import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from loadData import loaddata
from plotData import plotData
data = loaddata('ex2data1.txt', ',')
X = np.c_[np.ones(
(data.shape[0], 1)
), data[:, 0:
2]] # adds column of ones (first argument) to X data (second argument) AND creates X from data at the same time
y = np.c_[data[:, 2]] # creates Y from data
plotData(data, 'Exam 1 score', 'Exam 2 score', 'Admitted',
'Not admitted') #plots the data
plt.show()
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.1, random_state=0
) #split X and y data into training and testing set. Model will train on train set and will check accuracy on test set
logreg = LogisticRegression() #Initializing the model
logreg.fit(X_train, y_train) #Feeding values in the model
print('Theta ', logreg.coef_) #theta value after the model is trained
y_pred = logreg.predict(X_test) # generating the predictions on the test set
print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(
logreg.score(X_test, y_test)))
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from plotData import plotData
from loadData import loaddata
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import metrics
from predict import predict
data = loaddata(
'C:\\Users\\Ananya\\Desktop\\ML-Exercises-\\Week3\\ex2data1.txt', ',')
X = np.c_[np.ones(
(data.shape[0], 1)
), data[:, 0:
2]] # adds column of ones (first argument) to X data (second argument) AND creates X from data at the same time
y = np.c_[data[:, 2]] # creates Y from data
plotData(data, 'Exam 1 score', 'Exam 2 score', 'Admitted',
'Not admitted') #plots the data
plt.show()
input(
"proceed only if you've completed completed ALL the files, else press Ctrl+C then enter to exit the program"
)
from costFunction import costFunction, gradient
initial_theta = np.zeros(X.shape[1])
cost = costFunction(initial_theta, X, y)
grad = gradient(initial_theta, X, y)
print('Cost: \n', cost)
print('Grad: \n', grad)
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from sigmoid import sigmoid
from plotData import plotData
from predict import predict
from loadData import loaddata
from sklearn.preprocessing import PolynomialFeatures
from costFunctionReg import costFunctionReg, gradientReg
data2 = loaddata('ex2data2.txt', ',')
y = np.c_[data2[:, 2]]
X = data2[:, 0:2]
''' Since we see that the data cannot be separated linearly, we shall
create more features to let the logistic regressor design a more complex boundary which will fit the data.
We use a technique called feature mapping as shown below. However feature mapping makes the data prone to overfitting. '''
poly = PolynomialFeatures(6)
XX = poly.fit_transform(
data2[:, 0:2]
) # create more features from data as terms of X1 and X2 upto the sixth power, called feature mapping, helps us fit the data better
initial_theta = np.zeros(XX.shape[1])
print(costFunctionReg(initial_theta, 1, XX, y))
fig, axes = plt.subplots(1, 3, sharey=True, figsize=(17, 5))
# Decision boundaries
# Lambda = 0 : No regularization --> too flexible, overfitting the training data
# Lambda = 1 : Looks about right
# Lambda = 100 : Too much regularization --> high bias
#img = resize_image(img, 224, 224)
def pil_to_nparray(pil_image):
""" Convert a PIL.Image to numpy array. """
pil_image.load()
return np.asarray(pil_image, dtype="float32")
#img = pil_to_nparray(img)
print(u'用于测试的图片加载完成！')
# Data loading and preprocessing
import tflearn.datasets.oxflower17 as oxflower17
(coll, collmask, testcoll, testcollmask) = loaddata()
#print('------')
#print('666666666666')
#X, Y = oxflower17.load_data(one_hot=True)
# Building ‘VGG Network‘以下为模型的加载，其中3是卷积核的大小即3*3.64/128/256/512是卷积核的个数
network = input_data(shape=[None, 224, 224, 1])
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
network = conv_2d(network, 128, 3, activation='relu')
network = conv_2d(network, 128, 3, activation='relu')
network = max_pool_2d(network, 2, strides=2)
