# data, and the test data. data = mnist_loader.load_mnist() # Set training data and training labels. The training dataset is a set # of 50,000 MNIST (hand-drawn digit) images. training_data = data[0] training_labels = data[1] # Set validation data and validation labels. The validation dataset is a # set of 10,000 MNIST (hand-drawn digit) images. validation_data = data[2] validation_labels = data[3] # Set test data and test labels. The test dataset is a # set of 10,000 MNIST (hand-drawn digit) images. test_data = data[4] test_labels = data[5] # Instantiate the first neural network. net1 = network_library.Network([784, 100, 100, 10], 0.6, 64, cost_function=network_library.QuadraticCost) # Instantiate the second neural network. net2 = network_library.Network([784, 100, 10], 0.6, 64, cost_function=network_library.QuadraticCost) # Compare both networks. network_library.compare_net(net1, net2, training_data, training_labels, test_data, test_labels, 1000, 64, 'Four-layer network', 'Three-layer network')
# Set training data and training labels. The training dataset is a set
# of 50,000 MNIST (hand-drawn digit) images.
training_data = data[0]
training_labels = data[1]
# Set validation data and validation labels. The validation dataset is a
# set of 10,000 MNIST (hand-drawn digit) images.
validation_data = data[2]
validation_labels = data[3]
# Set test data and test labels. The test dataset is a
# set of 10,000 MNIST (hand-drawn digit) images.
test_data = data[4]
test_labels = data[5]
# Instantiate the first neural network.
net1 = network_library.Network([784, 100, 100, 10], 0.6, 64,
cost_function=network_library.CrossEntropyCost, small_weights=True)
# Instantiate the second neural network.
net2 = network_library.Network([784, 100, 100, 10], 0.6, 64,
cost_function=network_library.CrossEntropyCost, small_weights=True)
# Compare both networks.
network_library.compare_net(net1, net2, training_data, training_labels,
test_data, test_labels, 2000, 64, 'L2 regularization: ON ',
'L2 regularization: OFF', L2_test=True)
print("---Done: L2 Regularization Test---")