
# Find `opt_theta` by running the sparse autoencoder on unlabeled
# training images.
opt_theta, loss, d = (
scipy.optimize.fmin_l_bfgs_b(fn, theta, maxfun=maxfun, iprint=1, m=20))
# Visualize weights
W1, W2, b1, b2 = autoencoder.unflatten(opt_theta, input_size, hidden_size)
util.display_network(W1.T)
# === Step 3: Extract Features from the Supervised Dataset ===
train_features = autoencoder.feedforward_autoencoder(
opt_theta, hidden_size, input_size, train_data)
test_features = autoencoder.feedforward_autoencoder(
opt_theta, hidden_size, input_size, test_data)
# === Step 4: Train the softmax classifier ===
lamb = 1e-4
num_classes = len(set(train_labels))
softmax_model = softmax.train(hidden_size, num_classes, lamb,
train_features, train_labels, maxfun=100)
# === Step 5: Testing ===
#
# Compute Predictions on the test set (testFeatures) using
# `softmax.predict`.
pred = softmax.predict(softmax_model, test_features)
acc = (test_labels == pred).mean()
# beta, trainData), ...
# sae1Theta, options);
fn = lambda theta: autoencoder.sparse_autoencoder_loss(
theta, input_size, hidden_size_l1, lamb, sparsity_param, beta, train_data
)
sae1_opt_theta, loss, d = scipy.optimize.fmin_l_bfgs_b(fn, sae1_theta, maxfun=maxfun, iprint=1)
if DISPLAY:
W1, W2, b1, b2 = autoencoder.unflatten(sae1_opt_theta, input_size, hidden_size_l1)
util.display_network(W1.T)
# === Step 3: Train the second sparse autoencoder ===
#
# Train the second sparse autoencoder on the first autoencoder features.
sae1_features = autoencoder.feedforward_autoencoder(sae1_opt_theta, hidden_size_l1, input_size, train_data)
# Randomly initialize the parameters
sae2_theta = autoencoder.initialize_parameters(hidden_size_l2, hidden_size_l1)
fn = lambda theta: autoencoder.sparse_autoencoder_loss(
theta, hidden_size_l1, hidden_size_l2, lamb, sparsity_param, beta, sae1_features
)
sae2_opt_theta, loss, d = scipy.optimize.fmin_l_bfgs_b(fn, sae2_theta, maxfun=maxfun, iprint=1)
if DISPLAY:
W11, W21, b11, b21 = autoencoder.unflatten(sae1_opt_theta, input_size, hidden_size_l1)
W12, W22, b12, b22 = autoencoder.unflatten(sae2_opt_theta, hidden_size_l1, hidden_size_l2)
# TODO(zellyn): figure out how to display a 2-level network
# display_network(log(W11' ./ (1-W11')) * W12');
fn = lambda theta: autoencoder.sparse_autoencoder_loss(
theta, input_size, hidden_size, lamb, sparsity_param, beta, unlabeled_data)
# Find `opt_theta` by running the sparse autoencoder on unlabeled
# training images.
opt_theta, loss, d = (scipy.optimize.fmin_l_bfgs_b(fn,
theta,
maxfun=maxfun,
iprint=1,
m=20))
# Visualize weights
W1, W2, b1, b2 = autoencoder.unflatten(opt_theta, input_size, hidden_size)
util.display_network(W1.T)
# === Step 3: Extract Features from the Supervised Dataset ===
train_features = autoencoder.feedforward_autoencoder(opt_theta, hidden_size,
input_size, train_data)
test_features = autoencoder.feedforward_autoencoder(opt_theta, hidden_size,
input_size, test_data)
# === Step 4: Train the softmax classifier ===
lamb = 1e-4
num_classes = len(set(train_labels))
softmax_model = softmax.train(hidden_size,
num_classes,
lamb,
train_features,
train_labels,
maxfun=100)
# === Step 5: Testing ===
#