在 Python 中训练后，神经网络没有给出预期的输出
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【中文标题】在 Python 中训练后，神经网络没有给出预期的输出【英文标题】：Neural network is not giving the expected output after training in Python 【发布时间】：2020-03-14 01:09:57 【问题描述】：我的神经网络在 Python 训练后没有给出预期的输出。代码中是否有任何错误？有什么方法可以减少均方误差（MSE）？
我尝试反复训练（运行程序）网络，但它没有学习，而是给出相同的 MSE 和输出。
这是我使用的数据：
https://drive.google.com/open?id=1GLm87-5E_6YhUIPZ_CtQLV9F9wcGaTj2
这是我的代码：
#load and evaluate a saved model
from numpy import loadtxt
from tensorflow.keras.models import load_model
# load model
model = load_model('ANNnew.h5')
# summarize model.
model.summary()
#Model starts
import numpy as np
import pandas as pd
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.models import Sequential
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
# Importing the dataset
X = pd.read_excel(r"C:\filelocation\Data.xlsx","Sheet1").values
y = pd.read_excel(r"C:\filelocation\Data.xlsx","Sheet2").values
# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.08, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Initialising the ANN
model = Sequential()
# Adding the input layer and the first hidden layer
model.add(Dense(32, activation = 'tanh', input_dim = 4))
# Adding the second hidden layer
model.add(Dense(units = 18, activation = 'tanh'))
# Adding the third hidden layer
model.add(Dense(units = 32, activation = 'tanh'))
#model.add(Dense(1))
model.add(Dense(units = 1))
# Compiling the ANN
model.compile(optimizer = 'adam', loss = 'mean_squared_error')
# Fitting the ANN to the Training set
model.fit(X_train, y_train, batch_size = 100, epochs = 1000)
y_pred = model.predict(X_test)
for i in range(5):
print('%s => %d (expected %s)' % (X[i].tolist(), y_pred[i], y[i].tolist()))
plt.plot(y_test, color = 'red', label = 'Test data')
plt.plot(y_pred, color = 'blue', label = 'Predicted data')
plt.title('Prediction')
plt.legend()
plt.show()
# save model and architecture to single file
model.save("ANNnew.h5")
print("Saved model to disk")
【问题讨论】：
> 神经网络在 python 训练后没有给出预期的输出。那是什么意思？您希望看到什么以及您观察到的错误是什么？ @papanito 正确回答问题。 你的目标域真的是连续的吗，它是否带有高斯噪声（MSE 目标的假设）？如果您只是预测几个值之一，将模型训练为分类器可能更合适... @dedObed 目标域是离散的。范围为 [1, 7]。 @dedObed 如何建模为分类器。，你有什么想法吗？请详细说明 【参考方案1】：我注意到您在通过印刷品进行报告时存在一个小错误 - 而不是：
for i in range(5):
print('%s => %d (expected %s)' % (X[i].tolist(), y_pred[i], y[i].tolist()))
你应该有：
for i in range(len(y_test)):
print('%s => %d (expected %s)' % (X[i].tolist(), y_pred[i], y_test[i].tolist()))
在此打印中，您最终将比较测试的预测与测试的真实（之前您将测试的预测与数组 y 中前 5 个观察值的真实进行比较），以及测试中的所有 6 个观察结果，而不仅仅是 5 个 :-)
您还应该监控火车数据的模型质量。为了清楚起见，非常简单：
-
您应该尝试使用神经网络 (NN) 过度拟合训练数据；如果您甚至无法使用 NN 过度拟合训练数据，那么 NN 可能在当前状态下对您的问题的解决方案令人失望；在这种情况下，您需要寻找其他功能（也将在下面提到）、更改模型质量指标或只是接受归因于正在准备的解决方案的预测质量限制；
确保过拟合训练数据是可能的或接受预测质量的限制，您的目标是找到可以泛化的最佳模型；监控模型的训练和测试质量至关重要；可泛化模型是在训练数据和有效数据上表现相似的模型；为了找到最好的泛化模型，您可以：
寻找有价值的特征（您拥有的数据转换或其他数据源）
玩转 NN 架构
玩弄 NN 估计过程
一般来说，为了实现找到可以泛化的最佳 NN 的最终目标，在 model.fit 调用中使用 validation_split 或 validation_data 是一种很好的做法。
进口
# imports
import numpy as np
import pandas as pd
import os
import tensorflow as tf
import matplotlib.pyplot as plt
import random
from tensorflow.keras.layers import Dense, Activation
from tensorflow.keras.models import Sequential
from tensorflow import set_random_seed
from tensorflow.keras.initializers import glorot_uniform
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
from importlib import reload
有用的功能
# useful pandas display settings
pd.options.display.float_format = ':.3f'.format
# useful functions
def plot_history(history, metrics_to_plot):
"""
Function plots history of selected metrics for fitted neural net.
"""
# plot
for metric in metrics_to_plot:
plt.plot(history.history[metric])
# name X axis informatively
plt.xlabel('epoch')
# name Y axis informatively
plt.ylabel('metric')
# add informative legend
plt.legend(metrics_to_plot)
# plot
plt.show()
def plot_fit(y_true, y_pred, title='title'):
"""
Function plots true values and predicted values, sorted in increase order by true values.
"""
# create one dataframe with true values and predicted values
results = y_true.reset_index(drop=True).merge(pd.DataFrame(y_pred), left_index=True, right_index=True)
# rename columns informartively
results.columns = ['true', 'prediction']
# sort for clarity of visualization
results = results.sort_values(by=['true']).reset_index(drop=True)
# plot true values vs predicted values
results.plot()
# adding scatter on line plots
plt.scatter(results.index, results.true, s=5)
plt.scatter(results.index, results.prediction, s=5)
# name X axis informatively
plt.xlabel('obs sorted in ascending order with respect to true values')
# add customizable title
plt.title(title)
# plot
plt.show();
def reset_all_randomness():
"""
Function assures reproducibility of NN estimation results.
"""
# reloads
reload(tf)
reload(np)
reload(random)
# seeds - for reproducibility
os.environ['PYTHONHASHSEED']=str(984797)
random.seed(984797)
set_random_seed(984797)
np.random.seed(984797)
my_init = glorot_uniform(seed=984797)
return my_init
从文件中加载 X 和 y
X = pd.read_excel(r"C:\filelocation\Data.xlsx","Sheet1").values
y = pd.read_excel(r"C:\filelocation\Data.xlsx","Sheet2").values
将 X 和 y 拆分为训练集和测试集
# reset_all_randomness - for reproducibility
my_init = reset_all_randomness()
# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.08, random_state = 0)
特征缩放
# Feature Scaling
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
Model0 - 尝试过拟合训练数据并验证过拟合
# reset_all_randomness - for reproducibility
my_init = reset_all_randomness()
# model0
# Initialising the ANN
model0 = Sequential()
# Adding 1 hidden layer: the input layer and the first hidden layer
model0.add(Dense(units = 128, activation = 'tanh', input_dim = 4, kernel_initializer=my_init))
# Adding 2 hidden layer
model0.add(Dense(units = 64, activation = 'tanh', kernel_initializer=my_init))
# Adding 3 hidden layer
model0.add(Dense(units = 32, activation = 'tanh', kernel_initializer=my_init))
# Adding 4 hidden layer
model0.add(Dense(units = 16, activation = 'tanh', kernel_initializer=my_init))
# Adding output layer
model0.add(Dense(units = 1, kernel_initializer=my_init))
# Set up Optimizer
Optimizer = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.99)
# Compiling the ANN
model0.compile(optimizer = Optimizer, loss = 'mean_squared_error', metrics=['mse','mae'])
# Fitting the ANN to the Train set, at the same time observing quality on Valid set
history = model0.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size = 100, epochs = 1000)
# Generate prediction for both Train and Valid set
y_train_pred_model0 = model0.predict(X_train)
y_test_pred_model0 = model0.predict(X_test)
# check what metrics are in fact available in history
history.history.keys()
dict_keys(['val_loss', 'val_mean_squared_error', 'val_mean_absolute_error', 'loss', 'mean_squared_error', 'mean_absolute_error'])
# look at model fitting history
plot_history(history, ['mean_squared_error', 'val_mean_squared_error'])
plot_history(history, ['mean_absolute_error', 'val_mean_absolute_error'])
# look at model fit quality
for i in range(len(y_test)):
print('%s => %s (expected %s)' % (X[i].tolist(), y_test_pred_model0[i], y_test[i]))
plot_fit(pd.DataFrame(y_train), y_train_pred_model0, 'Fit on train data')
plot_fit(pd.DataFrame(y_test), y_test_pred_model0, 'Fit on test data')
print('MSE on train data is: '.format(history.history['mean_squared_error'][-1]))
print('MSE on test data is: '.format(history.history['val_mean_squared_error'][-1]))
[1000.0, 25.0, 2235.3, 1.0] => [2.2463024] (expected [3])
[1000.0, 30.0, 2190.1, 1.0] => [5.6396966] (expected [3])
[1000.0, 35.0, 2144.7, 1.0] => [5.6486473] (expected [5])
[1000.0, 40.0, 2098.9, 1.0] => [4.852657] (expected [3])
[1000.0, 45.0, 2052.9, 1.0] => [3.9801836] (expected [4])
[1000.0, 25.0, 2235.3, 1.0] => [5.761505] (expected [6])
MSE on train data is: 0.1629941761493683
MSE on test data is: 1.9077353477478027
有了这个结果，我们假设过拟合成功了。
寻找有价值的特征（你拥有的数据的转换）
# augment features by calculating absolute values and squares of original features
X_train = np.array([list(x) + list(np.abs(x)) + list(x**2) for x in X_train])
X_test = np.array([list(x) + list(np.abs(x)) + list(x**2) for x in X_test])
Model1 - 具有 8 个附加功能，总共 12 个输入（而不是 4 个）
# reset_all_randomness - for reproducibility
my_init = reset_all_randomness()
# model1
# Initialising the ANN
model1 = Sequential()
# Adding 1 hidden layer: the input layer and the first hidden layer
model1.add(Dense(units = 128, activation = 'tanh', input_dim = 12, kernel_initializer=my_init))
# Adding 2 hidden layer
model1.add(Dense(units = 64, activation = 'tanh', kernel_initializer=my_init))
# Adding 3 hidden layer
model1.add(Dense(units = 32, activation = 'tanh', kernel_initializer=my_init))
# Adding 4 hidden layer
model1.add(Dense(units = 16, activation = 'tanh', kernel_initializer=my_init))
# Adding output layer
model1.add(Dense(units = 1, kernel_initializer=my_init))
# Set up Optimizer
Optimizer = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.99)
# Compiling the ANN
model1.compile(optimizer = Optimizer, loss = 'mean_squared_error', metrics=['mse','mae'])
# Fitting the ANN to the Train set, at the same time observing quality on Valid set
history = model1.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size = 100, epochs = 1000)
# Generate prediction for both Train and Valid set
y_train_pred_model1 = model1.predict(X_train)
y_test_pred_model1 = model1.predict(X_test)
# look at model fitting history
plot_history(history, ['mean_squared_error', 'val_mean_squared_error'])
plot_history(history, ['mean_absolute_error', 'val_mean_absolute_error'])
# look at model fit quality
for i in range(len(y_test)):
print('%s => %s (expected %s)' % (X[i].tolist(), y_test_pred_model1[i], y_test[i]))
plot_fit(pd.DataFrame(y_train), y_train_pred_model1, 'Fit on train data')
plot_fit(pd.DataFrame(y_test), y_test_pred_model1, 'Fit on test data')
print('MSE on train data is: '.format(history.history['mean_squared_error'][-1]))
print('MSE on test data is: '.format(history.history['val_mean_squared_error'][-1]))
[1000.0, 25.0, 2235.3, 1.0] => [2.5696845] (expected [3])
[1000.0, 30.0, 2190.1, 1.0] => [5.0152197] (expected [3])
[1000.0, 35.0, 2144.7, 1.0] => [4.4963903] (expected [5])
[1000.0, 40.0, 2098.9, 1.0] => [5.004753] (expected [3])
[1000.0, 45.0, 2052.9, 1.0] => [3.982211] (expected [4])
[1000.0, 25.0, 2235.3, 1.0] => [6.158882] (expected [6])
MSE on train data is: 0.17548464238643646
MSE on test data is: 1.4240833520889282
Model2 - 2-hidden-layers NNs 的网格搜索实验 寻址：
使用 NN 架构（layer1_neurons、layer2_neurons、activation_function）
玩弄 NN 估计过程（learning_rate、beta1、beta2）
# init experiment_results
experiment_results = []
# the experiment
for layer1_neurons in [4, 8, 16,32 ]:
for layer2_neurons in [4, 8, 16, 32]:
for activation_function in ['tanh', 'relu']:
for learning_rate in [0.01, 0.001]:
for beta1 in [0.9]:
for beta2 in [0.99]:
# reset_all_randomness - for reproducibility
my_init = reset_all_randomness()
# model2
# Initialising the ANN
model2 = Sequential()
# Adding 1 hidden layer: the input layer and the first hidden layer
model2.add(Dense(units = layer1_neurons, activation = activation_function, input_dim = 12, kernel_initializer=my_init))
# Adding 2 hidden layer
model2.add(Dense(units = layer2_neurons, activation = activation_function, kernel_initializer=my_init))
# Adding output layer
model2.add(Dense(units = 1, kernel_initializer=my_init))
# Set up Optimizer
Optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1, beta2=beta2)
# Compiling the ANN
model2.compile(optimizer = Optimizer, loss = 'mean_squared_error', metrics=['mse','mae'])
# Fitting the ANN to the Train set, at the same time observing quality on Valid set
history = model2.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size = 100, epochs = 1000, verbose=0)
# Generate prediction for both Train and Valid set
y_train_pred_model2 = model2.predict(X_train)
y_test_pred_model2 = model2.predict(X_test)
print('MSE on train data is: '.format(history.history['mean_squared_error'][-1]))
print('MSE on test data is: '.format(history.history['val_mean_squared_error'][-1]))
# create data you want to save for each processed NN
partial_results = \
'layer1_neurons': layer1_neurons,
'layer2_neurons': layer2_neurons,
'activation_function': activation_function,
'learning_rate': learning_rate,
'beta1': beta1,
'beta2': beta2,
'final_train_mean_squared_error': history.history['mean_squared_error'][-1],
'final_val_mean_squared_error': history.history['val_mean_squared_error'][-1],
'best_train_epoch': history.history['mean_squared_error'].index(min(history.history['mean_squared_error'])),
'best_train_mean_squared_error': np.min(history.history['mean_squared_error']),
'best_val_epoch': history.history['val_mean_squared_error'].index(min(history.history['val_mean_squared_error'])),
'best_val_mean_squared_error': np.min(history.history['val_mean_squared_error']),
experiment_results.append(
partial_results
)
探索实验结果：
# put experiment_results into DataFrame
experiment_results_df = pd.DataFrame(experiment_results)
# identifying models hopefully not too much overfitted to valid data at the end of estimation (after 1000 epochs) :
experiment_results_df['valid'] = experiment_results_df['final_val_mean_squared_error'] > experiment_results_df['final_train_mean_squared_error']
# display the best combinations of parameters for valid data, which seems not overfitted
experiment_results_df[experiment_results_df['valid']].sort_values(by=['final_val_mean_squared_error']).head()
layer1_neurons layer2_neurons activation_function learning_rate beta1 beta2 final_train_mean_squared_error final_val_mean_squared_error best_train_epoch best_train_mean_squared_error best_val_epoch best_val_mean_squared_error valid
26 8 16 relu 0.010 0.900 0.990 0.992 1.232 998 0.992 883 1.117 True
36 16 8 tanh 0.010 0.900 0.990 0.178 1.345 998 0.176 40 1.245 True
14 4 32 relu 0.010 0.900 0.990 1.320 1.378 980 1.300 98 0.937 True
2 4 4 relu 0.010 0.900 0.990 1.132 1.419 996 1.131 695 1.002 True
57 32 16 tanh 0.001 0.900 0.990 1.282 1.432 999 1.282 999 1.432 True
如果考虑到整个培训历史，您可以做得更好：
# for each NN estimation identify dictionary of epochs for which NN was not overfitted towards valid data
# for each such epoch I store its number and corresponding mean_squared_error on valid data
experiment_results_df['not_overfitted_epochs_on_valid'] = \
experiment_results_df.apply(
lambda row:
i: row['val_mean_squared_error_history'][i]
for i in range(len(row['train_mean_squared_error_history']))
if row['val_mean_squared_error_history'][i] > row['train_mean_squared_error_history'][i]
,
axis=1
)
# basing on previosuly prepared dict, for each NN estimation I can identify:
# best not overfitted mse value on valid data and corresponding best not overfitted epoch on valid data
experiment_results_df['best_not_overfitted_mse_on_valid'] = \
experiment_results_df['not_overfitted_epochs_on_valid'].apply(
lambda x: np.min(list(x.values())) if len(list(x.values()))>0 else np.NaN
)
experiment_results_df['best_not_overfitted_epoch_on_valid'] = \
experiment_results_df['not_overfitted_epochs_on_valid'].apply(
lambda x: list(x.keys())[list(x.values()).index(np.min(list(x.values())))] if len(list(x.values()))>0 else np.NaN
)
# now I can sort all estimations according to best not overfitted mse on valid data overall, not only at the end of estimation
experiment_results_df.sort_values(by=['best_not_overfitted_mse_on_valid'])[[
'layer1_neurons','layer2_neurons','activation_function','learning_rate','beta1','beta2',
'best_not_overfitted_mse_on_valid','best_not_overfitted_epoch_on_valid'
]].head()
layer1_neurons layer2_neurons activation_function learning_rate beta1 beta2 best_not_overfitted_mse_on_valid best_not_overfitted_epoch_on_valid
26 8 16 relu 0.010 0.900 0.990 1.117 883.000
54 32 8 relu 0.010 0.900 0.990 1.141 717.000
50 32 4 relu 0.010 0.900 0.990 1.210 411.000
36 16 8 tanh 0.010 0.900 0.990 1.246 821.000
56 32 16 tanh 0.010 0.900 0.990 1.264 693.000
现在我记录最终模型估计的最高估计组合：
layer1_neurons = 8 layer2_neurons = 16 activation_function = 'relu' learning_rate = 0.010 beta1 = 0.900 beta2 = 0.990 停止训练的纪元 = 883Model3 - 最终模型
# reset_all_randomness - for reproducibility
my_init = reset_all_randomness()
# model3
# Initialising the ANN
model3 = Sequential()
# Adding 1 hidden layer: the input layer and the first hidden layer
model3.add(Dense(units = 8, activation = 'relu', input_dim = 12, kernel_initializer=my_init))
# Adding 2 hidden layer
model3.add(Dense(units = 16, activation = 'relu', kernel_initializer=my_init))
# Adding output layer
model3.add(Dense(units = 1, kernel_initializer=my_init))
# Set up Optimizer
Optimizer = tf.train.AdamOptimizer(learning_rate=0.010, beta1=0.900, beta2=0.990)
# Compiling the ANN
model3.compile(optimizer = Optimizer, loss = 'mean_squared_error', metrics=['mse','mae'])
# Fitting the ANN to the Train set, at the same time observing quality on Valid set
history = model3.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size = 100, epochs = 884)
# Generate prediction for both Train and Valid set
y_train_pred_model3 = model3.predict(X_train)
y_test_pred_model3 = model3.predict(X_test)
# look at model fitting history
plot_history(history, ['mean_squared_error', 'val_mean_squared_error'])
plot_history(history, ['mean_absolute_error', 'val_mean_absolute_error'])
# look at model fit quality
for i in range(len(y_test)):
print('%s => %s (expected %s)' % (X[i].tolist(), y_test_pred_model3[i], y_test[i]))
plot_fit(pd.DataFrame(y_train), y_train_pred_model3, 'Fit on train data')
plot_fit(pd.DataFrame(y_test), y_test_pred_model3, 'Fit on test data')
print('MSE on train data is: '.format(history.history['mean_squared_error'][-1]))
print('MSE on test data is: '.format(history.history['val_mean_squared_error'][-1]))
[1000.0, 25.0, 2235.3, 1.0] => [1.8813248] (expected [3])
[1000.0, 30.0, 2190.1, 1.0] => [4.3430963] (expected [3])
[1000.0, 35.0, 2144.7, 1.0] => [4.827326] (expected [5])
[1000.0, 40.0, 2098.9, 1.0] => [4.6029215] (expected [3])
[1000.0, 45.0, 2052.9, 1.0] => [3.8530324] (expected [4])
[1000.0, 25.0, 2235.3, 1.0] => [4.9882255] (expected [6])
MSE on train data is: 1.088669776916504
MSE on test data is: 1.1166337728500366
在任何情况下，我都不会声称 Model3 是最适合您的数据的。我只是想向您介绍使用 NN 的方法。您可能还对进一步探索主题感兴趣：
探索性分析（寻找功能的想法）， 特征提取（计算特征）， 交叉验证（与确保模型泛化相关的方法 - 特别是因为您的数据很小）， 神经网络的超参数及其估计过程（调整什么）， 超参数优化（网格搜索、随机搜索、贝叶斯搜索、支持调整参数的遗传算法等方法 = 找到最佳模型）， 提前停止神经网络估计（估计规则可以节省一些估计时间）。希望你会发现它对进一步的学习有启发:-)
编辑：
我正在分享示例性步骤，这些步骤是重新定义此问题从近似到分类所需的步骤，对于 Model0。如果您想更了解 Python 中的神经网络，我还想分享一些有价值的文献参考：
[2018 Chollet] 使用 Python 进行深度学习
其他有用的功能
def give_me_mse(true, prediction):
"""
This function returns mse for 2 vectors: true and predicted values.
"""
return np.mean((true-prediction)**2)
从文件中加载 X 和 y
# as previosly
编码目标 - 因为现在您需要 7 个反映目标值的向量（因为您的目标有 7 个级别）
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
# encode class values as integers
encoder = LabelEncoder()
encoder.fit(np.ravel(y))
y_encoded = encoder.transform(np.ravel(y))
# convert integers to dummy variables (i.e. one hot encoded)
y_dummy = np_utils.to_categorical(y_encoded)
将 X 和 y 拆分为训练集和测试集
# reset_all_randomness - for reproducibility
my_init = reset_all_randomness()
# Splitting the dataset into the Training set and Test set
X_train, X_test, y_train, y_test, y_train_dummy, y_test_dummy = train_test_split(X, y, y_dummy, test_size = 0.08, random_state = 0)
特征缩放
# as previosly
Model0 - 为分类问题重新排列
现在 NN 为单个输入数据条目生成 7 元素输出
输出由7个概率组成，分别是属于对应目标级别的概率
# model0
# Initialising the ANN
model0 = Sequential()
# Adding 1 hidden layer: the input layer and the first hidden layer
model0.add(Dense(units = 128, activation = 'tanh', input_dim = 4, kernel_initializer=my_init))
# Adding 2 hidden layer
model0.add(Dense(units = 64, activation = 'tanh', kernel_initializer=my_init))
# Adding 3 hidden layer
model0.add(Dense(units = 32, activation = 'tanh', kernel_initializer=my_init))
# Adding 4 hidden layer
model0.add(Dense(units = 16, activation = 'tanh', kernel_initializer=my_init))
# Adding output layer
model0.add(Dense(units = 7, activation = 'softmax', kernel_initializer=my_init))
# Set up Optimizer
Optimizer = tf.train.AdamOptimizer(learning_rate=0.001, beta1=0.9, beta2=0.99)
# Compiling the ANN
model0.compile(optimizer = Optimizer, loss = 'categorical_crossentropy', metrics=['accuracy','categorical_crossentropy','mse'])
# Fitting the ANN to the Train set, at the same time observing quality on Valid set
history = model0.fit(X_train, y_train_dummy, validation_data=(X_test, y_test_dummy), batch_size = 100, epochs = 1000)
# Generate prediction for both Train and Valid set
y_train_pred_model0 = model0.predict(X_train)
y_test_pred_model0 = model0.predict(X_test)
# find final prediction by taking class with highest probability
y_train_pred_model0 = np.array([[list(x).index(max(list(x))) + 1] for x in y_train_pred_model0])
y_test_pred_model0 = np.array([[list(x).index(max(list(x))) + 1] for x in y_test_pred_model0])
# check what metrics are in fact available in history
history.history.keys()
dict_keys(['val_loss', 'val_acc', 'val_categorical_crossentropy', 'val_mean_squared_error', 'loss', 'acc', 'categorical_crossentropy', 'mean_squared_error'])
# look at model fitting history
plot_history(history, ['mean_squared_error', 'val_mean_squared_error'])
plot_history(history, ['categorical_crossentropy', 'val_categorical_crossentropy'])
plot_history(history, ['acc', 'val_acc'])
# look at model fit quality
plot_fit(pd.DataFrame(y_train), y_train_pred_model0, 'Fit on train data')
plot_fit(pd.DataFrame(y_test), y_test_pred_model0, 'Fit on test data')
print('MSE on train data is: '.format(give_me_mse(y_train, y_train_pred_model0)))
print('MSE on test data is: '.format(give_me_mse(y_test, y_test_pred_model0)))
MSE on train data is: 0.0
MSE on test data is: 1.3333333333333333
【讨论】：
这是一个很棒的答案；如果可以的话，我会给10票！我认为我唯一可能要补充的是，通过测试多个架构进行超参数调整可能需要使用训练集、验证集和测试集，而不仅仅是训练/测试拆分。 @Juan Kania-Morales 感谢您的回复。请问有什么方法可以重新设计模型（ANN）以获得预期的输出。以上是关于在 Python 中训练后，神经网络没有给出预期的输出的主要内容，如果未能解决你的问题，请参考以下文章