Question

我在下面的代码中有错误，第二部分的代码有错误，第一部分我声明了我的数据集、图层等。

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns

data=pd.read_excel('/content/dataset.xlsx')
data.head()

data.plot(kind='scatter', x='fiyat', y='yil',alpha = 0.5,color = 'red')
plt.xlabel('price')              # label = name of label
plt.ylabel('year')
plt.title('Fiyat ve yil Scatter Plot') 

data.plot(kind='scatter', x='fiyat', y='km',alpha = 0.5,color = 'grey')
plt.xlabel('price')              # label = name of label
plt.ylabel('km')
plt.title('Fiyat ve km Scatter Plot') 
data.plot(kind='scatter', x='fiyat', y='motor_gucu_hp',alpha = 0.5,color = 'green')
plt.xlabel('price')              # label = name of label
plt.ylabel('machine power')
plt.title('fiyat ve motor_gucu_hp Scatter Plot') 

# Importing the dataset
X = data.iloc[:, data.columns != 'fiyat']
y = data.fiyat

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.preprocessing import StandardScaler
from matplotlib import pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split

# define base model
def baseline_model():
    # create model
    model = Sequential()
    model.add(Dense(30, input_dim=120, kernel_initializer='normal', activation='relu'))
    model.add(Dense(120, activation = 'relu'))
    model.add(Dense(120, activation = 'relu'))
    model.add(Dense(1, kernel_initializer='normal'))
    # Compile model
    model.compile(loss='mse',
                optimizer='adam',
                metrics=['mae'] )
    return model

model = baseline_model()
model.summary()

<块引用>

并在此处出错；在model.fit位置

import tensorflow as tf
from tensorflow import keras
import numpy as np


# Display training progress by printing a single dot for each completed epoch
EPOCHS = 500

# Store training stats
history = model.fit(X_train, y_train, epochs=EPOCHS,
                    batch_size=16, verbose=0)

ValueError: 层序列 2 的输入 0 与层不兼容：输入形状的预期轴 -1 具有值 120，但收到的输入形状为 (None, 47)

出现这样的错误，你能帮忙吗？我能做什么。

ValueError: 层序列 2 的输入 0 与层不兼容：

0 个答案: