AttributeError: 'function' 对象没有属性 'fit' [英] AttributeError: 'function' object has no attribute 'fit'

问题描述

我刚刚开始使用深度学习和 Python，当我尝试训练模型时，我已经遇到了这个错误.我认为将基本构建块放在一起会是一个简单的开始项目，但显然我还没有掌握一些基础知识.我的目标是在包含 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.>

我正在从我生成的 csv 文件中读取数据集，它按预期显示.

你能帮我理解我错过了什么吗?一如既往地非常感谢.这是我到目前为止编写的代码:

将 numpy 导入为 np导入 matplotlib.pyplot 作为 plt将熊猫导入为 pd导入操作系统从 sklearn.preprocessing 导入 MinMaxScaler从集合导入双端队列随机导入# 加载数据集df = pd.read_csv('DataSet.csv',delimiter=',',usecols=['Wheel','Date','1ex','2ex','3ex','4ex','5ex'])# 把它分成几部分times = sorted(df.index.values) # 获取时间last_10pct = sorted(df.index.values)[-int(0.1*len(times))] # 获取最后 10% 的次数last_20pct = sorted(df.index.values)[-int(0.2*len(times))] # 获取最后 20% 的次数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)] # 现在 train_df 是最后 20% 的所有数据# 删除日期"列train_df.drop(columns=["Date"], inplace=True)validation_df.drop(columns=["Date"], inplace=True)test_df.drop(columns=["Date"], inplace=True)# 该模型从 tensorflow.keras.models 导入顺序从 tensorflow.keras.layers 导入密集# 从 tensorflow.keras.layers 导入 LSTM从 tensorflow.keras.wrappers.scikit_learn 导入 KerasRegressor从 sklearn.model_selection 导入 cross_val_score从 sklearn.model_selection 导入 KFold从 sklearn.preprocessing 导入 StandardScaler从 sklearn.pipeline 导入管道# 定义基础模型定义基线模型():# scale = StandardScaler()# 创建模型模型 = 顺序()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))# 编译模型# model.compile(loss='mean_absolute_error', optimizer='adam')model.compile(loss='mean_squared_error', 优化器='adam')#model.fit(train_df, epochs = 5)回报模式# 训练模型基线模型.fit(train_df，batch_size=1，epochs=200，verbose=1)

解决方案

如果你写baseline_model，它返回的是函数，而不是结果.

因此baseline_model.fit不能被调用，因为'function'对象没有属性'fit'

您必须执行该函数以获取其结果，使用括号 - baseline_model() - 然后将对结果执行 fit.;)

<小时>

tl;博士:

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

I'm just starting with deep learning and python and I'm already stuck with this error when I try to train the model. I thought it would be an easy starting project to get together the basic building blocks, but I obviously haven't grasped some basics.. My goal is to train a model on a data set of 5 column values '1ex','2ex','3ex','4ex','5ex' and predict sequences of 5 values.

I'm reading the dataset from a csv file i generated and it's displaying as expected.

Can you help me understanding what I'm missing out? Thank you very much as always. This is the code I've written so far:

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)

解决方案

If you write baseline_model, it returns the function, not the result.

Therefore baseline_model.fit can't be called because 'function' object has no attribute 'fit'

You must execute the function to get its result, using parentheses - baseline_model() - and then fit will be performed on the result. ;)

---

tl;dr:

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

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