noise = 1,
).compile()
},
minibatch_size = minibatch_size,
test_epochs = sqrtspace(0, n_epochs, n_tests),
evaluation_function = percent_argmax_correct,
)
def run_and_plot(training_scheme):
learning_curves = training_scheme()
plot_learning_curves(learning_curves)
def get_experiments():
training_schemes = {
'adamax-showdown': mnist_adamax_showdown,
'mlp-normalization': mlp_normalization,
}
experiments = {name: lambda sc=scheme: run_and_plot(sc) for name, scheme in training_schemes.iteritems()}
return experiments
if __name__ == '__main__':
test_mode = False
experiment = 'adamax-showdown'
set_test_mode(test_mode)
run_experiment(experiment, exp_dict=get_experiments(), show_figs = None, print_to_console=True)
for exp_name, exp in get_experiments().iteritems():
print 'Running %s' % exp_name
exp()
def test_demo_mnist_mlp():
demo_mnist_mlp(test_mode = True)
def test_demo_dbn_mnist():
demo_dbn_mnist(plot = True, test_mode = True)
def test_demo_rbm_mnist():
demo_rbm_mnist(plot = True, test_mode = True)
def test_demo_prediction_example():
compare_example_predictors(test_mode = True)
if __name__ == '__main__':
set_test_mode(True)
test_demo_compare_optimizers()
test_demo_prediction_example()
test_demo_mnist_mlp()
test_demo_rbm_mnist()
test_demo_dbn_mnist()
function = MultiLayerPerceptron.from_init(
layer_sizes=[dataset.input_size, 500, dataset.n_categories],
hidden_activation='sig', # Sigmoidal hidden units
output_activation='softmax', # Softmax output unit, since we're doing multinomial classification
w_init = 0.01,
rng = 5
),
cost_function = negative_log_likelihood_dangerous, # "Dangerous" because it doesn't check to see that output is normalized, but we know it is because it comes from softmax.
optimizer = SimpleGradientDescent(eta = 0.1),
).compile(), # .compile() returns an IPredictor
},
offline_predictors={
'RF': RandomForestClassifier(n_estimators = 40)
},
minibatch_size = minibatch_size,
test_epochs = sqrtspace(0, n_epochs, n_tests),
evaluation_function = percent_argmax_correct # Compares one-hot
)
# Results is a LearningCurveData object
return learning_curve_data
if __name__ == '__main__':
set_test_mode(False)
records = compare_example_predictors(
n_epochs=30,
minibatch_size=20,
)
plot_learning_curves(records)
'normalize': make_mlp(normalize=True, scale = False),
'normalize and scale': make_mlp(normalize=True, scale = True),
},
minibatch_size = minibatch_size,
test_epochs = sqrtspace(0, n_epochs, n_tests),
evaluation_function = percent_argmax_correct
)
def run_and_plot(training_scheme):
learning_curves = training_scheme()
plot_learning_curves(learning_curves)
def get_experiments():
training_schemes = {
'adamax-showdown': mnist_adamax_showdown,
'mlp-normalization': mlp_normalization
}
experiments = {name: lambda sc=scheme: run_and_plot(sc) for name, scheme in training_schemes.iteritems()}
return experiments
if __name__ == '__main__':
test_mode = False
experiment = 'mlp-normalization'
set_test_mode(test_mode)
run_experiment(experiment, exp_dict=get_experiments(), show_figs = True, print_to_console=True)
def test_mnist_relu_vs_spiking():
set_test_mode(True)
ExperimentLibrary.mnist_relu_vs_spiking.run()
def test_try_hyperparams():
set_test_mode(True)
ExperimentLibrary.try_hyperparams.run()
for exp_name, exp in get_experiments().iteritems():
print 'Running %s' % exp_name
exp()
def test_demo_mnist_mlp():
demo_mnist_mlp(test_mode=True)
def test_demo_dbn_mnist():
demo_dbn_mnist(plot=True, test_mode=True)
def test_demo_rbm_mnist():
demo_rbm_mnist(plot=True, test_mode=True)
def test_demo_prediction_example():
compare_example_predictors(test_mode=True)
if __name__ == '__main__':
set_test_mode(True)
test_demo_compare_optimizers()
test_demo_prediction_example()
test_demo_mnist_mlp()
test_demo_rbm_mnist()
test_demo_dbn_mnist()
