Input X = [[1,1,1,1,1], [1,2,1,3,7], [3,1,5,7,8]] etc.. Output Y = [[0.77],[0.63],[0.77],[1.26]] etc..
input x mean some combination example
["car", "black", "sport", "xenon", "5dor"]
["car", "red", "sport", "noxenon", "3dor"] etc...
output mean some score of combination.
What i need? i need to predict is combination good or bad....
Dataset size 10k..
Model:
model.add(Dense(20, input_dim = 5, activation = 'relu'))
model.add(Dense(20, activation = 'relu'))
model.add(Dense(1, activation = 'linear'))
optimizer = adam, loss = mse, validation split 0.2, epoch 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
not good no sens...
i need some good documentation how to proper setup or build model...
Just tried to reproduce. My results differ from yours. Please check:
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)
Results:
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
The model should work on real figures.