import graphviz
pydot.find_graphviz = lambda: True
x, y = make_classification(n_samples=100,
n_informative=2,
n_features=2,
n_redundant=0,
n_clusters_per_class=1,
random_state=7)
y
model = Sequential()
model.add(Dense(units=1, input_shape=(2, ), activation='sigmoid'))
#sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
#model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.compile(optimizer='sgd',
loss='binary_crossentropy',
metrics=['accuracy'])
history = model.fit(x=x, y=y, verbose=0, epochs=200)
print(model.get_weights())
print(model.summary())
plot_loss_accuracy(history)
plot_decision_boundary(lambda x: model.predict(x), x, y)
y_pred = model.predict_classes(x, verbose=0)
plot_confusion_matrix(model, x, y)
from keras.optimizers import Adam
from keras.utils import plot_model
X, y = make_circles(n_samples=1000, noise=0.05, factor=0.3, random_state=0)
#X, y = make_moons(n_samples=1000, noise=0.05, random_state=0)
plot_data(X, y)
#single perceptron model for binary classifcation
model1 = Sequential()
model1.add(Dense(1, input_shape=(2, ), activation='sigmoid'))
model1.compile('adam', 'binary_crossentropy', metrics=['accuracy'])
plot_model(model1, show_shapes=True, to_file='model1.png')
history1 = model1.fit(X, y, verbose=0, epochs=100)
plot_loss_accuracy(history1)
plot_decision_boundary(lambda x: model1.predict(x), X, y)
y_pred = model1.predict_classes(X, verbose=0)
plot_confusion_matrix(model1, X, y)
#mlp model for binary classification
model2 = Sequential()
model2.add(Dense(4, input_shape=(2, ), activation='tanh'))
model2.add(Dense(2, activation='tanh'))
model2.add(Dense(1, activation='sigmoid'))
model2.compile(Adam(lr=0.01), 'binary_crossentropy', metrics=['accuracy'])
plot_model(model2, show_shapes=True, to_file='model2.png')
history2 = model2.fit(X, y, verbose=0, epochs=50)