Question

我只是从深度学习和python开始，当我尝试训练模型时，我已经遇到了这个错误。我以为将基本构建模块放在一起将是一个容易的开始项目，但是我显然还没有掌握一些基本知识。我的目标是在5个列值'1ex','2ex','3ex','4ex','5ex'的数据集上训练模型并预测5个值的序列。

我正在从生成的csv文件中读取数据集，并按预期显示。

您能帮助我了解我遗漏的内容吗？一如既往地非常感谢您。这是我到目前为止编写的代码：

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os 
from sklearn.preprocessing import MinMaxScaler

from collections import deque
import random

# load the data set
df = pd.read_csv('DataSet.csv',delimiter=',',usecols=['Wheel','Date','1ex','2ex','3ex','4ex','5ex'])

# divide it into portions

times = sorted(df.index.values)  # get the times
last_10pct = sorted(df.index.values)[-int(0.1*len(times))]  # get the last 10% of the times
last_20pct = sorted(df.index.values)[-int(0.2*len(times))]  # get the last 20% of the times

test_df = df[(df.index >= last_10pct)]
validation_df = df[(df.index >= last_20pct) & (df.index < last_10pct)]  
train_df = df[(df.index < last_20pct)]  # now the train_df is all the data up to the last 20%

# drop 'Date' column
train_df.drop(columns=["Date"], inplace=True)
validation_df.drop(columns=["Date"], inplace=True)
test_df.drop(columns=["Date"], inplace=True)

# the model

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
# from tensorflow.keras.layers import LSTM
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# define base model

def baseline_model():
#   scale = StandardScaler()
    # create model
    model = Sequential()
    model.add(Dense(5, input_dim=5, kernel_initializer='normal', activation='relu'))
    model.add(Dense(15, kernel_initializer='normal', activation='relu'))
    model.add(Dense(15, kernel_initializer='normal', activation='relu'))
    model.add(Dense(5, kernel_initializer='normal', activation = softmax))
    # Compile model
#   model.compile(loss='mean_absolute_error', optimizer='adam')
    model.compile(loss='mean_squared_error', optimizer='adam')
#   model.fit(train_df, epochs = 5)
    return model

# train the model

baseline_model.fit(train_df, batch_size=1, epochs=200, verbose=1)

Answer 1

如果您写baseline_model，它将返回函数，而不是结果。

因此baseline_model.fit因为'function' object has no attribute 'fit'不能被调用

必须使用括号-baseline_model()-来执行该函数以获取结果，然后对结果执行fit。 ;）

tl; dr：

baseline_model.fit(-> baseline_model().fit(

AttributeError：“功能”对象没有属性“适合”

1 个答案: