# allow your sparse autoencoder to get good filters; you do not need to
# change the parameters below.
input_size = 28 * 28
num_classes = 10
hidden_size_L1 = 10 # Layer 1 Hidden Size
hidden_size_L2 = 10 # Layer 2 Hidden Size
lambda_ = 3e-3 # weight decay parameter
# ======================================================================
# STEP 1: Load data from the MNIST database
#
# This loads our training data from the MNIST database files.
train_images = load_MNIST.load_MNIST_images('train-images.idx3-ubyte')
train_labels = load_MNIST.load_MNIST_labels('train-labels.idx1-ubyte')
train_images = train_images[:, 0:10]
train_labels = train_labels[0:10]
# ======================================================================
# STEP 2: Train the first sparse autoencoder
# This trains the first sparse autoencoder on the unlabelled STL training
# images.
# If you've correctly implemented sparseAutoencoderCost.m, you don't need
# to change anything here.
# Randomly initialize the parameters
sae1_theta = utils_hw.initialize(hidden_size_L1, input_size)
J = lambda x: utils_hw.sparse_autoencoder_cost(x, input_size, hidden_size_L1,
lambda_, train_images)
input_size = 28 * 28
num_classes = 10
hidden_size_L1 = 200 # Layer 1 Hidden Size
hidden_size_L2 = 200 # Layer 2 Hidden Size
sparsity_param = 0.1 # desired average activation of the hidden units.
lambda_ = 3e-3 # weight decay parameter
beta = 3 # weight of sparsity penalty term
##======================================================================
## STEP 1: Load data from the MNIST database
#
# This loads our training data from the MNIST database files.
train_images = load_MNIST.load_MNIST_images('data/mnist/train-images-idx3-ubyte')
train_labels = load_MNIST.load_MNIST_labels('data/mnist/train-labels-idx1-ubyte')
##======================================================================
## STEP 2: Train the first sparse autoencoder
# This trains the first sparse autoencoder on the unlabelled STL training
# images.
# If you've correctly implemented sparseAutoencoderCost.m, you don't need
# to change anything here.
# Randomly initialize the parameters
sae1_theta = sparse_autoencoder.initialize(hidden_size_L1, input_size)
J = lambda x: sparse_autoencoder.sparse_autoencoder_cost(x, input_size, hidden_size_L1,
lambda_, sparsity_param,
beta, train_images)
# in the lecture notes).
lambda_ = 3e-3 # weight decay parameter
beta = 3 # weight of sparsity penalty term
maxiter = 400 # Maximum iterations for training
"""
STEP 1: Load data from the MNIST database
This loads our training data from the MNIST database files.
"""
# Load MNIST database files
# Load MNIST database files
train_data = load_MNIST_images('data/mnist/train-images-idx3-ubyte')
train_labels = load_MNIST_labels('data/mnist/train-labels-idx1-ubyte')
"""
STEP 2: Train the first sparse autoencoder
This trains the first sparse autoencoder on the unlabelled STL training images.
If you've correctly implemented sparse_autoencoder_cost, you don't need
to change anything here.
"""
# Randomly initialize the parameters
sae1_theta = initialize_parameters(hidden_size_L1, input_size)
# Instructions: Train the first layer sparse autoencoder, this layer has
# an hidden size of "hidden_size_L1"
num_labels = 5
hidden_size = 196
sparsity_param = 0.1 # desired average activation of the hidden units.
lambda_ = 3e-3 # weight decay parameter
beta = 3 # weight of sparsity penalty term
## ======================================================================
# STEP 1: Load data from the MNIST database
#
# This loads our training and test data from the MNIST database files.
# We have sorted the data for you in this so that you will not have to
# change it.
images = load_MNIST.load_MNIST_images('data/mnist/train-images-idx3-ubyte')
labels = load_MNIST.load_MNIST_labels('data/mnist/train-labels-idx1-ubyte')
unlabeled_index = np.argwhere(labels >= 5).flatten()
labeled_index = np.argwhere(labels < 5).flatten()
num_train = round(labeled_index.shape[0] / 2)
train_index = labeled_index[0:num_train]
test_index = labeled_index[num_train:]
unlabeled_data = images[:, unlabeled_index]
train_data = images[:, train_index]
train_labels = labels[train_index]
test_data = images[:, test_index]
test_labels = labels[test_index]
## STEP 0: Here we provide the relevant parameters values
input_size = 28 * 28
num_classes = 10
hidden_size_L1 = 40 # Layer 1 Hidden Size
hidden_size_L2 = 40 # Layer 2 Hidden Size
lambda_ = 3e-3 # weight decay parameter
##======================================================================
## STEP 1: Load data from the MNIST database
#
# This loads our training data from the MNIST database files.
train_images = load_MNIST.load_MNIST_images(
'C:/Users/leon/Documents/data/mnist/train-images-idx3-ubyte')
train_labels = load_MNIST.load_MNIST_labels(
'C:/Users/leon/Documents/data/mnist/train-labels-idx1-ubyte')
train_images = train_images[:, 0:]
train_labels = train_labels[0:]
train_images = train_images[:, 0:500]
train_labels = train_labels[0:500]
##======================================================================
## STEP 2: Train the first sparse autoencoder
# This trains the first sparse autoencoder on the unlabelled STL training
# images.
# Randomly initialize the parameters
sae1_theta = utils_hw.initialize(hidden_size_L1, input_size)
J = lambda x: utils_hw.sparse_autoencoder_cost(x, input_size, hidden_size_L1,
lambda_, train_images)