intilizition

msc/REFERENCES

@@ -0,0 +1,16 @@
+https://github.com/salu133445/musegan
+
+https://arxiv.org/pdf/1611.03477v1.pdf
+
+
+https://github.com/salu133445/bmusegan
+
+https://salu133445.github.io/lakh-pianoroll-dataset/dataset
+
+https://blog.csdn.net/weixin_33796205/article/details/85960646
+
+http://www.sohu.com/a/242051796_473283
+
+http://www.360doc.com/content/19/0521/16/2459_837283904.shtml
+
+https://magenta.tensorflow.org/datasets/maestro#v200

novel/generate.py

@@ -0,0 +1,86 @@
+'''
+@lanhuage: python
+@Descripttion: 
+@version: beta
+@Author: xiaoshuyui
+@Date: 2020-06-17 17:07:23
+@LastEditors: xiaoshuyui
+@LastEditTime: 2020-07-16 16:05:51
+'''
+from keras.models import load_model
+import numpy as np
+import random
+
+with open('D:\\testALg\\Diy-musics\\novel\\static\\nietzsche.txt', 'r') as f:
+    text = f.read()
+
+print('corpus length:', len(text))
+
+chars = sorted(list(set(text)))
+char_num = len(chars)
+print('total chars:', char_num)
+char_indices = dict((c, i) for i, c in enumerate(chars))
+indices_char = dict((i, c) for i, c in enumerate(chars))
+
+# cut the text in semi-redundant sequences of maxlen characters
+maxlen = 40  # 需要和kears_lstm_train.py里边一样
+
+# build the model: a single LSTM
+print('load model...')
+# model = load_model('shakes_model.h5')
+model = load_model('D:\\testALg\\Diy-musics\\novel\\nz_model.h5')
+
+
+def sample(preds, temperature=1.0):
+    """
+    helper function to sample an index from a probability array
+    :param preds: 模型正向传播计算得到的向量a，维度(char_num, 1)
+    :param temperature: 多样性控制，值越大，随机性越强
+    :return:
+    """
+    preds = np.asarray(preds).astype('float64')
+    preds = np.log(preds) / temperature
+    # 下面个两行做了softmax
+    exp_preds = np.exp(preds)
+    preds = exp_preds / np.sum(exp_preds)
+    # 按照概率preds做一次随机抽样试验，返回一个和preds维度相同0，1向量，1表示该位置选中
+    probas = np.random.multinomial(1, preds, 1)
+    # 返回1所在位置，根据位置可以去找到对应字符
+    return np.argmax(probas)
+
+
+def generate_output(text_len=10):
+    """
+    生成文本
+    :param text_len: 生成的text文本长度
+    :return:
+    """
+    diversity = 1.0
+    generated = ''  # 最终生成的文本
+    # 从文本中随机选取位置截取maxlen字符作为初始输入句子
+    start_index = random.randint(0, len(text) - maxlen - 1)
+
+    sentence = text[start_index: start_index + maxlen]
+
+    for i in range(text_len):
+        # 输入句子x_pred
+        x_pred = np.zeros((1, maxlen, len(chars)))
+        # 生成one_hot向量
+        for t, char in enumerate(sentence):
+            x_pred[0, t, char_indices[char]] = 1.
+        # 预测输出
+        preds = model.predict(x_pred, verbose=0)[0]
+        next_index = sample(preds, diversity)
+        next_char = indices_char[next_index]
+        # 将句子字符串左移一位，新字符加载句子末尾，作为新的输入
+        sentence = sentence[1:] + next_char
+        generated += next_char
+    return generated
+
+
+text_len = random.randint(100, 200)
+print('text len:', text_len)
+new_text = generate_output(text_len)
+
+print('====================')
+print(new_text)

novel/mergeTxt.py

@@ -0,0 +1,32 @@
+'''
+@lanhuage: python
+@Descripttion: 
+@version: beta
+@Author: xiaoshuyui
+@Date: 2020-06-17 16:56:25
+@LastEditors: xiaoshuyui
+@LastEditTime: 2020-06-17 17:05:18
+'''
+import os
+
+filePath = 'D:\\testALg\\Diy-musics\\novel\\static\\holmes\\'
+
+txts = []
+for root,dirs,files in os.walk(filePath):
+    for f in files:
+        # if re.match(r'.*\d.*', f):
+        fullname = os.path.join(root, f)
+        txts.append(fullname)
+
+print(txts)
+
+res = ""
+for f in txts:
+
+    with open(f,'r',encoding='utf-8',errors='ignore') as fil:
+        cotent = fil.read()
+
+        res = res + cotent
+
+with open('D:\\testALg\\Diy-musics\\novel\\static\\h.txt','w',encoding='utf-8') as f:
+    f.write(res)