AI Keras建筑模型

Question

输入X=[[1,1,1,1,1]、[1,2,1,3,7]、[3,1,5,7,8]等。。 输出Y=[[0.77]、[0.63]、[0.77]、[1.26]]等

输入x表示某个组合示例

["car", "black", "sport", "xenon", "5dor"] 
["car", "red", "sport", "noxenon", "3dor"] etc...

输出意味着组合的一些分数

我需要什么？我需要预测组合是好是坏

数据集大小10k

型号：

model.add(Dense(20, input_dim = 5, activation = 'relu'))
model.add(Dense(20, activation = 'relu'))
model.add(Dense(1, activation = 'linear'))

优化器=adam，损失=mse，验证分割0.2，历元30

Tr:

Epoch 1/30
238/238 [==============================] - 0s 783us/step - loss: 29.8973 - val_loss: 19.0270
Epoch 2/30
238/238 [==============================] - 0s 599us/step - loss: 29.6696 - val_loss: 19.0100
Epoch 3/30
238/238 [==============================] - 0s 579us/step - loss: 29.6606 - val_loss: 19.0066
Epoch 4/30
238/238 [==============================] - 0s 583us/step - loss: 29.6579 - val_loss: 19.0050
Epoch 5/30

不好没有感觉

我需要一些好的文件如何正确设置或建立模型

Answer 1

只是想复制。我的结果和你的不同。请检查：

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras import Model
inputA = Input(shape=(5, ))
x = Dense(20, activation='relu')(inputA)
x = Dense(20, activation='relu')(x)
x = Dense(1, activation='linear')(x)
model = Model(inputs=inputA, outputs=x)
model.compile(optimizer = 'adam', loss = 'mse')
input = tf.random.uniform([10000, 5], 0, 10, dtype=tf.int32)
labels = tf.random.uniform([10000, 1])
model.fit(input, labels, epochs=30, validation_split=0.2)

结果:

Epoch 1/30 250/250 [==============================] - 1s 3ms/step - loss: 0.1980 - val_loss: 0.1082

Epoch 2/30 250/250 [==============================] - 1s 2ms/step - loss: 0.0988 - val_loss: 0.0951

Epoch 3/30 250/250 [==============================] - 1s 2ms/step - loss: 0.0918 - val_loss: 0.0916

Epoch 4/30 250/250 [==============================] - 1s 2ms/step - loss: 0.0892 - val_loss: 0.0872

Epoch 5/30 250/250 [==============================] - 0s 2ms/step - loss: 0.0886 - val_loss: 0.0859

Epoch 6/30 250/250 [==============================] - 1s 2ms/step - loss: 0.0864 - val_loss: 0.0860

Epoch 7/30 250/250 [==============================] - 1s 3ms/step - loss: 0.0873 - val_loss: 0.0863

Epoch 8/30 250/250 [==============================] - 1s 2ms/step - loss: 0.0863 - val_loss: 0.0992

Epoch 9/30 250/250 [==============================] - 0s 2ms/step - loss: 0.0876 - val_loss: 0.0865

该模型应适用于真实数字