3类标号作为分类问题的LSTM模型
3类标号作为分类问题的LSTM模型
提问于 2020-06-21 01:47:53
我的问题是预测输出有3个类标签，假设我在我的数据集中有20000个样本，每个样本与标签(0,1,2)相关联。因为这是多类分类问题。
我只能将输入作为( 0，1,2)的标签输入到网络中，并根据标签进行预测。提供给网络的数据是否足以学习和预测输出？
请帮我处理你的意见。
# Below is the code
X_train, X_test, y_train, y_test = train_test_split(values_train[:, 0],
values_train[:, 1],
test_size=0.25,
random_state=42)
print(" X Training Set size is",X_train.shape )
print(" y Training Set size is",y_train.shape )
print(" X Test Set size is",X_test.shape)
print(" y Test Set size is",y_test.shape )'X Training Set size is (165081,)'
'y Training Set size is (165081,)'
'X Test Set size is (55028,)'
'y Test Set size is (55028,)'# convert to LSTM friendly format
X_train = X_train.reshape(len(X_train),1, 1)
X_test = X_test.reshape(len(X_test),1,1)
print(X_train.shape, X_test.shape)(165081, 1, 1) (55028, 1, 1)# configure network
n_batch = 1
n_epoch = 100
n_neurons = 10
from keras.optimizers import SGD
opt = SGD(lr=0.01)
# design network
model = Sequential()
model.add(LSTM(n_neurons, batch_input_shape=(n_batch, X_train.shape[1],
X_train.shape[2]),
stateful=True))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy'])
# fit network
for i in range(n_epoch):
model.fit(X_train, y_train ,validation_data=(X_test, y_test),
epochs=1, batch_size=n_batch, verbose=1, shuffle= False)
model.reset_states()
df_actual = []
dp_predict = []
for i in range(len(X_test)):
testX,testy = X_test[i],y_test[i]
testX = testX.reshape(1, 1, 1)
yhat = model.predict(testX, batch_size=1)
df_actual.append(testy)
dp_predict.append(yhat)
print('>Actual =%.1f, Predicted=%.1f' % (testy, yhat))在这个模型中，我无法得到正确的预测。
更新：
请在154076份样本上找到以下验证精度和培训精度，并对66033份样本进行验证。
Epoch 1/5
154076/154076 [==============================] - 289s 2ms/step - loss: 1.0033 - accuracy: 0.3816 - val_loss: 1.0018 - val_accuracy: 0.4286
Epoch 2/5
154076/154076 [==============================] - 291s 2ms/step - loss: 1.0021 - accuracy: 0.3817 - val_loss: 1.0020 - val_accuracy: 0.4286
Epoch 3/5
154076/154076 [==============================] - 293s 2ms/step - loss: 1.0018 - accuracy: 0.3804 - val_loss: 1.0014 - val_accuracy: 0.4286
Epoch 4/5
154076/154076 [==============================] - 290s 2ms/step - loss: 1.0016 - accuracy: 0.3812 - val_loss: 1.0012 - val_accuracy: 0.4286
Epoch 5/5
154076/154076 [==============================] - 290s 2ms/step - loss: 1.0015 - accuracy: 0.3814 - val_loss: 1.0012 - val_accuracy: 0.4286有人能告诉我什么是可以改进的吗？
注意：-我已经用MinMaxScalar规范了输入数据，并使用了缩放的数据，但是输出没有变化。
回答 1
Stack Overflow用户
发布于 2020-06-21 04:40:37
类标签是分类类型的。神经网络不能学习分类数据。你必须用keras.utils.to_categorical对它进行一次热编码。
x = values_train[:, 0]
y = values_train[:, 1]
y = keras.utils.to_categorical(y)
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/62493599
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