python代码出错

Question

这是我的代码，出错了，应该要怎么改正，把改正后的完整代码写出

import pandas as pd
from collections import deque
import numpy as np
from tensorflow.keras.callbacks import ModelCheckpoint
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, Conv1D, MaxPooling1D, Flatten

def parse_date(date_string):
    return pd.Timestamp(date_string.replace('_', '-'))

df = pd.read_csv('D:/LSTMdata.csv', index_col='Date', parse_dates=True, date_parser=parse_date)
df.sort_index(inplace=True)

def Stock_Price_LSTM_Data_Precesing(df, mem_his_days, pre_days):
    df.dropna(inplace=True)
    df.sort_index(inplace=True)
    df['label'] = df['Close'].shift(-pre_days)
    scaler = StandardScaler()
    sca_X = scaler.fit_transform(df.iloc[:, :-1])

    deq = deque(maxlen=mem_his_days)
    X = []
    for i in sca_X:
        deq.append(list(i))
        if len(deq) == mem_his_days:
            X.append(list(deq))
    X_lately = X[-pre_days:]
    X = X[:-pre_days]
    y = df['label'].values[mem_his_days-1:-pre_days]

    X = np.array(X)
    y = np.array(y)
    return X, y, X_lately

X, y, X_lately = Stock_Price_LSTM_Data_Precesing(df, 5, 10)
print(len(X))
print(len(y))
print(len(X_lately))

pre_days = 10
mem_days = [5, 10, 15]
lstm_layers = [1, 2, 3]
dense_layers = [1, 2, 3]
units = [16, 32]

for the_mem_days in mem_days:
    for the_lstm_layers in lstm_layers:
        for the_dense_layers in dense_layers:
            for the_units in units:
                filepath = f"./theLSTMbestmodel1/{{val_mape:.2f}}{{epoch:02d}}men{the_mem_days}lstm{the_lstm_layers}dense{the_dense_layers}unit{the_units}.keras"
                checkpoint = ModelCheckpoint(
                    filepath=filepath,
                    save_weights_only=False,
                    monitor='val_mape',
                    mode='min',
                    save_best_only=True)
                X, y, X_lately = Stock_Price_LSTM_Data_Precesing(df, the_mem_days, pre_days)
                X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=False, test_size=0.1)

                model = Sequential()
                model.add(Conv1D(filters=32, kernel_size=3, activation='relu'))
                model.add(MaxPooling1D(pool_size=2))
                model.add(Flatten())
                model.add(LSTM(the_units, input_shape=X.shape[1:], activation='relu', return_sequences=True))
                model.add(Dropout(0.1))
                for i in range(the_lstm_layers):
                    model.add(LSTM(the_units, activation='relu', return_sequences=True))
                    model.add(Dropout(0.1))

                model.add(LSTM(the_units, activation='relu'))
                model.add(Dropout(0.1))
                for i in range(the_dense_layers):
                    model.add(Dense(the_units, activation='relu'))
                    model.add(Dropout(0.1))

                model.add(Dense(1))
                model.compile(optimizer='adam', loss='mse', metrics=['mape'])
                model.fit(X_train, y_train, batch_size=32, epochs=50, validation_data=(X_test, y_test), callbacks=[checkpoint])

这是出错原因

C:\Users\JAY\AppData\Local\Temp\ipykernel_15564\498793550.py:14: FutureWarning: The argument 'date_parser' is deprecated and will be removed in a future version. Please use 'date_format' instead, or read your data in as 'object' dtype and then call 'to_datetime'.
  df = pd.read_csv('D:/LSTMdata.csv', index_col='Date', parse_dates=True, date_parser=parse_date)
D:\anaconda\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(**kwargs)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[3], line 81
     79 model.add(Dense(1))
     80 model.compile(optimizer='adam', loss='mse', metrics=['mape'])
---> 81 model.fit(X_train, y_train, batch_size=32, epochs=50, validation_data=(X_test, y_test), callbacks=[checkpoint])

File D:\anaconda\Lib\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
    119     filtered_tb = _process_traceback_frames(e.__traceback__)
    120     # To get the full stack trace, call:
    121     # `keras.config.disable_traceback_filtering()`
--> 122     raise e.with_traceback(filtered_tb) from None
    123 finally:
    124     del filtered_tb

File D:\anaconda\Lib\site-packages\keras\src\layers\input_spec.py:186, in assert_input_compatibility(input_spec, inputs, layer_name)
    184 if spec.ndim is not None and not spec.allow_last_axis_squeeze:
    185     if ndim != spec.ndim:
--> 186         raise ValueError(
    187             f'Input {input_index} of layer "{layer_name}" '
    188             "is incompatible with the layer: "
    189             f"expected ndim={spec.ndim}, found ndim={ndim}. "
    190             f"Full shape received: {shape}"
    191         )
    192 if spec.max_ndim is not None:
    193     if ndim is not None and ndim > spec.max_ndim:

ValueError: Input 0 of layer "lstm" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 32)

Answer 1

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根据您提供的错误信息，看起来有两处需要修改您的代码。

首先，对于FutureWarning，您可以按照警告提示，将date_parser参数替换为date_format。在读取CSV文件时，增加date_format='%Y%m%d'（假设日期格式是'YYYYMMDD'）。

其次，错误信息提到输入维度不匹配的问题。这可能是因为您没有正确设置LSTM层的输入形状。LSTM层期望一个三维的数据输入，其中第一维是批次大小（batch size），第二维是时间步长（timesteps），第三维是特征维度（features）。但在您的代码中，X的形状可能是(num_samples, num_features)。为了解决这个问题，您需要在预处理数据时添加一个额外的时间步长维度。可以通过将X = np.expand_dims(X, axis=1)添加到X = np.array(X)之后来解决此问题。

下面是修改后的代码：

import pandas as pd
from collections import deque
import numpy as np
from tensorflow.keras.callbacks import ModelCheckpoint
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Dropout, Conv1D, MaxPooling1D, Flatten

def parse_date(date_string):
    return pd.Timestamp(date_string.replace('_', '-'))

df = pd.read_csv('D:/LSTMdata.csv', index_col='Date', parse_dates=True, date_format='%Y%m%d')
df.sort_index(inplace=True)

def Stock_Price_LSTM_Data_Precesing(df, mem_his_days, pre_days):
    df.dropna(inplace=True)
    df.sort_index(inplace=True)
    df['label'] = df['Close'].shift(-pre_days)
    scaler = StandardScaler()
    sca_X = scaler.fit_transform(df.iloc[:, :-1])

    deq = deque(maxlen=mem_his_days)
    X = []
    for i in sca_X:
        deq.append(list(i))
        if len(deq) == mem_his_days:
            X.append(list(deq))
    X_lately = X[-pre_days:]
    X = X[:-pre_days]
    y = df['label'].values[mem_his_days-1:-pre_days]

    X = np.array(X)
    X = np.expand_dims(X, axis=1)  # 添加时间步长维度
    y = np.array(y)
    return X, y, X_lately

X, y, X_lately = Stock_Price_LSTM_Data_Precesising(df, 5, 10)
print(len(X))
print(len(y))
print(len(X_lately))

pre_days = 10
mem_days = [5, 10, 15]
lstm_layers = [1, 2, 3]
dense_layers = [1, 2, 3]
units = [16, 32]

for the_mem_days in mem_days:
    for the_lstm_layers in lstm_layers:
        for the_dense_layers in dense_layers:
            for the_units in units:
                filepath = f"./theLSTMbestmodel1/{{val_mape:.2f}}{{epoch:02d}}men{the_mem_days}lstm{the_lstm_layers}dense{the_dense_layers}unit{the_units}.keras"
                checkpoint = ModelCheckpoint(
                    filepath=filepath,
                    save_weights_only=False,
                    monitor='val_mape',
                    mode='min',
                    save_best_only=True)
                X, y, X_lately = Stock_Price_LSTM_Data_Precesing(df, the_mem_days, pre_days)
                X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=False, test_size=0.1)

                model = Sequential()
                model.add(Conv1D(filters=32, kernel_size=3, activation='relu'))
                model.add(MaxPooling1D(pool_size=2))
                model.add(Flatten())
                model.add(LSTM(the_units, input_shape=(X.shape[1], X.shape[2]), activation='relu', return_sequences=True))
                model.add(Dropout(0.1))
                for i in range(the_lstm_layers):
                    model.add(LSTM(the_units, activation='relu', return_sequences=True))
                    model.add(Dropout(0.1))

                model.add(LSTM(the_units, activation='relu'))
                model.add(Dropout(0.1))
                for i in range(the_dense_layers):
                    model.add(Dense(the_units, activation='relu'))
                    model.add(Dropout(0.1))

                model.add(Dense(1))
                model.compile(optimizer='adam', loss='mse', metrics=['mape'])
                model.fit(X_train, y_train, batch_size=32, epochs=50, validation_data=(X_test, y_test), callbacks=[checkpoint])

这段代码应该能解决您遇到的问题，并运行无误。注意，我还更新了LSTM层中的input_shape参数，确保其与X的形状匹配。