Question

我的二进制分类器DNN的准确性似乎从第1个时代开始就停滞了。我认为这意味着该模型没有学习。为什么会发生这种情况的任何见解？

问题陈述：我想将传感器的给定读数序列（例如[0 1 15 1 0 3]）分类为0或1（0等于“空闲”状态，1等于“活动”状态）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（至少对于早期时期而言）的准确性都会提高（而损失会降低）。

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

Answer 1

您使用了错误的损失，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。

DNN二进制分类器的精度未提高

1 个答案: