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
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(Conv2D(128, filter_shape=(3, 3), stride=2, padding='same'))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.25))
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(128))
model.add(Activation('leaky_relu'))
model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))
return model
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(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 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
# 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(n_iterations=300,
optimizer=Adam(),
loss=CrossEntropy,
batch_size=50)
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)
# ........
# TRAIN
def main():
data = datasets.load_digits()
X = data.data
y = data.target
n_samples = np.shape(X)
n_hidden = 512
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=1)
optimizer = Adam()
#-----
# MLP
#-----
# clf = NeuralNetwork(optimizer=optimizer,
# loss=CrossEntropy,
# validation_data=(X_test, y_test))
# clf.add(Dense(n_hidden, input_shape=(8*8,)))
# 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")
# clf.fit(X_train, y_train, n_epochs=50, batch_size=256)
# clf.plot_errors()
# y_pred = np.argmax(clf.predict(X_test), axis=1)
# accuracy = accuracy_score(y_test, y_pred)
# print ("Accuracy:", accuracy)
#----------
# Conv Net
#----------
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),
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), 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(0.5))
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()
# Make a prediction of the test set
y_pred = np.argmax(clf.predict(X_test), axis=1)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
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=np.unique(y))
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 = np.shape(X)
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=(8*8,)))
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()
# Predict labels of the test data
y_pred = np.argmax(clf.predict(X_test), axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = accuracy_score(y_test, y_pred)
print ("Accuracy:", accuracy)
# Reduce dimension to 2D using PCA and plot the results
Plot().plot_in_2d(X_test, y_pred, title="Multilayer Perceptron", accuracy=accuracy, legend_labels=range(10))
def main():
data = datasets.load_digits()
X = data.data
y = data.target
n_samples = np.shape(X)
n_hidden = 512
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
seed=1)
optimizer = Adam()
#-----
# MLP
#-----
# clf = NeuralNetwork(optimizer=optimizer,
# loss=CrossEntropy,
# validation_data=(X_test, y_test))
# clf.add(Dense(n_hidden, input_shape=(8*8,)))
# 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'))
# clf.fit(X_train, y_train, n_iterations=50, batch_size=256)
# clf.plot_errors()
# y_pred = np.argmax(clf.predict(X_test), axis=1)
# accuracy = accuracy_score(y_test, y_pred)
# print ("Accuracy:", accuracy)
#----------
# Conv Net
#----------
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),
input_shape=(1, 8, 8),
padding='same'))
clf.add(Activation('relu'))
clf.add(Conv2D(n_filters=32, filter_shape=(3, 3), padding='same'))
clf.add(Activation('relu'))
clf.add(Flatten())
clf.add(Dense(128))
clf.add(Activation('relu'))
clf.add(Dense(10))
clf.add(Activation('softmax'))
clf.fit(X_train, y_train, n_iterations=50, batch_size=256)
clf.plot_errors()
y_pred = np.argmax(clf.predict(X_test), axis=1)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
X_test = X_test.reshape(-1, 8 * 8)
# Reduce dimension to two using PCA and plot the results
Plot().plot_in_2d(X_test,
y_pred,
title="Convolutional Neural Network",
accuracy=accuracy,
legend_labels=np.unique(y))
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"))
n_samples = np.shape(X)
n_hidden = 512
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),
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), 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(0.5))
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()
# Predict labels of the test data
y_pred = np.argmax(clf.predict(X_test), axis=1)
y_test = np.argmax(y_test, axis=1)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
# Flatten data set
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))