Data Analysis

写在前面
该篇主要讲述的是数据分析的代码实现：
包含：
Linear Regression
Logistic_Regression
Support Vector Machine
Convolution Neural Network

Linear Regression

import tensorflow as tf
import numpy as npdef read_data():"""读取数据"""passX = tf.placeholder(tf.float32, shape=[3,1], name="X")
Y = tf.placeholder(tf.float32, name="Y")w = tf.Variable(tf.random_normal(shape[3,1], dtype=tf.float32), name="weight")
b = tf.Variable(.0, dtype=tf.float32, name="bias")y_predict = tf.matmul(X, w) + b
loss = tf.square(Y - Y_predicted, name='loss')
optimizer = tf.train.GradientDescentOptimizer(learning_rate=.001).minimize(loss)with tf.Session() as sess:sess.run(tf.global_variables_initializer()) for i in range(epochs):total_loss = 0for j in range(len(data)):"""read data"""x_ = read_x_data()y_ = read_y_data()"""add some functions to get the precise data of x and y"""_, loss_ = sess.run([optimizer, loss], feed_dict={X:x_, Y:y_})total_loss += loss_w_, b_ = sess.run([w,b])

Logits Regression

import padas # mabey useful
import tensorflow as tf
import numpy as npcsv_path = "./data/haberman.csv"
data = pandas.read_csv(csv_path).to_numpy()X = tf.placeholder(tf.float32, shape=[1,3], name="X")
Y = tf.placeholder(tf.float32, name="Y")mizew = tf.Variable(tf.random_normal(shape=[3,1], dtype=tf.float32), name="weight")
b = tf.Variable(.0, dtype=tf.float32 ,name="bias")logits = tf.matmul(X , w)+ b
entropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=Y, name="sigmoid")
loss = tf.reduce_mean(entropy)optimizer = tf.train.AdamOptimizer(learning_rate=.01).minimize(loss)#Train
with tf.Session() as sess:sess.run(tf.global_variables_initializer())epoches = 10for n in range(epoches):total_loss = 0for i in range(int(len(data) * .8)):x_data = np.array(data[i][:3]).reshape((1, 3))y_data = float(data[i][3:] - 1)# print([x_data, y_data])_, loss_ = sess.run([optimizer, loss], feed_dict={X: x_data, Y: y_data})total_loss += loss_print("total loss : {0}".format(total_loss/ int(len(data) * .8)))w, b = sess.run([w, b])print([w,b])# Test
with tf.Session() as sess:print("start test!")Accurency = 0for i in range(int(len(data)*.8), len(data)):x_data = np.array(data[i][:3]).reshape((1, 3))y_data = float(data[i][3:] - 1)value = tf.nn.sigmoid(logits)sess.run(tf.global_variables_initializer())# print([sess.run(value,feed_dict={X:x_data, Y: y_data})[0][0],y_data])Accurency += sess.run(tf.cast(tf.equal(sess.run(value, feed_dict={X:x_data, Y: y_data})[0][0], y_data), tf.float32))print("Accurency : {0}%".format(Accurency/int(len(data)*.2) * 100))print("Done!")

SVM

import numpy as np
from sklearn import svmpath_file = "./Image_Files/fer2013.csv"def data_get(type):"""param type:Training or Test"""pass
train_dataX, train_dataY = data_get("Training")clf = svm.SVC()
clf.fit(train_dataX, train_dataY)
print(clf.score(trai_dataX, train_dataY))

Convolution Neural Network

import tensorflow as tf
import numpy as np
from PIL import ImageX = tf.placeholder(tf.float32, [28, 28], name="X_placeholder")
Y = tf.placeholder(tf.float32, [10], name="Y_placeholder")# cnn
images = tf.reshape(X, shape=[-1, 28, 28, 1])
kernel = tf.get_variable('kernel', [5,5,1,16], initializer=tf.truncated_normal_initializer())biases = tf.get_variable('bias', [16], initializer=tf.random_normal_initializer())conv = tf.nn.conv2d(images, kernel, strides=[1,1,1,1], padding="SAME")
conv = tf.nn.relu(conv + biases, name="conv")# pool
pool = tf.nn.max_pool(conv, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
pool = tf.reshape(pool, [-1,14*14*16])# linear processing
w1 = tf.get_variable('weights', [14*14*16, 1024], initializer=tf.truncated_normal_initializer())
b1 = tf.get_variable('biases', [1024], initializer=tf.random_normal_initializer())f = tf.nn.relu(tf.matmul(pool, w1) + b1, name="relu")
# dropout
f = tf.nn.dropout(f, .75, name='relu_dropout')# linear processing
w = tf.get_variable('weights_', [1024, 10], initializer=tf.truncated_normal_initializer())
b = tf.get_variable('bias_', [10], initializer=tf.random_normal_initializer())# logits regression
logits = tf.matmul(f, w) + bentropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=Y, name="entropy")
loss = tf.reduce_mean(entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=.001).minimize(loss)# get datas
train_data_images = load_train_images()
train_data_label = load_train_labels()
test_data_images = load_test_images()
test_data_label = load_test_labels()with tf.Session() as sess:sess.run(tf.global_variables_initializer())# Traintotal_loss = 0for i in range(len(train_data_images)):x_data = train_data_images[i]y_data = train_data_label[i]_, loss_ = sess.run([optimizer, loss], feed_dict={X: x_data, Y: y_data})total_loss += loss_print("total loss : {0}".format(total_loss))# Testtotal_loss = 0w, b = sess.run([w,b])for i in range(len(test_data_images)):x_data = train_data_images[i]y_data = train_data_label[i]softmax_ = tf.nn.softmax(logits)pred = tf.equal(tf.argmax(softmax_, 1), tf.argmax(Y,1))total_loss += sess.run(pred, feed_dict={X: x_data, Y: y_data})print("total loss : {0}".format(total_loss))

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Data Analysis

Linear Regression

Logits Regression

SVM

Convolution Neural Network

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