def build_discriminator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(
Conv2D(32,
filter_shape=(3, 3),
stride=2,
input_shape=self.img_shape,
padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(Conv2D(64, filter_shape=(3, 3), stride=2, padding='same'))
model.add(ZeroPadding2D(padding=((0, 1), (0, 1))))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, filter_shape=(3, 3), stride=2, padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(256, filter_shape=(3, 3), stride=1, padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(2))
model.add(Activation('softmax'))
return model
def main():
optimizer = Adam()
#-----
# MLP
#-----
data = datasets.load_digits()
X = data.data
y = data.target
# Convert to one-hot encoding
y = to_categorical(y.astype("int"))
n_samples, n_features = X.shape
n_hidden = 512
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=1)
clf = NeuralNetwork(optimizer=optimizer,
loss=CrossEntropy,
validation_data=(X_test, y_test))
clf.add(Dense(n_hidden, input_shape=(n_features,)))
clf.add(Activation('leaky_relu'))
clf.add(Dense(n_hidden))
clf.add(Activation('leaky_relu'))
clf.add(Dropout(0.25))
clf.add(Dense(n_hidden))
clf.add(Activation('leaky_relu'))
clf.add(Dropout(0.25))
clf.add(Dense(n_hidden))
clf.add(Activation('leaky_relu'))
clf.add(Dropout(0.25))
clf.add(Dense(10))
clf.add(Activation('softmax'))
print ()
clf.summary(name="MLP")
train_err, val_err = clf.fit(X_train, y_train, n_epochs=50, batch_size=256)
# Training and validation error plot
n = len(train_err)
training, = plt.plot(range(n), train_err, label="Training Error")
validation, = plt.plot(range(n), val_err, label="Validation Error")
plt.legend(handles=[training, validation])
plt.title("Error Plot")
plt.ylabel('Error')
plt.xlabel('Iterations')
plt.show()
_, accuracy = clf.test_on_batch(X_test, y_test)
print ("Accuracy:", accuracy)
# Reduce dimension to 2D using PCA and plot the results
y_pred = np.argmax(clf.predict(X_test), axis=1)
Plot().plot_in_2d(X_test, y_pred, title="Multilayer Perceptron", accuracy=accuracy, legend_labels=range(10))
def model_builder(n_inputs, n_outputs):
model = NeuralNetwork(optimizer=Adam(), loss=CrossEntropy)
model.add(Dense(16, input_shape=(n_inputs, )))
model.add(Activation('relu'))
model.add(Dense(n_outputs))
model.add(Activation('softmax'))
return model
def build_encoder(self, optimizer, loss_function):
encoder = NeuralNetwork(optimizer=optimizer, loss=loss_function)
encoder.add(Dense(512, input_shape=(self.img_dim, )))
encoder.add(Activation('leaky_relu'))
encoder.add(BatchNormalization(momentum=0.8))
encoder.add(Dense(256))
encoder.add(Activation('leaky_relu'))
encoder.add(BatchNormalization(momentum=0.8))
encoder.add(Dense(self.latent_dim))
return encoder
def build_discriminator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(Dense(512, input_shape=(self.img_dim,)))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.5))
model.add(Dense(256))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))
return model
def build_generator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(Dense(128 * 7 * 7, input_shape=(100, )))
model.add(Activation('leaky_relu'))
model.add(Reshape((128, 7, 7)))
model.add(UpSampling2D())
model.add(Conv2D(128, filter_shape=(3, 3), padding='same'))
model.add(Activation("leaky_relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, filter_shape=(3, 3), padding='same'))
model.add(Activation("leaky_relu"))
model.add(Conv2D(1, filter_shape=(3, 3), padding='same'))
model.add(Activation("tanh"))
return model
def build_generator(self, optimizer, loss_function):
model = NeuralNetwork(optimizer=optimizer, loss=loss_function)
model.add(Dense(256, input_shape=(self.latent_dim,)))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(512))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(1024))
model.add(Activation('leaky_relu'))
model.add(BatchNormalization(momentum=0.8))
model.add(Dense(self.img_dim))
model.add(Activation('tanh'))
return model
def main():
# define the model
components = 3
optimizer = Adam()
loss = MdnLoss(num_components=components, output_dim=1)
clf = NeuralNetwork(optimizer=optimizer, loss=loss)
clf.add(Dense(n_units=26, input_shape=(1, )))
clf.add(Activation('tanh'))
clf.add(
MDN(input_shape=(26, ), output_shape=(1, ), num_components=components))
clf.summary(name="MDN")
# generate 1D regression data (Bishop book, page 273).
# Note: P(y|x) is not a nice distribution.
# (e.g.) it has three modes for x ~= 0.5
N = 225
X = np.linspace(0, 1, N)
Y = X + 0.3 * np.sin(2 * 3.1415926 * X) + np.random.uniform(-0.1, 0.1, N)
X, Y = Y, X
nb = N # full_batch
xbatch = np.reshape(X[:nb], (nb, 1))
ybatch = np.reshape(Y[:nb], (nb, 1))
train_err, val_err = clf.fit(xbatch,
ybatch,
n_epochs=int(4e3),
batch_size=N)
plt.plot(train_err, label="Training Error")
plt.title("Error Plot")
plt.ylabel('Error')
plt.xlabel('Iterations')
plt.show()
# utility function for creating contour plot of the predictions
n = 15
xx = np.linspace(0, 1, n)
yy = np.linspace(0, 1, n)
xm, ym = np.meshgrid(xx, yy)
loss, acc = clf.test_on_batch(xm.reshape(xm.size, 1),
ym.reshape(ym.size, 1))
ypred = clf.loss_function.ypred
plt.figure(figsize=(10, 10))
plt.scatter(X, Y, color='g')
plt.contour(xm,
ym,
np.reshape(ypred, (n, n)),
levels=np.linspace(ypred.min(), ypred.max(), 20))
plt.xlabel('x')
plt.ylabel('y')
plt.title('{}-component Gaussian Mixture Model for '
'P(y|x)'.format(components))
plt.show()
# ..........................
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Rescaled labels {-1, 1}
rescaled_y_train = 2 * y_train - np.ones(np.shape(y_train))
rescaled_y_test = 2 * y_test - np.ones(np.shape(y_test))
# .......
# SETUP
# .......
adaboost = Adaboost(n_clf=8)
naive_bayes = NaiveBayes()
knn = KNN(k=4)
logistic_regression = LogisticRegression()
mlp = NeuralNetwork(optimizer=Adam(), loss=CrossEntropy)
mlp.add(Dense(input_shape=(n_features, ), n_units=64))
mlp.add(Activation('relu'))
mlp.add(Dense(n_units=64))
mlp.add(Activation('relu'))
mlp.add(Dense(n_units=2))
mlp.add(Activation('softmax'))
perceptron = Perceptron()
decision_tree = ClassificationTree()
random_forest = RandomForest(n_estimators=50)
support_vector_machine = SupportVectorMachine()
lda = LDA()
gbc = GradientBoostingClassifier(n_estimators=50,
learning_rate=.9,
max_depth=2)
xgboost = XGBoost(n_estimators=50, learning_rate=0.5)
# ........
def main():
optimizer = Adam()
def gen_mult_ser(nums):
""" Method which generates multiplication series """
X = np.zeros([nums, 10, 61], dtype=float)
y = np.zeros([nums, 10, 61], dtype=float)
for i in range(nums):
start = np.random.randint(2, 7)
mult_ser = np.linspace(start, start * 10, num=10, dtype=int)
X[i] = to_categorical(mult_ser, n_col=61)
y[i] = np.roll(X[i], -1, axis=0)
y[:, -1, 1] = 1 # Mark endpoint as 1
return X, y
def gen_num_seq(nums):
""" Method which generates sequence of numbers """
X = np.zeros([nums, 10, 20], dtype=float)
y = np.zeros([nums, 10, 20], dtype=float)
for i in range(nums):
start = np.random.randint(0, 10)
num_seq = np.arange(start, start + 10)
X[i] = to_categorical(num_seq, n_col=20)
y[i] = np.roll(X[i], -1, axis=0)
y[:, -1, 1] = 1 # Mark endpoint as 1
return X, y
X, y = gen_mult_ser(3000)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
# Model definition
clf = NeuralNetwork(optimizer=optimizer,
loss=CrossEntropy)
clf.add(RNN(10, activation="tanh", bptt_trunc=5, input_shape=(10, 61)))
clf.add(Activation('softmax'))
clf.summary("RNN")
# Print a problem instance and the correct solution
tmp_X = np.argmax(X_train[0], axis=1)
tmp_y = np.argmax(y_train[0], axis=1)
print("Number Series Problem:")
print("X = [" + " ".join(tmp_X.astype("str")) + "]")
print("y = [" + " ".join(tmp_y.astype("str")) + "]")
print()
train_err, _ = clf.fit(X_train, y_train, n_epochs=500, batch_size=512)
# Predict labels of the test data
y_pred = np.argmax(clf.predict(X_test), axis=2)
y_test = np.argmax(y_test, axis=2)
print()
print("Results:")
for i in range(5):
# Print a problem instance and the correct solution
tmp_X = np.argmax(X_test[i], axis=1)
tmp_y1 = y_test[i]
tmp_y2 = y_pred[i]
print("X = [" + " ".join(tmp_X.astype("str")) + "]")
print("y_true = [" + " ".join(tmp_y1.astype("str")) + "]")
print("y_pred = [" + " ".join(tmp_y2.astype("str")) + "]")
print()
accuracy = np.mean(accuracy_score(y_test, y_pred))
print("Accuracy:", accuracy)
training = plt.plot(range(500), train_err, label="Training Error")
plt.title("Error Plot")
plt.ylabel('Training Error')
plt.xlabel('Iterations')
plt.show()
def main():
#----------
# Conv Net
#----------
optimizer = Adam()
data = datasets.load_digits()
X = data.data
y = data.target
# Convert to one-hot encoding
y = to_categorical(y.astype("int"))
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=1)
# Reshape X to (n_samples, channels, height, width)
X_train = X_train.reshape((-1, 1, 8, 8))
X_test = X_test.reshape((-1, 1, 8, 8))
clf = NeuralNetwork(optimizer=optimizer,
loss=CrossEntropy,
validation_data=(X_test, y_test))
clf.add(
Conv2D(n_filters=16,
filter_shape=(3, 3),
stride=1,
input_shape=(1, 8, 8),
padding='same'))
clf.add(Activation('relu'))
clf.add(Dropout(0.25))
clf.add(BatchNormalization())
clf.add(Conv2D(n_filters=32, filter_shape=(3, 3), stride=1,
padding='same'))
clf.add(Activation('relu'))
clf.add(Dropout(0.25))
clf.add(BatchNormalization())
clf.add(Flatten())
clf.add(Dense(256))
clf.add(Activation('relu'))
clf.add(Dropout(0.4))
clf.add(BatchNormalization())
clf.add(Dense(10))
clf.add(Activation('softmax'))
print()
clf.summary(name="ConvNet")
train_err, val_err = clf.fit(X_train, y_train, n_epochs=50, batch_size=256)
# Training and validation error plot
n = len(train_err)
training, = plt.plot(range(n), train_err, label="Training Error")
validation, = plt.plot(range(n), val_err, label="Validation Error")
plt.legend(handles=[training, validation])
plt.title("Error Plot")
plt.ylabel('Error')
plt.xlabel('Iterations')
plt.show()
_, accuracy = clf.test_on_batch(X_test, y_test)
print("Accuracy:", accuracy)
y_pred = np.argmax(clf.predict(X_test), axis=1)
X_test = X_test.reshape(-1, 8 * 8)
# Reduce dimension to 2D using PCA and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="Convolutional Neural Network",
accuracy=accuracy,
legend_labels=range(10))
def model(n_inputs, n_outputs):
clf = NeuralNetwork(optimizer=Adam(), loss=SquareLoss)
clf.add(Dense(64, input_shape=(n_inputs, )))
clf.add(Activation('relu'))
clf.add(Dense(n_outputs))
return clf
