Question

我正在尝试使用LSTM预测股价，但遇到以下错误

这是我的代码：


public class BookDetailsBean {
    private int bookingid;
    private String name;
    private String email;
    private Long mobilenumber;
    private String address;
    private String evnets;
    private int nooftickets;




    public int getBookingid() {
        return bookingid;
    }
    public void setBookingid(int bookingid) {
        this.bookingid = bookingid;
    }
    public String getName() {
        return name;
    }
    public void setName(String name) {
        this.name = name;
    }
    public String getEmail() {
        return email;
    }
    public void setEmail(String email) {
        this.email = email;
    }
    public Long getMobilenumber() {
        return mobilenumber;
    }
    public void setMobilenumber(Long mobilenumber) {
        this.mobilenumber = mobilenumber;
    }
    public String getAddress() {
        return address;
    }
    public void setAddress(String address) {
        this.address = address;
    }
    public String getEvnets() {
        return evnets;
    }
    public void setEvnets(String evnets) {
        this.evnets = evnets;
    }
    public int getNooftickets() {
        return nooftickets;
    }
    public void setNooftickets(int nooftickets) {
        this.nooftickets = nooftickets;
    }

}```



But booking id is not incrementing, why?

这是我遇到的错误：

from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
import lstm, time

X_train, X_test, Y_train, Y_test = lstm.load_data('tata.csv', 50, True)

#build model
model = Sequential()

mode.add(LSTM(
    input_dim=1,
    output_dim=50,
    return_sequences=True))
model.add(Dropout(0.2))

model.add(LSTM(
    100,
    return_sequences=False))
model.add(Dropout(0.2))

model.add(Dense(
    output_dim=1,))
model.add(Activation('linear'))

start = time.time()
model.compile(loss='mse', optimizer='rmsprop')
print("compilation time: ", time.time - start)

#train the model
model.fit(
    X_train,
    Y_train,
    batch_size=512,
    nb_epoch=1,
    validation_split=0.05)

#predicting the prices
predictions = lstm.predict_sequences_multiple(model, X_test, 50, 50)
lstm.plot_results_multiple(predictions, Y_test, 50)

this is the full image

我无法理解问题出在哪里。请查看我链接的图片，以更好地理解。

Answer 1

当模型输入形状和数据传递形状不同时，会发生此错误。

下面是一个示例，其中我使用了您的代码，但是使用了不同的输入文件（因为我无法import lstm），并且使用MinMaxScaler进行了一些预处理以缩放输入，将数据集拆分为{{ 1}}和Xtrain，最后将Ytrain转换为list。

我在代码中添加了np.array以适应模型中的X_train = X_train[:,:,np.newaxis]形状。如果没有此行，模型将抛出X_train。

完整代码-

ValueError: Error when checking input: expected lstm_13_input to have 3 dimensions, but got array with shape (1975, 50)

输出-

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
import time

url = 'https://raw.githubusercontent.com/mwitiderrick/stockprice/master/NSE-TATAGLOBAL.csv'
dataset_train = pd.read_csv(url)
training_set = dataset_train.iloc[:, 1:2].values

dataset_train.head()

from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(0,1))
training_set_scaled = sc.fit_transform(training_set)

X_train = []
y_train = []
for i in range(60, 2035):
  X_train.append(training_set_scaled[i-50:i, 0])
  y_train.append(training_set_scaled[i, 0])

X_train, Y_train = np.array(X_train), np.array(y_train)
X_train = X_train[:,:,np.newaxis]

#build model
model = Sequential()

model.add(LSTM(
    input_dim=1,
    output_dim=50,
    return_sequences=True))
model.add(Dropout(0.2))

model.add(LSTM(
    100,
    return_sequences=False))
model.add(Dropout(0.2))

model.add(Dense(
    output_dim=1,))
model.add(Activation('linear'))

#model.summary()

start = time.time()
model.compile(loss='mse', optimizer='rmsprop')
print("compilation time: ", time.time() - start)

#train the model
model.fit(
    X_train,
    Y_train,
    batch_size=512,
    nb_epoch=1,
    validation_split=0.05)

希望这能回答您的问题。谢谢。

ValueError：检查目标时出错：预期activation_10具有2个维，但数组的形状为（118、50、1）

1 个答案: