ada = Adadelta(lr=0.1, rho=0.95, epsilon=1e-08)
model.compile(loss='categorical_crossentropy',
optimizer=ada,
metrics=['accuracy'])
model.summary()
return model
img_rows, img_cols = 48, 48
batch_size = 128
nb_classes = 7
nb_epoch = 1200
img_channels = 1
Train_x, Train_y, Val_x, Val_y = dataprocessing.load_data()
Train_x = numpy.asarray(Train_x)
Train_x = Train_x.reshape(Train_x.shape[0], img_rows, img_cols)
Val_x = numpy.asarray(Val_x)
Val_x = Val_x.reshape(Val_x.shape[0], img_rows, img_cols)
Train_x = Train_x.reshape(Train_x.shape[0], 1, img_rows, img_cols)
Val_x = Val_x.reshape(Val_x.shape[0], 1, img_rows, img_cols)
Train_x = Train_x.astype('float32')
Val_x = Val_x.astype('float32')
Train_y = np_utils.to_categorical(Train_y, nb_classes)
Val_y = np_utils.to_categorical(Val_y, nb_classes)
import linear_regression as rg
import dataprocessing as dp
import numpy as np
import matplotlib.pyplot as plt
# Loading training examples
X = dp.load_data('linearX.csv')
y = dp.load_data('linearY.csv')
X = X.reshape((-1, 1))
#Normalizing the training examples
X, meu, sigma = dp.normalize(X)
#Initialising parameters for gradient descent
m, n = X.shape
init_theta = np.zeros(n + 1)
epsilon = 1e-10
eta = [0.001, 0.005, 0.009, 0.013, 0.017]
color = ['red', 'blue', 'green', 'yellow', 'magenta']
plt.ion()
for i in range(len(eta)):
# Executing gradient descent
theta, iterations, theta_history, cost_history = rg.linear_reg(
X, y, init_theta, eta[i], epsilon)
plt.plot(list(range(0, iterations + 1)),
cost_history,
color=color[i],
label='eta = ' + str(eta[i]))
import logistic_regression as log_rg
import dataprocessing as dp
import numpy as np
# Loading training examples
X = dp.load_data('logisticX.csv')
y = dp.load_data('logisticY.csv', dtype='int')
#Normalizing the training examples
X, meu, sigma = dp.normalize(X)
m, n = X.shape
init_theta = np.zeros(n + 1)
epsilon = 1e-10
theta, iterations = log_rg.newton(X, y, init_theta, epsilon)
# Plotting the data and hypothesis
dp.plot_classification_data(X, y, ['Negative', 'Positive'])
print('No. of iterations = ', iterations)
print('Theta = ', theta)
input('Press Enter to draw hypothesis')
#Plotting decision boundary
x_test = np.linspace(np.amin(X[:, 0]) - 1, np.amax(X[:, 0]) + 1, num=500)
dp.plot_linear_decision_boundary(x_test, theta)
input('Press Enter to close')
dp.plot_close()
model.add(Activation('softmax'))
ada = Adadelta(lr=0.1, rho=0.95, epsilon=1e-08)
model.compile(loss='categorical_crossentropy',
optimizer=ada,
metrics=['accuracy'])
model.summary()
return model
img_rows, img_cols = 48, 48
batch_size = 128
nb_classes = 7
nb_epoch = 1200
img_channels = 1
Train_x, Train_y, Val_x, Val_y = dataprocessing.load_data()
Train_x = numpy.asarray(Train_x)
Train_x = Train_x.reshape(Train_x.shape[0],img_rows,img_cols)
Val_x = numpy.asarray(Val_x)
Val_x = Val_x.reshape(Val_x.shape[0],img_rows,img_cols)
Train_x = Train_x.reshape(Train_x.shape[0], 1, img_rows, img_cols)
Val_x = Val_x.reshape(Val_x.shape[0], 1, img_rows, img_cols)
Train_x = Train_x.astype('float32')
Val_x = Val_x.astype('float32')
Train_y = np_utils.to_categorical(Train_y, nb_classes)