如何训练具有多个独立训练数据的LSTM模型？
如何训练具有多个独立训练数据的LSTM模型？
提问于 2020-08-27 01:47:37
我有一年内100名销售人员的销售数据。
而且我要有一个模型，预测所有100个人以后都会卖男人的。
这是我的密码：
model=Sequential()
y_train=sells_men_sell[1] # sells_men_sell[1] is a 1d array that contains the first sells man's sells record
x_train=sells_men_data[1] # sells_men_sell[1] is a array that contains the first sells man's sells record for training
#, each value in the array(sells_men_sell) contains the sells record for the past 30 days.
model.add(LSTM(50, input_shape=(x_train.shape[1], x_train.shape[2])))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(x_train, y_train, batch_size=1, epoch=1)我知道100名销售人员在一种模式听起来很奇怪，但我这样做是为了一个项目。
我该怎么处理我的代码？
我应该在model.fit(x_train, y_train, batch_size=1, epoch=1)之后添加以下代码吗？
y_train1=sells_men_sell[2] # sells_men_sell[2] is a 1d array that contains the second sells man's sells record
x_train1=sells_men_data[2] # sells_men_sell[2] is a array that contains the second sells man's sells record for training
model.add(LSTM(50, input_shape=(x_train1.shape[1], x_train1.shape[2])))
model.fit(x_train1, y_train1, batch_size=1, epoch=1)回答 1
Stack Overflow用户
回答已采纳
发布于 2020-08-27 02:27:30
您的模型可以有多个输入和多个输出。您可以使用functional来实现这一点。
我已经分享了一个关于你如何做到这一点的小例子。你可以把这个例子改编成你的用法。
代码：
# imports
import tensorflow as tf
import pandas as pd
import numpy as np
# genration of dummy data
x1 = np.random.randint(100, size =(5, 5, 5), dtype = np.int16)
x2 = np.random.randint(100, size =(5, 4, 4), dtype = np.int16)
y1 = np.random.randint(2, size =(5,), dtype = np.int16)
y2 = np.random.randint(2, size =(5,), dtype = np.int16)
# creation of model
def create_model3():
input1 = tf.keras.Input(shape=(5,5,), name = 'I1')
input2 = tf.keras.Input(shape=(4,4,), name = 'I2')
hidden1 = tf.keras.layers.LSTM(units = 4)(input1)
hidden2 = tf.keras.layers.LSTM(units = 4)(input2)
merge = tf.keras.layers.concatenate([hidden1, hidden2])
hidden3 = tf.keras.layers.Dense(units = 3, activation='relu')(merge)
output1 = tf.keras.layers.Dense(units = 2, activation='softmax', name ='O1')(hidden3)
output2 = tf.keras.layers.Dense(units = 2, activation='softmax', name = 'O2')(hidden3)
model = tf.keras.models.Model(inputs = [input1,input2], outputs = [output1,output2])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
model = create_model3()
tf.keras.utils.plot_model(model, 'my_first_model.png', show_shapes=True)
# training the model
history = model.fit(
x = {'I1':x1, 'I2':x2},
y = {'O1':y1, 'O2': y2},
batch_size = 32,
epochs = 10,
verbose = 1,
callbacks = None,
# validation_data = [(val_data,new_val_data),(val_labels, new_val_labels)]
)生成的模型如下所示。
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/63607981
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