AttributeError: 'Funktion' Objekt hat kein Attribut 'Anpassung' ⇐ Python

[Guest]

Ich fange gerade erst mit Deep Learning und Python an und bin bereits an diesem Fehler festgehalten, wenn ich versuche, das Modell zu trainieren. , aber ich habe offensichtlich einige Grundlagen nicht erfasst. und prognostizieren Sequenzen von 5 Werten. />
Können Sie mir helfen zu verstehen, was ich verpasse? >

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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)