def test_score_function_ftrl():
mlp = MLP(
epochs=100, eta=0.5, hidden_layers=[5], optimizer="ftrl", activations=["logistic"], minibatches=1, random_seed=1
)
mlp.fit(X, y)
acc = mlp.score(X, y)
assert acc == 1.0, acc
def test_score_function_adagrad():
mlp = MLP(epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer='adagrad',
activations=['logistic'],
minibatches=1,
random_seed=1)
mlp.fit(X, y)
acc = mlp.score(X, y)
assert acc == 1.0, acc
def test_score_function_adagrad():
mlp = MLP(epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer='adagrad',
activations=['logistic'],
minibatches=1,
random_seed=1)
mlp.fit(X, y)
acc = mlp.score(X, y)
assert acc == 1.0, acc
def test_multiclass_gd_dropout():
mlp = MLP(epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=1,
random_seed=1,
dropout=0.05)
mlp.fit(X, y)
acc = round(mlp.score(X, y), 2)
assert acc == 0.67, acc
def test_multiclass_gd_dropout():
mlp = MLP(epochs=100,
eta=0.5,
hidden_layers=[5],
optimizer='gradientdescent',
activations=['logistic'],
minibatches=1,
random_seed=1,
dropout=0.05)
mlp.fit(X, y)
acc = round(mlp.score(X, y), 2)
assert acc == 0.67, acc
print_progress=3,
minibatches=1,
random_seed=1)
nn1 = nn1.fit(X_std, y)
fig = plot_decision_regions(X=X_std, y=y, clf=nn1, legend=2)
plt.title('Multi-layer perception w. 1 hidden layer (logistic sigmod)')
plt.show()
plt.plot(range(len(nn1.cost_)), nn1.cost_)
plt.ylabel("Cost")
plt.xlabel("Epochs")
plt.show()
print 'Accuracy: %.2f%%' % (100 * nn1.score(X_std, y))
# Stochastic Gradient Descent
nn2 = TfMultiLayerPerceptron(eta=0.5,
epochs=20,
hidden_layers=[10],
activations=['logistic'],
optimizer='gradientdescent',
print_progress=3,
minibatches=len(y),
random_seed=1)
nn2.fit(X_std, y)
fig = plot_decision_regions(X=X_std, y=y, clf=nn2, legend=2)
