#改變batch_size 和max_features的值影響圖像的形狀

import tensorflow as tf import matplotlib.pyplot as plt import csv import numpy as np import string import os from zipfile import ZipFile import io import requests sess = tf.Session()

batch_size = 500 max_features = 2000

save_file_name = os.path.join('temp', 'temp_spam_data.csv')

if not os.path.exists('temp'): os.makedirs('temp')

if os.path.isfile(save_file_name): text_data = [] with open(save_file_name, 'r') as temp_output_file: reader = csv.reader(temp_output_file) for row in reader: text_data.append(row) else: zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip' r = requests.get(zip_url) z = ZipFile(io.BytesIO(r.content)) file = z.read('SMSSpamCollection') # Format Data text_data = file.decode() text_data = text_data.encode('ascii', errors='ignore') text_data = text_data.decode().split('\n') text_data = [x.split('\t') for x in text_data if len(x)>= 1]

# And write to csv
with open(save_file_name, 'w') as temp_output_file:
 writer = csv.writer(temp_output_file)
 writer.writerows(text_data)

texts = [x[1] for x in text_data if len(x)>0] #如果不加len（x）容易報index 超過邊界 target = [x[0] for x in text_data if len(x)>0] target = [1. if x == 'spam' else 0. for x in target]

texts = [x.lower() for x in texts] texts = [''.join(c for c in x if c not in string.punctuation) for x in texts] texts = [''.join(c for c in x if c not in '0123456789') for x in texts] texts = [' '.join(x.split()) for x in texts] import nltk def tokenizer(text): words = nltk.word_tokenize(text) return words from sklearn.feature_extraction.text import TfidfVectorizer tfidf = TfidfVectorizer(tokenizer = tokenizer,stop_words='english',max_features = max_features) sparse_tfidf_texts = tfidf.fit_transform(texts)

train_indices = np.random.choice(sparse_tfidf_texts.shape[0],round(0.8*sparse_tfidf_texts.shape[0]),replace=False) test_indices = np.array(list(set(range(sparse_tfidf_texts.shape[0]))-set(train_indices))) tests_train = sparse_tfidf_texts[train_indices] tests_test = sparse_tfidf_texts[test_indices] target_train = np.array([x for ix,x in enumerate(target) if ix in train_indices]) target_test = np.array([x for ix,x in enumerate(target) if ix in test_indices]) A = tf.Variable(tf.random_normal(shape=[max_features,1])) b = tf.Variable(tf.random_normal(shape=[1,1]))

x_data = tf.placeholder(shape=[None,max_features],dtype=tf.float32) y_target = tf.placeholder(shape=[None,1],dtype=tf.float32)

model_out = tf.add(tf.matmul(x_data,A),b) #For brevity, let x = logits, z = labels. The logistic loss is notice the sequence loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_target,logits=model_out))

prediction = tf.round(tf.sigmoid(model_out))#notice prediction_correct = tf.cast(tf.equal(prediction,y_target),tf.float32) accuracy = tf.reduce_mean(prediction_correct) my_opt = tf.train.GradientDescentOptimizer(0.0025) train_step = my_opt.minimize(loss)

init = tf.global_variables_initializer() sess.run(init)

Start Logistic Regression

train_loss = [] test_loss = [] train_acc = [] test_acc = [] i_data = [] for i in range(10000): rand_index = np.random.choice(tests_train.shape[0], size=batch_size) rand_x = tests_train[rand_index].todense() rand_y = np.transpose([target_train[rand_index]]) sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y}) if (i + 1) % 100 == 0: i_data.append(i + 1) train_loss_temp = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y}) train_loss.append(train_loss_temp)

 test_loss_temp = sess.run(loss, feed_dict={x_data: tests_test.todense(), y_target: np.transpose([target_test])})
 test_loss.append(test_loss_temp)
 train_acc_temp = sess.run(accuracy, feed_dict={x_data: rand_x, y_target: rand_y})
 train_acc.append(train_acc_temp)
 test_acc_temp = sess.run(accuracy, feed_dict={x_data: tests_test.todense(), y_target: np.transpose([target_test])})
 test_acc.append(test_acc_temp)

Plot loss over time

plt.plot(i_data, train_loss, 'k-', label='Train Loss') plt.plot(i_data, test_loss, 'r--', label='Test Loss', linewidth=4) plt.title('Cross Entropy Loss per Generation') plt.xlabel('Generation') plt.ylabel('Cross Entropy Loss') plt.legend(loc='upper right') plt.show()

Plot train and test accuracy

plt.plot(i_data, train_acc, 'k-', label='Train Set Accuracy') plt.plot(i_data, test_acc, 'r--', label='Test Set Accuracy',linewidth=4) plt.title('Train and Test Accuracy') plt.xlabel('Generation') plt.ylabel('Accuracy') plt.legend(loc='lower right') plt.show()