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lasagne_implementation.py
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lasagne_implementation.py
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# -*- coding: utf-8 -*-
"""
Group LASSO regularization for neural networks (Theano/Lasagne)
Author: Simone Scardapane
Preprint: https://arxiv.org/abs/1607.00485
"""
# TODO: RUN THIS SHIT ON UBUNTU
# Necessary imports
import lasagne
from lasagne.nonlinearities import leaky_rectify, softmax
import theano, theano.tensor as T
import numpy as np
import sklearn.datasets, sklearn.preprocessing, sklearn.cross_validation
import matplotlib.pyplot as plt
from tabulate import tabulate
import time
# Define the group lasso penalty
def groupl1(x):
return T.sum(T.sqrt(x.shape[1]) * T.sqrt(T.sum(x ** 2, axis=1)))
# Number of simulations
N_runs = 1
# Maximum number of epochs
max_epochs = 1500
# Define number of layers and number of neurons
H_layers = np.asarray([40, 20])
# Minibatch size
batch_size = 300
# Lasagne Regularizers to be tested
regularizers = [lasagne.regularization.l2,
lasagne.regularization.l1,
groupl1,
lambda x: lasagne.regularization.l1(x) + groupl1(x), # Sparse group LASSO
]
# Define the regularization factors for each algorithm
reg_factors = [10 ** -3.5, 10 ** -3.5, 10 ** -3.5, 10 ** -3.5]
# Define the names (for display purposes)
names = ['L2', 'L1', 'Group L1', 'Sparse GL1']
# Load the dataset (DIGITS)
digits = sklearn.datasets.load_digits()
X = digits.data
y = digits.target
# MNIST
# mnist = sklearn.datasets.fetch_mldata('MNIST original', data_home='C:/Users/ISPAMM/Downloads')
# X = mnist.data
# y = mnist.target
# Preprocessing (input)
scaler = sklearn.preprocessing.MinMaxScaler()
X = scaler.fit_transform(X)
# Output structures
tr_errors = np.zeros((len(regularizers), N_runs))
tst_errors = np.zeros((len(regularizers), N_runs))
tr_times = np.zeros((len(regularizers), N_runs))
tr_obj = np.zeros((len(regularizers), N_runs, max_epochs))
sparsity_weights = np.zeros((len(regularizers), N_runs, len(H_layers) + 1))
sparsity_neurons = np.zeros((len(regularizers), N_runs, len(H_layers) + 1))
# Define the input and output symbolic variables
input_var = T.matrix(name='X')
target_var = T.ivector(name='y')
# Utility function for minibatches
def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt]
for k in np.arange(0, N_runs):
print("Run ", k + 1, " of ", N_runs, "...\n")
# Split the data
X_train, X_test, y_train, y_test = sklearn.cross_validation.train_test_split(X, y, test_size=0.25)
# Define the network structure
network = lasagne.layers.InputLayer((None, X.shape[1]), input_var)
for h in H_layers:
network = lasagne.layers.DenseLayer(network, h, nonlinearity=leaky_rectify, W=lasagne.init.GlorotNormal())
network = lasagne.layers.DenseLayer(network, len(np.unique(y)), nonlinearity=softmax, W=lasagne.init.GlorotNormal())
params_original = lasagne.layers.get_all_param_values(network)
params = lasagne.layers.get_all_params(network, trainable=True)
# Define the loss function
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
# Define the test function
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),
dtype=theano.config.floatX)
test_fn = theano.function([input_var, target_var], test_acc, allow_input_downcast=True)
for r in np.arange(0, len(regularizers)):
# Set to original parameters
lasagne.layers.set_all_param_values(network, params_original)
# Define the regularized loss function
loss_reg = loss.mean() + reg_factors[r] * lasagne.regularization.regularize_network_params(network,
regularizers[r])
# Update function
# updates_reg = lasagne.updates.nesterov_momentum(loss_reg, params,learning_rate=0.01)
updates_reg = lasagne.updates.adam(loss_reg, params)
# Training function
train_fn = theano.function([input_var, target_var], loss_reg, updates=updates_reg, allow_input_downcast=True)
# Train network
print("\tTraining with ", names[r], " regularization, epoch: ")
start = time.time()
for epoch in range(max_epochs):
loss_epoch = 0
batches = 0
if np.mod(epoch, 10) == 0:
print(epoch, "... ")
for batch in iterate_minibatches(X_train, y_train, batch_size, shuffle=True):
input_batch, target_batch = batch
loss_epoch += train_fn(input_batch, target_batch)
batches += 1
tr_obj[r, k, epoch] = loss_epoch / batches
end = time.time()
tr_times[r, k] = end - start
print(epoch, ".")
# Final test with accuracy
print("\tTesting the network with ", names[r], " regularization...")
tr_errors[r, k] = test_fn(X_train, y_train)
tst_errors[r, k] = test_fn(X_test, y_test)
# Check sparsity
params_trained = lasagne.layers.get_all_param_values(network, trainable=True)
sparsity_weights[r, k, :] = [1 - (x.round(decimals=3).ravel().nonzero()[0].shape[0] / x.size) for x in
params_trained[0::2]]
sparsity_neurons[r, k, :] = [x.round(decimals=3).sum(axis=1).nonzero()[0].shape[0] for x in
params_trained[0::2]]
tr_obj_mean = np.mean(tr_obj, axis=1)
# Plot the average loss
plt.figure()
plt.title('Training objective')
for r in np.arange(0, len(regularizers)):
plt.semilogy(tr_obj_mean[r, :], label=names[r])
plt.legend()
# Print the results
print(tabulate([['Tr. accuracy [%]'] + np.mean(tr_errors, axis=1).round(decimals=4).tolist(),
['Test. accuracy [%]'] + np.mean(tst_errors, axis=1).round(decimals=4).tolist(),
['Tr. times [secs.]'] + np.mean(tr_times, axis=1).round(decimals=4).tolist(),
['Sparsity [%]'] + np.mean(sparsity_weights, axis=1).round(decimals=4).tolist(),
['Neurons'] + np.mean(sparsity_neurons, axis=1).round(decimals=4).tolist()],
headers=[''] + names))