X = np.asarray([[6.1,1.4],[7.7,2.3],[6.3,2.4],[6.4,1.8],[6.2,1.8],[6.9,2.1],
[6.7,2.4],[6.9,2.3],[5.8,1.9],[6.8,2.3],[6.7,2.5],[6.7,2.3],[6.3,1.9],[6.5,2.1 ],[6.2,2.3],[5.9,1.8]] )
X = (X - X.mean(axis=0)) / X.std(axis=0)
y = np.asarray([0,2,2,1,2,2,2,2,2,2,2,2,2,2,2,2])
nn = MLP(hidden_layers=[50],l2=0.00,l1=0.0,epochs=150,eta=0.05,
momentum=0.1,decrease_const=0.0,minibatches=1,random_seed=1,print_progress=3)
nn = nn.fit(X, y)
fig = plot_decision_regions(X=X, y=y, clf=nn, legend=2)
plt.show()
print('Accuracy(epochs = 150): %.2f%%' % (100 * nn.score(X, y)))
nn.epochs = 250
nn = nn.fit(X, y)
fig = plot_decision_regions(X=X, y=y, clf=nn, legend=2)
plt.title('epochs = 250')
plt.show()
print('Accuracy(epochs = 250): %.2f%%' % (100 * nn.score(X, y)))
plt.plot(range(len(nn.cost_)), nn.cost_)
plt.title('Gradient Descent training (minibatches=1)')
plt.xlabel('Epochs')
plt.ylabel('Cost')
plt.show()
nn.minibatches = len(y)
nn = nn.fit(X, y)
plt.plot(range(len(nn.cost_)), nn.cost_)
momentum=0.1,
decrease_const=0.0,
minibatches=len(y),
random_seed=1,
print_progress=3)
nn2.fit(X_std, y)
plt.plot(range(len(nn2.cost_)), nn2.cost_)
plt.ylabel('Cost')
plt.xlabel('Epochs')
plt.show()
# In[46]:
nn2.epochs = 25
nn2 = nn2.fit(X_std, y)
# In[47]:
plt.plot(range(len(nn2.cost_)), nn2.cost_)
plt.ylabel('Cost')
plt.xlabel('Epochs')
plt.show()
# ## Example 2 - Classifying Handwritten Digits from a 10% MNIST Subset
# In[53]:
from mlxtend.data import mnist_data
from mlxtend.preprocessing import shuffle_arrays_unison
