├── .ipynb_checkpoints
    └── CNN-LSTM-Attention-checkpoint.ipynb
├── CNN-LSTM-Attention.ipynb
├── CNN-LSTM-Attention.py
├── README.md
└── data.csv


/CNN-LSTM-Attention.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stderr",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "F:\\Anaconda\\location\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
 13 |       "  from ._conv import register_converters as _register_converters\n",
 14 |       "Using TensorFlow backend.\n"
 15 |      ]
 16 |     }
 17 |    ],
 18 |    "source": [
 19 |     "#导入必要的库\n",
 20 |     "import numpy as np\n",
 21 |     "import matplotlib.pyplot as plt\n",
 22 |     "import pandas as pd\n",
 23 |     "from sklearn import preprocessing\n",
 24 |     "from sklearn.metrics import mean_squared_error\n",
 25 |     "from sklearn.metrics import mean_absolute_error\n",
 26 |     "from math import sqrt\n",
 27 |     "import math\n",
 28 |     "from keras.layers import *\n",
 29 |     "from keras.models import *\n",
 30 |     "from keras.optimizers import Adam"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "code",
 35 |    "execution_count": 2,
 36 |    "metadata": {},
 37 |    "outputs": [
 38 |     {
 39 |      "data": {
 40 |       "text/html": [
 41 |        "<div>\n",
 42 |        "<style scoped>\n",
 43 |        "    .dataframe tbody tr th:only-of-type {\n",
 44 |        "        vertical-align: middle;\n",
 45 |        "    }\n",
 46 |        "\n",
 47 |        "    .dataframe tbody tr th {\n",
 48 |        "        vertical-align: top;\n",
 49 |        "    }\n",
 50 |        "\n",
 51 |        "    .dataframe thead th {\n",
 52 |        "        text-align: right;\n",
 53 |        "    }\n",
 54 |        "</style>\n",
 55 |        "<table border=\"1\" class=\"dataframe\">\n",
 56 |        "  <thead>\n",
 57 |        "    <tr style=\"text-align: right;\">\n",
 58 |        "      <th></th>\n",
 59 |        "      <th>open</th>\n",
 60 |        "      <th>low</th>\n",
 61 |        "      <th>close</th>\n",
 62 |        "      <th>high</th>\n",
 63 |        "    </tr>\n",
 64 |        "  </thead>\n",
 65 |        "  <tbody>\n",
 66 |        "    <tr>\n",
 67 |        "      <th>26272</th>\n",
 68 |        "      <td>7.1042</td>\n",
 69 |        "      <td>7.1042</td>\n",
 70 |        "      <td>7.1048</td>\n",
 71 |        "      <td>7.1055</td>\n",
 72 |        "    </tr>\n",
 73 |        "    <tr>\n",
 74 |        "      <th>26273</th>\n",
 75 |        "      <td>7.1047</td>\n",
 76 |        "      <td>7.1038</td>\n",
 77 |        "      <td>7.1038</td>\n",
 78 |        "      <td>7.1047</td>\n",
 79 |        "    </tr>\n",
 80 |        "    <tr>\n",
 81 |        "      <th>26274</th>\n",
 82 |        "      <td>7.1039</td>\n",
 83 |        "      <td>7.1039</td>\n",
 84 |        "      <td>7.1046</td>\n",
 85 |        "      <td>7.1047</td>\n",
 86 |        "    </tr>\n",
 87 |        "    <tr>\n",
 88 |        "      <th>26275</th>\n",
 89 |        "      <td>7.1048</td>\n",
 90 |        "      <td>7.1043</td>\n",
 91 |        "      <td>7.1045</td>\n",
 92 |        "      <td>7.1049</td>\n",
 93 |        "    </tr>\n",
 94 |        "    <tr>\n",
 95 |        "      <th>26276</th>\n",
 96 |        "      <td>7.1049</td>\n",
 97 |        "      <td>7.1049</td>\n",
 98 |        "      <td>7.1057</td>\n",
 99 |        "      <td>7.1057</td>\n",
100 |        "    </tr>\n",
101 |        "  </tbody>\n",
102 |        "</table>\n",
103 |        "</div>"
104 |       ],
105 |       "text/plain": [
106 |        "         open     low   close    high\n",
107 |        "26272  7.1042  7.1042  7.1048  7.1055\n",
108 |        "26273  7.1047  7.1038  7.1038  7.1047\n",
109 |        "26274  7.1039  7.1039  7.1046  7.1047\n",
110 |        "26275  7.1048  7.1043  7.1045  7.1049\n",
111 |        "26276  7.1049  7.1049  7.1057  7.1057"
112 |       ]
113 |      },
114 |      "execution_count": 2,
115 |      "metadata": {},
116 |      "output_type": "execute_result"
117 |     }
118 |    ],
119 |    "source": [
120 |     "#设置LSTM的时间窗等参数\n",
121 |     "window=5\n",
122 |     "lstm_units = 16\n",
123 |     "dropout = 0.01\n",
124 |     "epoch=60\n",
125 |     "#读取数据\n",
126 |     "df1=pd.read_csv('data.csv') \n",
127 |     "df1=df1.iloc[:,2:]\n",
128 |     "df1.tail()"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 3,
134 |    "metadata": {},
135 |    "outputs": [],
136 |    "source": [
137 |     "#进行数据归一化\n",
138 |     "from sklearn import preprocessing\n",
139 |     "min_max_scaler = preprocessing.MinMaxScaler()\n",
140 |     "df0=min_max_scaler.fit_transform(df1)\n",
141 |     "df = pd.DataFrame(df0, columns=df1.columns)\n",
142 |     "input_size=len(df.iloc[1,:])"
143 |    ]
144 |   },
145 |   {
146 |    "cell_type": "code",
147 |    "execution_count": 4,
148 |    "metadata": {},
149 |    "outputs": [
150 |     {
151 |      "name": "stdout",
152 |      "output_type": "stream",
153 |      "text": [
154 |       "(23644, 5, 4) (23644,) (2627, 5, 4) (2627,)\n"
155 |      ]
156 |     },
157 |     {
158 |      "name": "stderr",
159 |      "output_type": "stream",
160 |      "text": [
161 |       "F:\\Anaconda\\location\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
162 |       "  \"\"\"\n"
163 |      ]
164 |     }
165 |    ],
166 |    "source": [
167 |     "#构建lstm输入\n",
168 |     "stock=df\n",
169 |     "seq_len=window\n",
170 |     "amount_of_features = len(stock.columns)#有几列\n",
171 |     "data = stock.as_matrix() #pd.DataFrame(stock) 表格转化为矩阵\n",
172 |     "sequence_length = seq_len + 1#序列长度\n",
173 |     "result = []\n",
174 |     "for index in range(len(data) - sequence_length):#循环数据长度-sequence_length次\n",
175 |     "    result.append(data[index: index + sequence_length])#第i行到i+sequence_length\n",
176 |     "result = np.array(result)#得到样本，样本形式为6天*3特征\n",
177 |     "row = round(0.9 * result.shape[0])#划分训练集测试集\n",
178 |     "train = result[:int(row), :]\n",
179 |     "x_train = train[:, :-1]\n",
180 |     "y_train = train[:, -1][:,-1]\n",
181 |     "x_test = result[int(row):, :-1]\n",
182 |     "y_test = result[int(row):, -1][:,-1]\n",
183 |     "#reshape成 6天*3特征\n",
184 |     "X_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], amount_of_features))\n",
185 |     "X_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], amount_of_features))  \n",
186 |     "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
187 |    ]
188 |   },
189 |   {
190 |    "cell_type": "code",
191 |    "execution_count": 5,
192 |    "metadata": {},
193 |    "outputs": [
194 |     {
195 |      "name": "stdout",
196 |      "output_type": "stream",
197 |      "text": [
198 |       "__________________________________________________________________________________________________\n",
199 |       "Layer (type)                    Output Shape         Param #     Connected to                     \n",
200 |       "==================================================================================================\n",
201 |       "input_1 (InputLayer)            (None, 5, 4)         0                                            \n",
202 |       "__________________________________________________________________________________________________\n",
203 |       "conv1d_1 (Conv1D)               (None, 5, 16)        80          input_1[0][0]                    \n",
204 |       "__________________________________________________________________________________________________\n",
205 |       "max_pooling1d_1 (MaxPooling1D)  (None, 1, 16)        0           conv1d_1[0][0]                   \n",
206 |       "__________________________________________________________________________________________________\n",
207 |       "dropout_1 (Dropout)             (None, 1, 16)        0           max_pooling1d_1[0][0]            \n",
208 |       "__________________________________________________________________________________________________\n",
209 |       "bilstm (Bidirectional)          (None, 32)           4224        dropout_1[0][0]                  \n",
210 |       "__________________________________________________________________________________________________\n",
211 |       "attention_vec (Dense)           (None, 32)           1056        bilstm[0][0]                     \n",
212 |       "__________________________________________________________________________________________________\n",
213 |       "multiply_1 (Multiply)           (None, 32)           0           bilstm[0][0]                     \n",
214 |       "                                                                 attention_vec[0][0]              \n",
215 |       "__________________________________________________________________________________________________\n",
216 |       "dense_1 (Dense)                 (None, 1)            33          multiply_1[0][0]                 \n",
217 |       "==================================================================================================\n",
218 |       "Total params: 5,393\n",
219 |       "Trainable params: 5,393\n",
220 |       "Non-trainable params: 0\n",
221 |       "__________________________________________________________________________________________________\n"
222 |      ]
223 |     }
224 |    ],
225 |    "source": [
226 |     "#建立LSTM模型 训练\n",
227 |     "inputs=Input(shape=(window, input_size))\n",
228 |     "model=Conv1D(filters = lstm_units, kernel_size = 1, activation = 'sigmoid')(inputs)#卷积层\n",
229 |     "model=MaxPooling1D(pool_size = window)(model)#池化层\n",
230 |     "model=Dropout(dropout)(model)#droupout层\n",
231 |     "model=Bidirectional(LSTM(lstm_units, activation='tanh'), name='bilstm')(model)#双向LSTM层\n",
232 |     "attention=Dense(lstm_units*2, activation='sigmoid', name='attention_vec')(model)#求解Attention权重\n",
233 |     "model=Multiply()([model, attention])#attention与LSTM对应数值相乘\n",
234 |     "\n",
235 |     "outputs = Dense(1, activation='tanh')(model)\n",
236 |     "model = Model(inputs=inputs, outputs=outputs)\n",
237 |     "model.compile(loss='mse',optimizer='adam',metrics=['accuracy'])\n",
238 |     "model.summary()#展示模型结构"
239 |    ]
240 |   },
241 |   {
242 |    "cell_type": "code",
243 |    "execution_count": 6,
244 |    "metadata": {},
245 |    "outputs": [
246 |     {
247 |      "name": "stderr",
248 |      "output_type": "stream",
249 |      "text": [
250 |       "F:\\Anaconda\\location\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`.\n",
251 |       "  \"\"\"Entry point for launching an IPython kernel.\n"
252 |      ]
253 |     },
254 |     {
255 |      "name": "stdout",
256 |      "output_type": "stream",
257 |      "text": [
258 |       "Train on 23644 samples, validate on 2627 samples\n",
259 |       "Epoch 1/60\n",
260 |       "23644/23644 [==============================] - 4s 162us/step - loss: 0.0869 - acc: 4.2294e-05 - val_loss: 0.0560 - val_acc: 0.0000e+00\n",
261 |       "Epoch 2/60\n",
262 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0592 - acc: 4.2294e-05 - val_loss: 0.0542 - val_acc: 0.0000e+00\n",
263 |       "Epoch 3/60\n",
264 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0557 - acc: 4.2294e-05 - val_loss: 0.0511 - val_acc: 0.0000e+00\n",
265 |       "Epoch 4/60\n",
266 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0523 - acc: 4.2294e-05 - val_loss: 0.0482 - val_acc: 0.0000e+00\n",
267 |       "Epoch 5/60\n",
268 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0487 - acc: 4.2294e-05 - val_loss: 0.0454 - val_acc: 0.0000e+00\n",
269 |       "Epoch 6/60\n",
270 |       "23644/23644 [==============================] - 1s 28us/step - loss: 0.0446 - acc: 8.4588e-05 - val_loss: 0.0419 - val_acc: 0.0000e+00\n",
271 |       "Epoch 7/60\n",
272 |       "23644/23644 [==============================] - 1s 28us/step - loss: 0.0398 - acc: 8.4588e-05 - val_loss: 0.0375 - val_acc: 0.0000e+00\n",
273 |       "Epoch 8/60\n",
274 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0341 - acc: 8.4588e-05 - val_loss: 0.0313 - val_acc: 0.0000e+00\n",
275 |       "Epoch 9/60\n",
276 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0272 - acc: 8.4588e-05 - val_loss: 0.0226 - val_acc: 0.0000e+00\n",
277 |       "Epoch 10/60\n",
278 |       "23644/23644 [==============================] - 1s 34us/step - loss: 0.0199 - acc: 8.4588e-05 - val_loss: 0.0129 - val_acc: 0.0000e+00\n",
279 |       "Epoch 11/60\n",
280 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0132 - acc: 8.4588e-05 - val_loss: 0.0056 - val_acc: 0.0000e+00\n",
281 |       "Epoch 12/60\n",
282 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0085 - acc: 8.4588e-05 - val_loss: 0.0021 - val_acc: 0.0000e+00\n",
283 |       "Epoch 13/60\n",
284 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0059 - acc: 8.4588e-05 - val_loss: 0.0012 - val_acc: 0.0000e+00\n",
285 |       "Epoch 14/60\n",
286 |       "23644/23644 [==============================] - 1s 28us/step - loss: 0.0046 - acc: 8.4588e-05 - val_loss: 0.0010 - val_acc: 0.0000e+00\n",
287 |       "Epoch 15/60\n",
288 |       "23644/23644 [==============================] - 1s 28us/step - loss: 0.0042 - acc: 8.4588e-05 - val_loss: 8.9252e-04 - val_acc: 0.0000e+00\n",
289 |       "Epoch 16/60\n",
290 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0040 - acc: 8.4588e-05 - val_loss: 8.3400e-04 - val_acc: 0.0000e+00\n",
291 |       "Epoch 17/60\n",
292 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0039 - acc: 8.4588e-05 - val_loss: 8.4182e-04 - val_acc: 0.0000e+00\n",
293 |       "Epoch 18/60\n",
294 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0037 - acc: 8.4588e-05 - val_loss: 7.7355e-04 - val_acc: 0.0000e+00\n",
295 |       "Epoch 19/60\n",
296 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0036 - acc: 8.4588e-05 - val_loss: 7.4042e-04 - val_acc: 0.0000e+00\n",
297 |       "Epoch 20/60\n",
298 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0035 - acc: 8.4588e-05 - val_loss: 7.0072e-04 - val_acc: 0.0000e+00\n",
299 |       "Epoch 21/60\n",
300 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0034 - acc: 8.4588e-05 - val_loss: 6.7811e-04 - val_acc: 0.0000e+00\n",
301 |       "Epoch 22/60\n",
302 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0034 - acc: 8.4588e-05 - val_loss: 6.3723e-04 - val_acc: 0.0000e+00\n",
303 |       "Epoch 23/60\n",
304 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0033 - acc: 8.4588e-05 - val_loss: 5.9323e-04 - val_acc: 0.0000e+00\n",
305 |       "Epoch 24/60\n",
306 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0032 - acc: 8.4588e-05 - val_loss: 5.9388e-04 - val_acc: 0.0000e+00\n",
307 |       "Epoch 25/60\n",
308 |       "23644/23644 [==============================] - 1s 28us/step - loss: 0.0031 - acc: 8.4588e-05 - val_loss: 5.0749e-04 - val_acc: 0.0000e+00\n",
309 |       "Epoch 26/60\n",
310 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0031 - acc: 8.4588e-05 - val_loss: 5.1683e-04 - val_acc: 0.0000e+00\n",
311 |       "Epoch 27/60\n",
312 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0030 - acc: 8.4588e-05 - val_loss: 4.8656e-04 - val_acc: 0.0000e+00\n",
313 |       "Epoch 28/60\n",
314 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0029 - acc: 8.4588e-05 - val_loss: 4.2581e-04 - val_acc: 0.0000e+00\n",
315 |       "Epoch 29/60\n",
316 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0028 - acc: 8.4588e-05 - val_loss: 3.8148e-04 - val_acc: 0.0000e+00\n",
317 |       "Epoch 30/60\n",
318 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0027 - acc: 8.4588e-05 - val_loss: 3.5561e-04 - val_acc: 0.0000e+00\n",
319 |       "Epoch 31/60\n",
320 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0028 - acc: 8.4588e-05 - val_loss: 3.2914e-04 - val_acc: 0.0000e+00\n",
321 |       "Epoch 32/60\n",
322 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0027 - acc: 8.4588e-05 - val_loss: 2.8842e-04 - val_acc: 0.0000e+00\n",
323 |       "Epoch 33/60\n",
324 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0026 - acc: 8.4588e-05 - val_loss: 2.6484e-04 - val_acc: 0.0000e+00\n",
325 |       "Epoch 34/60\n",
326 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0026 - acc: 8.4588e-05 - val_loss: 2.4199e-04 - val_acc: 0.0000e+00\n",
327 |       "Epoch 35/60\n",
328 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0025 - acc: 8.4588e-05 - val_loss: 2.3726e-04 - val_acc: 0.0000e+00\n",
329 |       "Epoch 36/60\n",
330 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0024 - acc: 8.4588e-05 - val_loss: 2.1881e-04 - val_acc: 0.0000e+00\n",
331 |       "Epoch 37/60\n",
332 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0025 - acc: 8.4588e-05 - val_loss: 2.0510e-04 - val_acc: 0.0000e+00\n",
333 |       "Epoch 38/60\n",
334 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0024 - acc: 8.4588e-05 - val_loss: 1.8633e-04 - val_acc: 0.0000e+00\n",
335 |       "Epoch 39/60\n",
336 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0024 - acc: 8.4588e-05 - val_loss: 1.7439e-04 - val_acc: 0.0000e+00\n",
337 |       "Epoch 40/60\n",
338 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0023 - acc: 8.4588e-05 - val_loss: 1.7087e-04 - val_acc: 0.0000e+00\n",
339 |       "Epoch 41/60\n",
340 |       "23644/23644 [==============================] - 1s 33us/step - loss: 0.0022 - acc: 8.4588e-05 - val_loss: 1.6699e-04 - val_acc: 0.0000e+00\n",
341 |       "Epoch 42/60\n",
342 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0022 - acc: 8.4588e-05 - val_loss: 1.6415e-04 - val_acc: 0.0000e+00\n",
343 |       "Epoch 43/60\n",
344 |       "23644/23644 [==============================] - 1s 33us/step - loss: 0.0022 - acc: 8.4588e-05 - val_loss: 1.6044e-04 - val_acc: 0.0000e+00\n",
345 |       "Epoch 44/60\n",
346 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0021 - acc: 8.4588e-05 - val_loss: 1.6057e-04 - val_acc: 0.0000e+00\n",
347 |       "Epoch 45/60\n",
348 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0021 - acc: 8.4588e-05 - val_loss: 1.5719e-04 - val_acc: 0.0000e+00\n",
349 |       "Epoch 46/60\n",
350 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0021 - acc: 8.4588e-05 - val_loss: 1.6046e-04 - val_acc: 0.0000e+00\n",
351 |       "Epoch 47/60\n",
352 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0021 - acc: 8.4588e-05 - val_loss: 1.5809e-04 - val_acc: 0.0000e+00\n",
353 |       "Epoch 48/60\n",
354 |       "23644/23644 [==============================] - 1s 29us/step - loss: 0.0021 - acc: 8.4588e-05 - val_loss: 1.5841e-04 - val_acc: 0.0000e+00\n",
355 |       "Epoch 49/60\n",
356 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0020 - acc: 8.4588e-05 - val_loss: 1.6275e-04 - val_acc: 0.0000e+00\n",
357 |       "Epoch 50/60\n",
358 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0020 - acc: 8.4588e-05 - val_loss: 1.6380e-04 - val_acc: 0.0000e+00\n",
359 |       "Epoch 51/60\n",
360 |       "23644/23644 [==============================] - 1s 34us/step - loss: 0.0020 - acc: 8.4588e-05 - val_loss: 1.6336e-04 - val_acc: 0.0000e+00\n",
361 |       "Epoch 52/60\n",
362 |       "23644/23644 [==============================] - 1s 33us/step - loss: 0.0020 - acc: 8.4588e-05 - val_loss: 1.5693e-04 - val_acc: 0.0000e+00\n",
363 |       "Epoch 53/60\n",
364 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0020 - acc: 8.4588e-05 - val_loss: 1.6286e-04 - val_acc: 0.0000e+00\n",
365 |       "Epoch 54/60\n",
366 |       "23644/23644 [==============================] - 1s 35us/step - loss: 0.0020 - acc: 8.4588e-05 - val_loss: 1.6297e-04 - val_acc: 0.0000e+00\n",
367 |       "Epoch 55/60\n",
368 |       "23644/23644 [==============================] - 1s 32us/step - loss: 0.0020 - acc: 8.4588e-05 - val_loss: 1.8383e-04 - val_acc: 0.0000e+00\n"
369 |      ]
370 |     },
371 |     {
372 |      "name": "stdout",
373 |      "output_type": "stream",
374 |      "text": [
375 |       "Epoch 56/60\n",
376 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0020 - acc: 8.4588e-05 - val_loss: 1.7346e-04 - val_acc: 0.0000e+00\n",
377 |       "Epoch 57/60\n",
378 |       "23644/23644 [==============================] - 1s 28us/step - loss: 0.0019 - acc: 8.4588e-05 - val_loss: 1.5935e-04 - val_acc: 0.0000e+00\n",
379 |       "Epoch 58/60\n",
380 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0019 - acc: 8.4588e-05 - val_loss: 1.7183e-04 - val_acc: 0.0000e+00\n",
381 |       "Epoch 59/60\n",
382 |       "23644/23644 [==============================] - 1s 31us/step - loss: 0.0019 - acc: 8.4588e-05 - val_loss: 1.6112e-04 - val_acc: 0.0000e+00\n",
383 |       "Epoch 60/60\n",
384 |       "23644/23644 [==============================] - 1s 30us/step - loss: 0.0019 - acc: 8.4588e-05 - val_loss: 1.7391e-04 - val_acc: 0.0000e+00\n"
385 |      ]
386 |     }
387 |    ],
388 |    "source": [
389 |     "history=model.fit(X_train, y_train, nb_epoch = epoch, batch_size = 256,shuffle=False,validation_data=(X_test, y_test)) #训练模型epoch次"
390 |    ]
391 |   },
392 |   {
393 |    "cell_type": "code",
394 |    "execution_count": 7,
395 |    "metadata": {},
396 |    "outputs": [
397 |     {
398 |      "data": {
399 |       "image/png": 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\n",
400 |       "text/plain": [
401 |        "<matplotlib.figure.Figure at 0x12991fdcdd8>"
402 |       ]
403 |      },
404 |      "metadata": {},
405 |      "output_type": "display_data"
406 |     }
407 |    ],
408 |    "source": [
409 |     "#迭代图像\n",
410 |     "loss = history.history['loss']\n",
411 |     "val_loss = history.history['val_loss']\n",
412 |     "epochs_range = range(epoch)\n",
413 |     "plt.plot(epochs_range, loss, label='Train Loss')\n",
414 |     "plt.plot(epochs_range, val_loss, label='Test Loss')\n",
415 |     "plt.legend(loc='upper right')\n",
416 |     "plt.title('Train and Val Loss')\n",
417 |     "plt.show()"
418 |    ]
419 |   },
420 |   {
421 |    "cell_type": "code",
422 |    "execution_count": 8,
423 |    "metadata": {},
424 |    "outputs": [
425 |     {
426 |      "data": {
427 |       "text/plain": [
428 |        "Text(0.5,1,'Train Data')"
429 |       ]
430 |      },
431 |      "execution_count": 8,
432 |      "metadata": {},
433 |      "output_type": "execute_result"
434 |     },
435 |     {
436 |      "data": {
437 |       "image/png": 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\n",
438 |       "text/plain": [
439 |        "<matplotlib.figure.Figure at 0x12982215940>"
440 |       ]
441 |      },
442 |      "metadata": {},
443 |      "output_type": "display_data"
444 |     }
445 |    ],
446 |    "source": [
447 |     "#在训练集上的拟合结果\n",
448 |     "y_train_predict=model.predict(X_train)\n",
449 |     "y_train_predict=y_train_predict[:,0]\n",
450 |     "draw=pd.concat([pd.DataFrame(y_train),pd.DataFrame(y_train_predict)],axis=1)\n",
451 |     "draw.iloc[200:500,0].plot(figsize=(12,6))\n",
452 |     "draw.iloc[200:500,1].plot(figsize=(12,6))\n",
453 |     "plt.legend(('real', 'predict'),fontsize='15')\n",
454 |     "plt.title(\"Train Data\",fontsize='30') #添加标题"
455 |    ]
456 |   },
457 |   {
458 |    "cell_type": "code",
459 |    "execution_count": 9,
460 |    "metadata": {},
461 |    "outputs": [
462 |     {
463 |      "data": {
464 |       "text/plain": [
465 |        "Text(0.5,1,'Test Data')"
466 |       ]
467 |      },
468 |      "execution_count": 9,
469 |      "metadata": {},
470 |      "output_type": "execute_result"
471 |     },
472 |     {
473 |      "data": {
474 |       "image/png": 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\n",
475 |       "text/plain": [
476 |        "<matplotlib.figure.Figure at 0x129823393c8>"
477 |       ]
478 |      },
479 |      "metadata": {},
480 |      "output_type": "display_data"
481 |     }
482 |    ],
483 |    "source": [
484 |     "#在测试集上的预测\n",
485 |     "y_test_predict=model.predict(X_test)\n",
486 |     "y_test_predict=y_test_predict[:,0]\n",
487 |     "draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_test_predict)],axis=1);\n",
488 |     "draw.iloc[200:500,0].plot(figsize=(12,6))\n",
489 |     "draw.iloc[200:500,1].plot(figsize=(12,6))\n",
490 |     "plt.legend(('real', 'predict'),loc='upper right',fontsize='15')\n",
491 |     "plt.title(\"Test Data\",fontsize='30') #添加标题"
492 |    ]
493 |   },
494 |   {
495 |    "cell_type": "code",
496 |    "execution_count": 10,
497 |    "metadata": {},
498 |    "outputs": [
499 |     {
500 |      "name": "stdout",
501 |      "output_type": "stream",
502 |      "text": [
503 |       "训练集上的MAE/MSE/MAPE/涨跌准确率\n",
504 |       "0.024356071166775788\n",
505 |       "0.0009762876353679087\n",
506 |       "6.014338127336609\n",
507 |       "0.43560461870321027\n",
508 |       "测试集上的MAE/MSE/MAPE/涨跌准确率\n",
509 |       "0.010155834005463554\n",
510 |       "0.00015539240964995382\n",
511 |       "1.4232616557473812\n",
512 |       "0.4044173648134044\n"
513 |      ]
514 |     }
515 |    ],
516 |    "source": [
517 |     "#输出结果\n",
518 |     "def mape(y_true, y_pred):\n",
519 |     "    return np.mean(np.abs((y_pred - y_true) / y_true)) * 100\n",
520 |     "def up_down_accuracy(y_true, y_pred):\n",
521 |     "    y_var_test=y_true[1:]-y_true[:len(y_true)-1]#实际涨跌\n",
522 |     "    y_var_predict=y_pred[1:]-y_pred[:len(y_pred)-1]#原始涨跌\n",
523 |     "    txt=np.zeros(len(y_var_test))\n",
524 |     "    for i in range(len(y_var_test-1)):#计算数量\n",
525 |     "        txt[i]=np.sign(y_var_test[i])==np.sign(y_var_predict[i])\n",
526 |     "    result=sum(txt)/len(txt)\n",
527 |     "    return result\n",
528 |     "print('训练集上的MAE/MSE/MAPE/涨跌准确率')\n",
529 |     "print(mean_absolute_error(y_train_predict, y_train))\n",
530 |     "print(mean_squared_error(y_train_predict, y_train) )\n",
531 |     "print(mape(y_train_predict, y_train) )\n",
532 |     "print(up_down_accuracy(y_train_predict,y_train))\n",
533 |     "print('测试集上的MAE/MSE/MAPE/涨跌准确率')\n",
534 |     "print(mean_absolute_error(y_test_predict, y_test))\n",
535 |     "print(mean_squared_error(y_test_predict, y_test) )\n",
536 |     "print(mape(y_test_predict,  y_test) )\n",
537 |     "print(up_down_accuracy(y_test_predict,y_test))"
538 |    ]
539 |   },
540 |   {
541 |    "cell_type": "code",
542 |    "execution_count": null,
543 |    "metadata": {},
544 |    "outputs": [],
545 |    "source": []
546 |   },
547 |   {
548 |    "cell_type": "code",
549 |    "execution_count": null,
550 |    "metadata": {},
551 |    "outputs": [],
552 |    "source": []
553 |   },
554 |   {
555 |    "cell_type": "code",
556 |    "execution_count": null,
557 |    "metadata": {},
558 |    "outputs": [],
559 |    "source": []
560 |   }
561 |  ],
562 |  "metadata": {
563 |   "kernelspec": {
564 |    "display_name": "Python 3",
565 |    "language": "python",
566 |    "name": "python3"
567 |   },
568 |   "language_info": {
569 |    "codemirror_mode": {
570 |     "name": "ipython",
571 |     "version": 3
572 |    },
573 |    "file_extension": ".py",
574 |    "mimetype": "text/x-python",
575 |    "name": "python",
576 |    "nbconvert_exporter": "python",
577 |    "pygments_lexer": "ipython3",
578 |    "version": "3.6.4"
579 |   }
580 |  },
581 |  "nbformat": 4,
582 |  "nbformat_minor": 2
583 | }
584 | 


--------------------------------------------------------------------------------
/CNN-LSTM-Attention.py:
--------------------------------------------------------------------------------
  1 | 
  2 | # coding: utf-8
  3 | 
  4 | # In[1]:
  5 | 
  6 | 
  7 | #导入必要的库
  8 | import numpy as np
  9 | import matplotlib.pyplot as plt
 10 | import pandas as pd
 11 | from sklearn import preprocessing
 12 | from sklearn.metrics import mean_squared_error
 13 | from sklearn.metrics import mean_absolute_error
 14 | from math import sqrt
 15 | import math
 16 | from keras.layers import *
 17 | from keras.models import *
 18 | from keras.optimizers import Adam
 19 | 
 20 | 
 21 | # In[2]:
 22 | 
 23 | 
 24 | #设置LSTM的时间窗等参数
 25 | window=5
 26 | lstm_units = 16
 27 | dropout = 0.01
 28 | epoch=60
 29 | #读取数据
 30 | df1=pd.read_csv('data.csv') 
 31 | df1=df1.iloc[:,2:]
 32 | df1.tail()
 33 | 
 34 | 
 35 | # In[3]:
 36 | 
 37 | 
 38 | #进行数据归一化
 39 | from sklearn import preprocessing
 40 | min_max_scaler = preprocessing.MinMaxScaler()
 41 | df0=min_max_scaler.fit_transform(df1)
 42 | df = pd.DataFrame(df0, columns=df1.columns)
 43 | input_size=len(df.iloc[1,:])
 44 | 
 45 | 
 46 | # In[4]:
 47 | 
 48 | 
 49 | #构建lstm输入
 50 | stock=df
 51 | seq_len=window
 52 | amount_of_features = len(stock.columns)#有几列
 53 | data = stock.as_matrix() #pd.DataFrame(stock) 表格转化为矩阵
 54 | sequence_length = seq_len + 1#序列长度
 55 | result = []
 56 | for index in range(len(data) - sequence_length):#循环数据长度-sequence_length次
 57 |     result.append(data[index: index + sequence_length])#第i行到i+sequence_length
 58 | result = np.array(result)#得到样本，样本形式为6天*3特征
 59 | row = round(0.9 * result.shape[0])#划分训练集测试集
 60 | train = result[:int(row), :]
 61 | x_train = train[:, :-1]
 62 | y_train = train[:, -1][:,-1]
 63 | x_test = result[int(row):, :-1]
 64 | y_test = result[int(row):, -1][:,-1]
 65 | #reshape成 6天*3特征
 66 | X_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], amount_of_features))
 67 | X_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], amount_of_features))  
 68 | print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
 69 | 
 70 | 
 71 | # In[5]:
 72 | 
 73 | 
 74 | #建立LSTM模型 训练
 75 | inputs=Input(shape=(window, input_size))
 76 | model=Conv1D(filters = lstm_units, kernel_size = 1, activation = 'sigmoid')(inputs)#卷积层
 77 | model=MaxPooling1D(pool_size = window)(model)#池化层
 78 | model=Dropout(dropout)(model)#droupout层
 79 | model=Bidirectional(LSTM(lstm_units, activation='tanh'), name='bilstm')(model)#双向LSTM层
 80 | attention=Dense(lstm_units*2, activation='sigmoid', name='attention_vec')(model)#求解Attention权重
 81 | model=Multiply()([model, attention])#attention与LSTM对应数值相乘
 82 | 
 83 | outputs = Dense(1, activation='tanh')(model)
 84 | model = Model(inputs=inputs, outputs=outputs)
 85 | model.compile(loss='mse',optimizer='adam',metrics=['accuracy'])
 86 | model.summary()#展示模型结构
 87 | 
 88 | 
 89 | # In[6]:
 90 | 
 91 | 
 92 | history=model.fit(X_train, y_train, nb_epoch = epoch, batch_size = 256,shuffle=False,validation_data=(X_test, y_test)) #训练模型epoch次
 93 | 
 94 | 
 95 | # In[7]:
 96 | 
 97 | 
 98 | #迭代图像
 99 | loss = history.history['loss']
100 | val_loss = history.history['val_loss']
101 | epochs_range = range(epoch)
102 | plt.plot(epochs_range, loss, label='Train Loss')
103 | plt.plot(epochs_range, val_loss, label='Test Loss')
104 | plt.legend(loc='upper right')
105 | plt.title('Train and Val Loss')
106 | plt.show()
107 | 
108 | 
109 | # In[8]:
110 | 
111 | 
112 | #在训练集上的拟合结果
113 | y_train_predict=model.predict(X_train)
114 | y_train_predict=y_train_predict[:,0]
115 | draw=pd.concat([pd.DataFrame(y_train),pd.DataFrame(y_train_predict)],axis=1)
116 | draw.iloc[200:500,0].plot(figsize=(12,6))
117 | draw.iloc[200:500,1].plot(figsize=(12,6))
118 | plt.legend(('real', 'predict'),fontsize='15')
119 | plt.title("Train Data",fontsize='30') #添加标题
120 | 
121 | 
122 | # In[9]:
123 | 
124 | 
125 | #在测试集上的预测
126 | y_test_predict=model.predict(X_test)
127 | y_test_predict=y_test_predict[:,0]
128 | draw=pd.concat([pd.DataFrame(y_test),pd.DataFrame(y_test_predict)],axis=1);
129 | draw.iloc[200:500,0].plot(figsize=(12,6))
130 | draw.iloc[200:500,1].plot(figsize=(12,6))
131 | plt.legend(('real', 'predict'),loc='upper right',fontsize='15')
132 | plt.title("Test Data",fontsize='30') #添加标题
133 | 
134 | 
135 | # In[10]:
136 | 
137 | 
138 | #输出结果
139 | def mape(y_true, y_pred):
140 |     return np.mean(np.abs((y_pred - y_true) / y_true)) * 100
141 | def up_down_accuracy(y_true, y_pred):
142 |     y_var_test=y_true[1:]-y_true[:len(y_true)-1]#实际涨跌
143 |     y_var_predict=y_pred[1:]-y_pred[:len(y_pred)-1]#原始涨跌
144 |     txt=np.zeros(len(y_var_test))
145 |     for i in range(len(y_var_test-1)):#计算数量
146 |         txt[i]=np.sign(y_var_test[i])==np.sign(y_var_predict[i])
147 |     result=sum(txt)/len(txt)
148 |     return result
149 | print('训练集上的MAE/MSE/MAPE/涨跌准确率')
150 | print(mean_absolute_error(y_train_predict, y_train))
151 | print(mean_squared_error(y_train_predict, y_train) )
152 | print(mape(y_train_predict, y_train) )
153 | print(up_down_accuracy(y_train_predict,y_train))
154 | print('测试集上的MAE/MSE/MAPE/涨跌准确率')
155 | print(mean_absolute_error(y_test_predict, y_test))
156 | print(mean_squared_error(y_test_predict, y_test) )
157 | print(mape(y_test_predict,  y_test) )
158 | print(up_down_accuracy(y_test_predict,y_test))
159 | 
160 | 


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/README.md:
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1 | # CNN-LSTM-Attention
2 | 使用卷积神经网络-长短期记忆网络（bi-LSTM）-注意力机制对股票收盘价进行回归预测。The convolution neural network, short-term memory network and attention mechanism are used to predict the closing price.
3 | 


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