Question

这是一种经常性的神经网络LSTM模型，旨在预测外汇市场运动的未来价值。

数据集形状为（1713,50），第一列是日期时间索引，其他列是数值。但是在打印训练数据和验证数据后立即形成错误开始。

当我尝试实现此代码时：

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

df = pd.read_csv(r"E:\Business\Stocks\StocksDF.csv", parse_dates=[0], index_col=[0], low_memory=False, dtype='object')
features = len(df.columns)
val_ratio = 0.2
epochs = 500
batch_size = df.__len__()
sequence_length = 822

data = df.as_matrix()
data_processed = []
for index in range(len(data) - sequence_length):
    data_processed.append(data[index: index + sequence_length])
data_processed = np.array(data_processed)

val_split = round((1 - val_ratio) * data_processed.shape[0])
train = data_processed[:, int(val_split), :]
val = data_processed[int(val_split):, :]

print('Training data: {}'.format(train.shape))
print('Validation data: {}'.format(val.shape))



train_samples, train_nx, train_ny = train.shape
val_samples, val_nx, val_ny = val.shape

train = train.reshape((train_samples, train_nx * train_ny))
val = val.reshape((val_samples, val_nx * val_ny))

preprocessor = MinMaxScaler().fit(train)
train = preprocessor.transform(train)
val = preprocessor.transform(val)

train = train.reshape((train_samples, train_nx, train_ny))
val = val.reshape((val_samples, val_nx, val_ny))

X_train = train[:, : -1]
y_train = train[:, -1][:, -1]
X_val = val[:, : -1]
y_val = val[:, -1][:, -1]

X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], features))
X_val = np.reshape(X_val, (X_val.shape[0], X_val.shape[1], features))

model = Sequential()
model.add(LSTM(input_shape=(X_train.shape[1:]), units=100, return_sequences=True))
model.add(Dropout(0.5))
model.add(LSTM(2, return_sequences=False))
model.add(Dropout(0.25))
model.add(Dense(units=1))
model.add(Activation("relu"))

model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mae', 'mse', 'accuracy'])

history = model.fit(
    X_train,
    y_train,
    batch_size=batch_size,
    epochs=epochs,
    verbose=2)

preds_val = model.predict(X_val)
diff = []
for i in range(len(y_val)):
    pred = preds_val[i][0]
    diff.append(y_val[i] - pred)

real_min = preprocessor.data_min_[104]
real_max = preprocessor.data_max_[104]
print(preprocessor.data_min_[:1])
print(preprocessor.data_max_[:1])

preds_real = preds_val * (real_max - real_min) + real_min
y_val_real = y_val * (real_max - real_min) + real_min

plt.plot(preds_real, label='Predictions')
plt.plot(y_val_real, label='Actual values')
plt.xlabel('test')
plt.legend(loc=0)
plt.show()
print(model.summary())

我收到了这个错误：

使用TensorFlow后端。

追踪（最近一次呼叫最后一次）：

训练数据：（891,50）

文件＆＃34; E：/Tutorial/new.py" ;,第31行，in 验证数据：（178,822,50）

train_samples，train_nx，train_ny = train.shape

ValueError：没有足够的值来解包（预期3，得到2）

Answer 1

这一行出现错误：

train = data_processed[:, int(val_split), :]

应该是：

train = data_processed[:int(val_split), :, :]
val = data_processed[int(val_split):, :, :]

python RNN LSTM错误

1 个答案: