add one_lstm_sequence_generate.py

lichuang · lichuang · commit a2b6c7444524 · 2016-11-23T09:20:03.000+08:00
diff --git a/chatbotv2/one_lstm_sequence_generate.py b/chatbotv2/one_lstm_sequence_generate.py
@@ -0,0 +1,232 @@
+# -*- coding: utf-8 -*-
+
+import sys
+import math
+import tflearn
+import chardet
+import numpy as np
+import struct
+
+seq = []
+
+max_w = 50
+float_size = 4
+word_vector_dict = {}
+
+def load_vectors(input):
+    """从vectors.bin加载词向量，返回一个word_vector_dict的词典，key是词，value是200维的向量
+    """
+    print "begin load vectors"
+
+    input_file = open(input, "rb")
+
+    # 获取词表数目及向量维度
+    words_and_size = input_file.readline()
+    words_and_size = words_and_size.strip()
+    words = long(words_and_size.split(' ')[0])
+    size = long(words_and_size.split(' ')[1])
+    print "words =", words
+    print "size =", size
+
+    for b in range(0, words):
+        a = 0
+        word = ''
+        # 读取一个词
+        while True:
+            c = input_file.read(1)
+            word = word + c
+            if False == c or c == ' ':
+                break
+            if a < max_w and c != '\n':
+                a = a + 1
+        word = word.strip()
+
+        vector = []
+        for index in range(0, size):
+            m = input_file.read(float_size)
+            (weight,) = struct.unpack('f', m)
+            vector.append(weight)
+
+        # 将词及其对应的向量存到dict中
+        word_vector_dict[word.decode('utf-8')] = vector
+
+    input_file.close()
+
+    print "load vectors finish"
+
+def init_seq():
+    """读取切好词的文本文件，加载全部词序列
+    """
+    file_object = open('zhenhuanzhuan.segment', 'r')
+    vocab_dict = {}
+    while True:
+        line = file_object.readline()
+        if line:
+            for word in line.decode('utf-8').split(' '):
+                if word_vector_dict.has_key(word):
+                    seq.append(word_vector_dict[word])
+        else:
+            break
+    file_object.close()
+
+def vector_sqrtlen(vector):
+    len = 0
+    for item in vector:
+        len += item * item
+    len = math.sqrt(len)
+    return len
+
+def vector_cosine(v1, v2):
+    if len(v1) != len(v2):
+        sys.exit(1)
+    sqrtlen1 = vector_sqrtlen(v1)
+    sqrtlen2 = vector_sqrtlen(v2)
+    value = 0
+    for item1, item2 in zip(v1, v2):
+        value += item1 * item2
+    return value / (sqrtlen1*sqrtlen2)
+
+
+def vector2word(vector):
+    max_cos = -10000
+    match_word = ''
+    for word in word_vector_dict:
+        v = word_vector_dict[word]
+        cosine = vector_cosine(vector, v)
+        if cosine > max_cos:
+            max_cos = cosine
+            match_word = word
+    return (match_word, max_cos)
+
+
+def main():
+    load_vectors("./vectors.bin")
+    init_seq()
+    xlist = []
+    ylist = []
+    test_X = None
+    #for i in range(len(seq)-100):
+    for i in range(10):
+        sequence = seq[i:i+20]
+        xlist.append(sequence)
+        ylist.append(seq[i+20])
+        if test_X is None:
+            test_X = np.array(sequence)
+            (match_word, max_cos) = vector2word(seq[i+20])
+            print "right answer=", match_word, max_cos
+
+    X = np.array(xlist)
+    Y = np.array(ylist)
+    net = tflearn.input_data([None, 20, 200])
+    net = tflearn.lstm(net, 200)
+    net = tflearn.fully_connected(net, 200, activation='linear')
+    net = tflearn.regression(net, optimizer='sgd', learning_rate=0.1,
+                                     loss='mean_square')
+    model = tflearn.DNN(net)
+    model.fit(X, Y, n_epoch=500, batch_size=10,snapshot_epoch=False,show_metric=True)
+    model.save("model")
+    predict = model.predict([test_X])
+    #print predict
+    #for v in test_X:
+    #    print vector2word(v)
+    (match_word, max_cos) = vector2word(predict[0])
+    print "predict=", match_word, max_cos
+
+main()
+#init_seq()
+#
+#x1 = [1,2,3,4,5]
+#x2 = [0,1,2,3]
+#y = [2,4,6,8,10]
+#X = np.array([x1,x2])
+#Y = np.array([y])
+#net = tflearn.input_data([None])
+##net = tflearn.embedding(net, input_dim=4, output_dim=2)
+#net = tflearn.single_unit(net)
+##net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,loss='categorical_crossentropy')
+#net = tflearn.regression(net, optimizer='sgd', loss='mean_square',
+#                                        metric='R2', learning_rate=0.01)
+##layer1 = tflearn.fully_connected(net, 2)
+#model = tflearn.DNN(net)
+#model.fit(x1, y, n_epoch=1000, snapshot_epoch=False,show_metric=True,batch_size=1)
+#print model.predict(x1)
+##net = tflearn.embedding(net, input_dim=10000, output_dim=16)
+#net = tflearn.lstm(net, 16, dropout=0.8)
+#net = tflearn.fully_connected(net, 2, activation='softmax')
+#net = tflearn.regression(net, optimizer='adam', learning_rate=0.001,loss='categorical_crossentropy')
+#model = tflearn.DNN(net, tensorboard_verbose=0)
+#model.fit(X, Y, show_metric=True,batch_size=32)
+
+
+#sys.exit(0)
+# 下面都是测试用的，不用看
+
+def test_case1():
+    x = [1,2,3]
+    y = [0.01,0.99]
+    # 多组x作为输入样本
+    X = np.array(np.repeat([x], 1, axis=0))
+    # 多组y作为样本的y值
+    Y = np.array(np.repeat([y], 1, axis=0))
+
+    #X = np.array([x1,x2], dtype=np.float32)
+    #Y = np.array([y1,y2])
+
+    # 这里的第二个数对应了x是多少维的向量
+    net = tflearn.input_data(shape=[None, 3])
+    #net = tflearn.fully_connected(net, 32)
+    net = tflearn.fully_connected(net, 2)
+    # 这里的第二个参数对应了输出的y是多少维的向量
+    #net = tflearn.fully_connected(net, 2, activation='softmax')
+    net = tflearn.regression(net)
+
+
+    model = tflearn.DNN(net)
+    model.fit(X, Y, n_epoch=1000, batch_size=1, show_metric=True, snapshot_epoch=False)
+    pred = model.predict([x])
+    print(pred)
+
+def case_linear_regression():
+    x = [1,2,3,4,5]
+    y = [2,4,6,8,10]
+    net = tflearn.input_data([None])
+    linear = tflearn.single_unit(net)
+    net = tflearn.regression(linear, optimizer='sgd', loss='mean_square',
+                                            metric='R2', learning_rate=0.01)
+    model = tflearn.DNN(net)
+    model.fit(x, y, n_epoch=200, snapshot_epoch=False,show_metric=True,batch_size=1)
+    print model.predict([8,9]) # [15.990408897399902, 17.988374710083008]
+    print model.get_weights(linear.W) # [ 1.99796414]
+    print model.get_weights(linear.b) # [ 0.00669619]
+
+#case_linear_regression()
+
+
+#X = [[0., 0.], [0., 1.], [1., 0.], [1., 1.]]
+#Y_xor = [[0.], [1.], [1.], [0.]]
+
+# 如何输出每一步的输出值
+# You can re-use a new model that share a same session (to use same weights): . Note that you could also save your 'm' model and load it with 'm2', that gives similar results.
+## Graph definition
+#with tf.Graph().as_default():
+#    tnorm = tflearn.initializations.uniform(minval=-1.0, maxval=1.0)
+#    net = tflearn.input_data(shape=[None, 2], name='inputLayer')
+#    layer1 = tflearn.fully_connected(net, 2, activation='sigmoid', weights_init=tnorm, name='layer1')
+#    layer2 = tflearn.fully_connected(layer1, 1, activation='softmax', weights_init=tnorm, name='layer2')
+#    regressor = tflearn.regression(layer2, optimizer='sgd', learning_rate=2., loss='mean_square', name='layer3')
+#
+#    # Training
+#    m = tflearn.DNN(regressor)
+#    m.fit(X, Y_xor, n_epoch=100, snapshot_epoch=False) 
+#
+#    # Testing
+#    print("Testing XOR operator")
+#    print("0 xor 0:", m.predict([[0., 0.]]))
+#    print("0 xor 1:", m.predict([[0., 1.]]))
+#    print("1 xor 0:", m.predict([[1., 0.]]))
+#    print("1 xor 1:", m.predict([[1., 1.]]))
+#
+#    # You can create a new model, that share the same session (to get same weights)
+#    # Or you can also simply save and load a model
+#    m2 = tflearn.DNN(layer1, session=m.session)
+#    print(m2.predict([[0., 0.]]))
