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mlp_main.py
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mlp_main.py
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from utils import fio
import matplotlib.pyplot as plt
from mlp.neural_network import MLPClassifier # Using copies of dev branch from sklearn, since these classes are not yet released.
from sklearn.preprocessing import MinMaxScaler
# different learning rate schedules and momentum parameters
#params = [{'algorithm': 'sgd', 'learning_rate': 'constant', 'momentum': 0, 'learning_rate_init': 0.2},
# {'algorithm': 'sgd', 'learning_rate': 'constant', 'momentum': .9, 'nesterovs_momentum': False, 'learning_rate_init': 0.2},
# {'algorithm': 'sgd', 'learning_rate': 'constant', 'momentum': .9, 'nesterovs_momentum': True, 'learning_rate_init': 0.2},
# {'algorithm': 'sgd', 'learning_rate': 'invscaling', 'momentum': 0, 'learning_rate_init': 0.2},
# {'algorithm': 'sgd', 'learning_rate': 'invscaling', 'momentum': .9, 'nesterovs_momentum': True, 'learning_rate_init': 0.2},
# {'algorithm': 'sgd', 'learning_rate': 'invscaling', 'momentum': .9, 'nesterovs_momentum': False, 'learning_rate_init': 0.2},
# {'algorithm': 'adam'}]
#
#labels = ["constant learning-rate",
# "constant with momentum",
# "constant with Nesterov's momentum",
# "inv-scaling learning-rate",
# "inv-scaling with momentum",
# "inv-scaling with Nesterov's momentum",
# "adam"]
params = [{'algorithm': 'sgd', 'learning_rate_init': 0.1, 'hidden_layer_sizes': (30,), 'max_iter': 20, 'tol': -1},
{'algorithm': 'sgd', 'learning_rate_init': 0.1, 'hidden_layer_sizes': (60,), 'max_iter': 20},
{'algorithm': 'sgd', 'learning_rate_init': 0.1, 'hidden_layer_sizes': (100,), 'max_iter': 20},
{'algorithm': 'sgd', 'learning_rate_init': 0.2, 'hidden_layer_sizes': (30,), 'max_iter': 20},
{'algorithm': 'sgd', 'learning_rate_init': 0.2, 'hidden_layer_sizes': (60,), 'max_iter': 20},
{'algorithm': 'sgd', 'learning_rate_init': 0.2, 'hidden_layer_sizes': (100,), 'max_iter': 20},
{'algorithm': 'sgd', 'learning_rate_init': 0.3, 'hidden_layer_sizes': (100,), 'max_iter': 20}]
labels = ["lr:0.1, neurons:30",
"lr:0.1, neurons:60",
"lr:0.1, neurons:100",
"lr:0.2, neurons:30",
"lr:0.2, neurons:60",
"lr:0.2, neurons:100",
"lr:0.3, neurons:100"]
plot_args = [{'c': 'red', 'linestyle': '-'},
{'c': 'green', 'linestyle': '-'},
{'c': 'blue', 'linestyle': '-'},
{'c': 'red', 'linestyle': '--'},
{'c': 'green', 'linestyle': '--'},
{'c': 'blue', 'linestyle': '--'},
{'c': 'black', 'linestyle': '-'}]
def plot_on_dataset(X, y, ax, name):
# for each dataset, plot learning for each learning strategy
print("\nlearning on dataset %s" % name)
ax.set_title(name)
X = MinMaxScaler().fit_transform(X)
mlps = []
for label, param in zip(labels, params):
print("training: %s" % label)
mlp = MLPClassifier(verbose=0, random_state=0, **param)
mlp.fit(X, y)
mlps.append(mlp)
print("Training set score: %f" % mlp.score(X, y))
print("Training set loss: %f" % mlp.loss_)
for mlp, label, args in zip(mlps, labels, plot_args):
ax.plot(mlp.loss_curve_, label=label, **args)
def export_predictions(predictions):
config = fio.get_config()
file_path = "./evaluation/mnist_mlp_result.csv"
fio.export_csv_data(file_path, predictions)
def main():
config = fio.get_config()
# print("Config sections: %s" % config.sections())
# Load train set.
csv_train_set_data = fio.import_csv_data(fio.get_absolute_path(config.get('MNIST', 'trainingset')))
#print("CSV train data length: %i" % len(csv_train_set_data))
#train_set_sample_data = fio.get_random_data_sample(csv_train_set_data, 2699) # Just load 10% random data while developing.
train_set_lables, train_set_data = fio.split_labels_data(csv_train_set_data, 0)
# Rescale.
train_set_data = train_set_data / 255.
print("Train data length: %i" % len(train_set_data))
# Load test set.
csv_test_set_data = fio.import_csv_data(fio.get_absolute_path(config.get('MNIST', 'testset')))
print("Test data length: %i" % len(csv_test_set_data))
#test_set_sample_data = fio.get_random_data_sample(csv_test_set_data, 1501) # Just load 10% random data while developing.
test_set_lables, test_set_data = fio.split_labels_data(csv_test_set_data, 0)
# Rescale.
test_set_data = test_set_data / 255.
## mlp = MLPClassifier(hidden_layer_sizes=(100, 100), max_iter=400, alpha=1e-4,
## algorithm='sgd', verbose=10, tol=1e-4, random_state=1)
mlp = MLPClassifier(hidden_layer_sizes=(len(train_set_data) * 0.1,), max_iter=30, alpha=1e-4,
algorithm='sgd', verbose=10, tol=1e-4, random_state=1,
learning_rate_init=.1)
X = MinMaxScaler().fit_transform(train_set_data)
mlp.fit(X, train_set_lables)
print("Training set score: %f" % mlp.score(X, train_set_lables))
print("Training set loss: %f" % mlp.loss_)
print("Test set score: %f" % mlp.score(test_set_data, test_set_lables))
# Load evaluation set.
evaluation_set_data = fio.import_csv_data(fio.get_absolute_path(config.get('Evaluation.SVM', 'mnist')))
print("Evaluation data length: %i" % len(evaluation_set_data))
# Rescale.
evaluation_set_data = evaluation_set_data / 255.
predictions = mlp.predict(evaluation_set_data)
export_predictions(predictions)
#fig, axes = plt.subplots(3, 3)
## use global min / max to ensure all weights are shown on the same scale
#vmin, vmax = mlp.coefs_[0].min(), mlp.coefs_[0].max()
#for coef, ax in zip(mlp.coefs_[0].T, axes.ravel()):
# ax.matshow(coef.reshape(28, 28), cmap=plt.cm.gray, vmin=.5 * vmin,
# vmax=.5 * vmax)
# ax.set_xticks(())
# ax.set_yticks(())
#plt.show()
#fig = plt.figure()
#ax = fig.add_subplot(1, 1, 1)
#plot_on_dataset(train_set_data, train_set_lables, ax=ax, name="mnist")
#fig.legend(ax.get_lines(), labels=labels, ncol=3, loc="upper center")
#plt.show()
# Program entry point. Don't execute if imported.
if __name__ == '__main__':
main()