TensorFlow 文本识别

import pandas as pdimport numpy as npimport tensorflow as tffrom collections import Counterfrom sklearn.datasets import fetch_20newsgroups

How TensorFlow works

import tensorflow as tfmy_graph = tf.Graph()with tf.Session(graph=my_graph) as sess:    x = tf.constant([1,3,6])     y = tf.constant([1,1,1])    op = tf.add(x,y)    result = sess.run(fetches=op)    print(result)

[2 4 7]

How to manipulate data and pass it to the Neural Network inputs

vocab = Counter()text = "Hi from Brazil"for word in text.split(' '):    word_lowercase = word.lower()    vocab[word_lowercase]+=1        def get_word_2_index(vocab):    word2index = {}    for i,word in enumerate(vocab):        word2index[word] = i            return word2index

word2index = get_word_2_index(vocab)total_words = len(vocab)matrix = np.zeros((total_words),dtype=float)for word in text.split():    matrix[word2index[word.lower()]] += 1    print("Hi from Brazil:", matrix)

Hi from Brazil: [ 1.  1.  1.]

matrix = np.zeros((total_words),dtype=float)text = "Hi"for word in text.split():    matrix[word2index[word.lower()]] += 1    print("Hi:", matrix)

Hi: [ 1.  0.  0.]

Building the neural network

categories = ["comp.graphics","sci.space","rec.sport.baseball"]newsgroups_train = fetch_20newsgroups(subset='train', categories=categories)newsgroups_test = fetch_20newsgroups(subset='test', categories=categories)

print('total texts in train:',len(newsgroups_train.data))print('total texts in test:',len(newsgroups_test.data))

total texts in train: 1774total texts in test: 1180

print('text',newsgroups_train.data[0])print('category:',newsgroups_train.target[0])

text From: [e-mail] (Kouhia Juhana)Subject: Re: More gray levels out of the screenOrganization: Tampere University of TechnologyLines: 21Distribution: inetNNTP-Posting-Host: cc.tut.fiIn article <[e-mail]> [e-mail](Kenneth Sloan) writes:>>Why didn't you create 8 grey-level images, and display them for>1,2,4,8,16,32,64,128... time slices?By '8 grey level images' you mean 8 items of 1bit images?It does work(!), but it doesn't work if you have more than 1bitin your screen and if the screen intensity is non-linear.With 2 bit per pixel; there could be 1*c_1 + 4*c_2 timing,this gives 16 levels, but they are linear if screen intensity islinear.With 1*c_1 + 2*c_2 it works, but we have to find the bestcompinations -- there's 10 levels, but 16 choises; best 10 must bechosen. Different compinations for the same level, varies a bit, butthe levels keeps their order.Readers should verify what I wrote... :-)Juhana Kouhiacategory: 0

vocab = Counter()for text in newsgroups_train.data:    for word in text.split(' '):        vocab[word.lower()]+=1        for text in newsgroups_test.data:    for word in text.split(' '):        vocab[word.lower()]+=1

print("Total words:",len(vocab))

Total words: 119930

total_words = len(vocab)def get_word_2_index(vocab):    word2index = {}    for i,word in enumerate(vocab):        word2index[word.lower()] = i            return word2indexword2index = get_word_2_index(vocab)print("Index of the word 'the':",word2index['the'])

Index of the word 'the': 10

def get_batch(df,i,batch_size):    batches = []    results = []    texts = df.data[i*batch_size:i*batch_size+batch_size]    categories = df.target[i*batch_size:i*batch_size+batch_size]    for text in texts:        layer = np.zeros(total_words,dtype=float)        for word in text.split(' '):            layer[word2index[word.lower()]] += 1                    batches.append(layer)            for category in categories:        y = np.zeros((3),dtype=float)        if category == 0:            y[0] = 1.        elif category == 1:            y[1] = 1.        else:            y[2] = 1.        results.append(y)                     return np.array(batches),np.array(results)

print("Each batch has 100 texts and each matrix has 119930 elements (words):",get_batch(newsgroups_train,1,100)[0].shape)

Each batch has 100 texts and each matrix has 119930 elements (words): (100, 119930)

print("Each batch has 100 labels and each matrix has 3 elements (3 categories):",get_batch(newsgroups_train,1,100)[1].shape)

Each batch has 100 labels and each matrix has 3 elements (3 categories): (100, 3)

# Parameterslearning_rate = 0.01training_epochs = 10batch_size = 150display_step = 1# Network Parametersn_hidden_1 = 100      # 1st layer number of featuresn_hidden_2 = 100       # 2nd layer number of featuresn_input = total_words # Words in vocabn_classes = 3         # Categories: graphics, sci.space and baseballinput_tensor = tf.placeholder(tf.float32,[None, n_input],name="input")output_tensor = tf.placeholder(tf.float32,[None, n_classes],name="output") 

def multilayer_perceptron(input_tensor, weights, biases):    layer_1_multiplication = tf.matmul(input_tensor, weights['h1'])    layer_1_addition = tf.add(layer_1_multiplication, biases['b1'])    layer_1 = tf.nn.relu(layer_1_addition)        # Hidden layer with RELU activation    layer_2_multiplication = tf.matmul(layer_1, weights['h2'])    layer_2_addition = tf.add(layer_2_multiplication, biases['b2'])    layer_2 = tf.nn.relu(layer_2_addition)        # Output layer     out_layer_multiplication = tf.matmul(layer_2, weights['out'])    out_layer_addition = out_layer_multiplication + biases['out']        return out_layer_addition

# Store layers weight & biasweights = {    'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),    'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))}biases = {    'b1': tf.Variable(tf.random_normal([n_hidden_1])),    'b2': tf.Variable(tf.random_normal([n_hidden_2])),    'out': tf.Variable(tf.random_normal([n_classes]))}# Construct modelprediction = multilayer_perceptron(input_tensor, weights, biases)# Define loss and optimizerloss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=output_tensor))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)# Initializing the variablesinit = tf.global_variables_initializer()

# Launch the graphwith tf.Session() as sess:    sess.run(init)    # Training cycle    for epoch in range(training_epochs):        avg_cost = 0.        total_batch = int(len(newsgroups_train.data)/batch_size)        # Loop over all batches        for i in range(total_batch):            batch_x,batch_y = get_batch(newsgroups_train,i,batch_size)            # Run optimization op (backprop) and cost op (to get loss value)            c,_ = sess.run([loss,optimizer], feed_dict={input_tensor: batch_x,output_tensor:batch_y})            # Compute average loss            avg_cost += c / total_batch        # Display logs per epoch step        if epoch % display_step == 0:            print("Epoch:", '%04d' % (epoch+1), "loss=", \                "{:.9f}".format(avg_cost))    print("Optimization Finished!")    # Test model    correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(output_tensor, 1))    # Calculate accuracy    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))    total_test_data = len(newsgroups_test.target)    batch_x_test,batch_y_test = get_batch(newsgroups_test,0,total_test_data)    print("Accuracy:", accuracy.eval({input_tensor: batch_x_test, output_tensor: batch_y_test}))

Epoch: 0001 loss= 1133.908114347Epoch: 0002 loss= 329.093700409Epoch: 0003 loss= 111.876660109Epoch: 0004 loss= 72.552971845Epoch: 0005 loss= 16.673050320Epoch: 0006 loss= 16.481995190Epoch: 0007 loss= 4.848220565Epoch: 0008 loss= 0.759822878Epoch: 0009 loss= 0.000000000Epoch: 0010 loss= 0.079848485Optimization Finished!Accuracy: 0.75