我的二进制分类器DNN的准确性似乎从时期1开始就停滞了。我认为这意味着该模型没有学习。为什么会这样？
问题陈述：我想将给定的传感器读数序列（例如[0 1 15 1 0 3]）分为0或1（0等于“空闲”状态，1等于“活动”状态）。
关于数据集：数据集可用here
“状态”列是目标，而其余列是要素。
我尝试使用SGD代替Adam，尝试使用不同的内核初始化，尝试更改隐藏层的数量和每层神经元的数量，并尝试使用sklearn的StandardScaler而不是MinMaxScaler。这些方法似乎都无法改变结果。
这是代码：
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.callbacks import EarlyStopping
from keras.optimizers import Adam
from keras.initializers import he_uniform
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder
seed = 7
random_state = np.random.seed(seed)
data = pd.read_csv('Dataset/Reformed/Model0_Dataset.csv')
X = data.drop(['state'], axis=1).values
y = data['state'].values
#min_max_scaler = MinMaxScaler()
std_scaler = StandardScaler()
# X_scaled = min_max_scaler.fit_transform(X)
X_scaled = std_scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=random_state)
# One Hot encode targets
y_train = y_train.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
enc = OneHotEncoder(categories='auto')
y_train_enc = enc.fit_transform(y_train).toarray()
y_test_enc = enc.fit_transform(y_test).toarray()
epochs = 500
batch_size = 100
model = Sequential()
model.add(Dense(700, input_shape=(X.shape[1],), kernel_initializer=he_uniform(seed)))
model.add(Dropout(0.5))
model.add(Dense(1400, activation='relu', kernel_initializer=he_uniform(seed)))
model.add(Dropout(0.5))
model.add(Dense(700, activation='relu', kernel_initializer=he_uniform(seed)))
model.add(Dropout(0.5))
model.add(Dense(800, activation='relu', kernel_initializer=he_uniform(seed)))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.summary()
early_stopping_monitor = EarlyStopping(patience=25)
# model.compile(SGD(lr=.01, decay=1e-6, momentum=0.9, nesterov=True), loss='binary_crossentropy', metrics=['accuracy'])
model.compile(Adam(lr=.01, decay=1e-6), loss='binary_crossentropy', metrics=['accuracy'], )
history = model.fit(X_train, y_train_enc, validation_split=0.2, batch_size=batch_size,
callbacks=[early_stopping_monitor], epochs=epochs, shuffle=True, verbose=1)
eval = model.evaluate(X_test, y_test_enc, batch_size=batch_size, verbose=1)
预期结果：每个时期（至少在早期时期）准确性提高（而损失降低）。
实际结果：在整个培训过程中，以下值是固定的：
loss: 8.0118 - acc: 0.5001 - val_loss: 8.0366 - val_acc: 0.4987
最佳答案
您使用的是错误的丢失，对于两个输出的softmax，应该使用categorical_crossentropy，并且应该对标签进行一键编码。如果要使用binary_crossentropy，则输出层应该是一个具有S型激活的单元。