machine-learning - DNN二元分类器的准确率没有增加

问题描述

我的二元分类器 DNN 的准确性似乎自 epoch 1 起就卡住了。我认为这意味着模型没有学习。关于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 相当于“空闲”状态，1 相当于“活动”状态）。

关于数据集：数据集在这里可用 “状态”列是目标，而其余列是特征。

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的神经元数量，并尝试使用 sklearn 的 StandardScaler 而不是 MinMaxScaler。这些方法似乎都没有改变结果。

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）的准确性增加（并且损失减少）。

实际结果：在整个训练过程中，以下值是固定的：

loss: 8.0118 - acc: 0.5001 - val_loss: 8.0366 - val_acc: 0.4987

标签： machine-learningkerasscikit-learndeep-learningclassification

machine-learning - DNN二元分类器的准确率没有增加

问题描述

解决方案

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