我是 Keras 的新手。刚开始在本地取自这里的示例。示例数据工作正常。然后我稍微修改了代码以适应我的数据（在我的数据文件中，结果列排在第一位）。然后当我再次运行并尝试预测输入时，它总是为每个输入行返回相同的结果 - [1. 0.], [1. 0.] ...。这是我的代码：
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import EarlyStopping
from keras.utils import to_categorical
#read in training data
train_df_2 = pd.read_csv('/Users/my_user/python-workspace/Deep-Learning-in-Keras-Tutorial/data/my_data.csv')
#view data structure
train_df_2.head()
#create a dataframe with all training data except the target column
train_X_2 = train_df_2.drop(columns=['result'])
target = train_df_2[['result']]
#check that the target variable has been removed
train_X_2.head()
#one-hot encode target column
train_y_2 = to_categorical(train_df_2.result)
#create model
model_2 = Sequential()
#get number of columns in training data
n_cols_2 = train_X_2.shape[1]
#add layers to model
model_2.add(Dense(25, activation='relu', input_shape=(n_cols_2,)))
model_2.add(Dense(25, activation='relu'))
model_2.add(Dense(2, activation='softmax'))
# model_2.add(Dense(10, input_dim=n_cols_2, kernel_initializer='normal', activation='relu'))
# model_2.add(Dense(25, activation='relu'))
# model_2.add(Dense(2, activation='softmax'))
#compile model using accuracy to measure model performance
model_2.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
#set early stopping monitor so the model stops training when it won't improve anymore
early_stopping_monitor = EarlyStopping(patience=3)
#train model
model_2.fit(train_X_2, train_y_2, epochs=30, validation_split=0.1, callbacks=[early_stopping_monitor])
p = model_2.predict(train_X_2, verbose=0, batch_size=1)
print(p)
我的输入数据示例：
result,i1,i2,i3,i4
0,1770,2390,1750,1816
1,1675,2540,2029,1940
1,1770,2384,1765,1770
0,1690,2485,2075,1900
0,1680,2465,2050,1920
0,1770,2395,1744,1795
1,1675,2490,2050,1915
0,1768,2400,1740,1790
0,1675,2525,2050,1910
.... (total 2312 rows)
为什么它总是[1. 0.]为每一行返回相同的结果？我预计至少有一排[0. 1.]. 我究竟做错了什么？
您尚未标准化输入数据。因此，它会阻碍训练过程并破坏梯度更新，并且您的模型可能一无所获。尝试使用类似sklearn.preprocessing.StandardScaler. 或者，您可以手动执行此操作：
mean = train_X_2.mean(axis=0)
train_X_2 -= mean
std = train_X_2.std(axis=0)
train_X_2 /= std
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