def run_test():
learning_rate = random.random()
L1_reg = random.random()
L2_reg = random.random()
hl = HiddenLayer(
rng, num_features, num_outputs, activation=numpy.tanh)
thl = theano_HiddenLayer(
rng, sx, num_features, num_outputs, W=hl.W, b=hl.b, activation=T.tanh)
mlp = MLP(
rng, n_in=num_features, n_hidden=num_hidden, n_out=num_outputs)
# Ensure that the theano MLP shares the same weights & biases as our MLP
hW = theano.shared(value=numpy.copy(mlp.hiddenLayer.W), name='W', borrow=True)
hb = theano.shared(value=numpy.copy(mlp.hiddenLayer.b), name='b', borrow=True)
tmlp = theano_MLP(
rng, sx, n_in=num_features, n_hidden=num_hidden, n_out=num_outputs, hW=hW, hb=hb)
thl_out = theano.function([sx], thl.output)
tmlp_p_y_given_x = theano.function([sx], tmlp.logRegressionLayer.p_y_given_x)
tmlp_neg_log = theano.function(
inputs=[sx, sy],
outputs=tmlp.negative_log_likelihood(sy))
tmlp_train_model, tmlp_test_model = theano_train_model(tmlp, learning_rate, L1_reg, L2_reg, sx, sy)
X = numpy.array(numpy.random.rand(num_samples, num_features), dtype=theano.config.floatX)
y = numpy.array(numpy.random.random_integers(0, num_outputs-1, num_samples), dtype='int32')
theirs = thl_out(X)
ours = hl.output(X)
assert numpy.allclose(theirs, ours, 0.005)
theirs = tmlp_p_y_given_x(X)
ours = mlp.logRegressionLayer.p_y_given_x(mlp.hiddenLayer.output(X))
assert numpy.allclose(theirs, ours, 0.0000001)
theirs = tmlp_neg_log(X, y)
ours = mlp.negative_log_likelihood(X, y)
assert numpy.allclose(theirs, ours, 0.0000001)
for j in range(10):
X = numpy.array(numpy.random.rand(num_samples, num_features), dtype=theano.config.floatX)
y = numpy.array(numpy.random.random_integers(0, num_outputs-1, num_samples), dtype='int32')
test_X = numpy.array(numpy.random.rand(num_samples, num_features), dtype=theano.config.floatX)
test_y = numpy.array(numpy.random.random_integers(0, num_outputs-1, num_samples), dtype='int32')
train_model(mlp, learning_rate, X, y, 0, num_samples, L1_reg, L2_reg)
tmlp_train_model(X, y)
print "Testing regression layer..."
theirs = tmlp.logRegressionLayer.W.get_value(borrow=True)
ours = mlp.logRegressionLayer.W
assert numpy.allclose(theirs, ours, 0.005)
theirs = tmlp.logRegressionLayer.b.get_value(borrow=True)
ours = mlp.logRegressionLayer.b
assert numpy.allclose(theirs, ours, 0.005)
print "Testing hidden layer..."
theirs = tmlp.hiddenLayer.W.get_value(borrow=True)
ours = mlp.hiddenLayer.W
assert numpy.allclose(theirs, ours, 0.005)
theirs = tmlp.hiddenLayer.b.get_value(borrow=True)
ours = mlp.hiddenLayer.b
assert numpy.allclose(theirs, ours, 0.005)
theirs = tmlp_test_model(test_X, test_y)
ours = test_model(mlp, test_X, test_y, 0, num_samples)
assert numpy.allclose(theirs, ours, 0.005)
def do_mnist():
num_features = 784
num_hidden = 500
num_outputs = 10
L1_reg = 0.00
L2_reg = 0.0001
num_epochs = 1000
learning_rate = 0.01
batch_size = 500
datasets = load_data('data/mnist.pkl.gz')
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.shape[0] / batch_size
n_valid_batches = valid_set_x.shape[0] / batch_size
n_test_batches = test_set_x.shape[0] / batch_size
mlp = MLP(
rng, n_in=num_features, n_hidden=num_hidden, n_out=num_outputs)
## Ensure that the theano MLP shares the same weights & biases as our MLP
#hW = theano.shared(value=numpy.copy(mlp.hiddenLayer.W), name='W', borrow=True)
#hb = theano.shared(value=numpy.copy(mlp.hiddenLayer.b), name='b', borrow=True)
tmlp = theano_MLP(
rng, sx, n_in=num_features, n_hidden=num_hidden, n_out=num_outputs)
tmlp_train_model, tmlp_test_model = theano_train_model(tmlp, learning_rate, L1_reg, L2_reg, sx, sy)
for epoch in range(num_epochs):
print "Epoch", epoch
for minibatch_index in xrange(n_train_batches):
print "Minibatch", minibatch_index
X = train_set_x[minibatch_index * batch_size:(minibatch_index + 1) * batch_size]
y = train_set_y[minibatch_index * batch_size:(minibatch_index + 1) * batch_size]
train_model(mlp, learning_rate, X, y, 0, len(X), L1_reg, L2_reg)
tmlp_train_model(X, y)
#print "Testing regression layer..."
#theirs = tmlp.logRegressionLayer.W.get_value(borrow=True)
#ours = mlp.logRegressionLayer.W
#print theirs
#print ours
#print format(numpy.sum(abs(theirs - ours)), 'f')
#assert numpy.allclose(theirs, ours, 0.005)
#theirs = tmlp.logRegressionLayer.b.get_value(borrow=True)
#ours = mlp.logRegressionLayer.b
#assert numpy.allclose(theirs, ours, 0.005)
#print "Testing hidden layer..."
#theirs = tmlp.hiddenLayer.W.get_value(borrow=True)
#ours = mlp.hiddenLayer.W
#assert numpy.allclose(theirs, ours, 0.005)
#theirs = tmlp.hiddenLayer.b.get_value(borrow=True)
#ours = mlp.hiddenLayer.b
#assert numpy.allclose(theirs, ours, 0.005)
print "Testing against test set..."
theirs = tmlp_test_model(test_set_x, test_set_y)
ours = test_model(
mlp, test_set_x, test_set_y, 0, test_set_x.shape[0])
print theirs
print ours
