多层感知器(MLP)Keras张量流模型

Question

我在为训练调整模型后遇到了一个问题。下面是我的代码

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn import preprocessing
from tensorflow import keras
from keras.models import Sequential
from tensorflow.keras import layers



bitcoin_data = pd.read_csv("BitcoinHeistData.csv")
#first we'll need to normalize the dataset
normal = bitcoin_data
normalized_bitcoin_data=preprocessing.normalize(normal)

# make it into a dataframe
columns = bitcoin_data.columns
normalized_bitcoin_df = pd.DataFrame(normalized_bitcoin_data, columns=columns)
# start out splitting the data
xtrain = normalized_bitcoin_df
labels = normalized_bitcoin_df.drop('label', axis=1)

x, x_validate, y, y_validate = train_test_split(xtrain, labels, test_size=0.2, train_size=0.8)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.12, train_size=0.88)

*#This is my output for my variables so far. Exactly how I want to split it 70% - 20% - 10%
#X HERE SHAPE
#(838860, 10)
#x_test HERE SHAPE
#(100664, 10)
#x_validate HERE SHAPE
#(209715, 10)
#X x_train SHAPE
#(738196, 10)
#y HERE SHAPE
#(838860, 9)
#y_test HERE SHAPE
#(100664, 9)
#X y_validate SHAPE
#(209715, 9)
#X y_train SHAPE
#(738196, 9)*
model = Sequential()
model.add(layers.Dense(64, activation='relu', kernel_initializer='glorot_normal', 
bias_initializer='zeros', input_shape=(128,)))
model.add(layers.BatchNormalization())
model.add(layers.Dense(32, activation='relu', kernel_initializer='glorot_normal', 
bias_initializer='zeros'))
model.add(layers.BatchNormalization())
model.add(layers.Dense(32, activation='relu', kernel_initializer='glorot_normal', 
bias_initializer='zeros'))
model.add(layers.Dense(32, activation='relu', kernel_initializer='glorot_normal', 
bias_initializer='zeros'))
model.add(layers.Dropout(0.4))
model.add(layers.Dense(10, activation='softmax'))
optimizer = keras.optimizers.RMSprop(lr=0.0005, rho=0)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train, epochs=20, batch_size=128)

#当我运行x_train和y_train的模型.fit时，我得到了这个错误ValueError。我不明白怎么回事尽管如此，还是要避开它。任何帮助都是先决条件

#ValueError:层序列的输入0与层不兼容：应为的轴-1输入形状具有值128，但接收到形状为[无，10]的输入

Answer 1

输入层中的神经元数量(input_shape属性(必须等于x_train数据集的列数(x_train.shape[1](。输出层中的神经细胞数量也必须等于y_train(y_train.shape[1](的列数。