title: AttributeError: 'function' object has no attribute 'fit'
body: <p>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 <code>'1ex','2ex','3ex','4ex','5ex'</code> and predict sequences of 5 values.</p>
  
  <p>I'm reading the dataset from a csv file i generated and it's displaying as expected.</p>
  
  <p>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: </p>
  
  <pre><code>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 &gt;= last_10pct)]
  validation_df = df[(df.index &gt;= last_20pct) &amp; (df.index &lt; last_10pct)]  
  train_df = df[(df.index &lt; 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)
  </code></pre>