背景關系是通過讀取加速度、冷卻水溫度、轉速、油耗、制動力等值來確定摩托車部件的健康狀況……
輸入資料：
water_temperature rpm federeindrückung in mm Spritverbrauch/100km Bremsverz?gerung [m/s2] ... Lichtanlage Reifen Rahmen Bordelektronik Auspuffanlage
0 99.995 6000 150 5.0 2 ... 99.995 99.995 99.995 99.995 99.995
1 80.000 11500 150 5.0 2 ... 99.995 99.995 99.995 99.995 99.995
2 80.000 6000 0 5.0 2 ... 99.995 99.000 99.000 99.995 99.000
3 80.000 6000 150 10.0 2 ... 99.995 99.995 99.995 99.995 99.995
4 80.000 8000 150 6.0 2 ... 99.995 99.995 99.995 99.995 99.995
5 75.000 8000 150 4.9 2 ... 99.995 99.995 99.995 99.995 99.995
6 75.000 8000 150 4.9 8 ... 99.995 99.000 99.000 99.995 99.995
7 80.000 8000 150 5.5 2 ... 99.995 99.995 99.995 99.995 99.995
8 83.000 8000 150 5.0 2 ... 99.995 99.995 99.995 99.995 99.995
9 79.000 8000 150 5.0 2 ... 99.000 99.995 99.000 98.000 99.000
10 80.000 11000 150 5.0 2 ... 99.995 99.995 99.995 99.995 99.995
神經網路的預測應該是1分鐘后摩托車各部分的剩余健康度，比如11500轉一分鐘，發動機損失1%的健康度。
所需的輸出資料：
Motor Federung Getriebe Bremsen Lichtanlage Reifen Rahmen Bordelektronik Auspuffanlage
0 99.000 99.995 99.000 99.995 99.995 99.995 99.995 99.995 99.995
1 98.000 99.995 99.000 99.995 99.995 99.995 99.995 99.995 99.995
2 99.995 97.000 99.995 99.995 99.995 99.000 99.000 99.995 99.000
3 98.000 99.995 99.995 99.995 99.995 99.995 99.995 99.995 99.995
4 99.995 99.995 99.995 99.995 99.995 99.995 99.995 99.995 99.995
5 99.995 99.995 99.995 99.995 99.995 99.995 99.995 99.995 99.995
6 99.995 99.995 99.995 99.000 99.995 99.000 99.000 99.995 99.995
7 99.995 99.995 99.995 99.995 99.995 99.995 99.995 99.995 99.995
8 98.000 99.995 99.995 99.995 99.995 99.995 99.995 99.995 99.995
9 99.995 99.995 99.995 99.000 99.000 99.995 99.000 98.000 99.000
10 98.000 99.995 99.995 99.995 99.995 99.995 99.995 99.995 99.995
目標是將整個程序轉換為神經網路。
到目前為止，我有這個代碼：
import pandas as pd
import tensorflow as tf
from tensorflow.keras import layers
names = open("./training_data_header.txt", "r").read().split(",")
motorbike_train = pd.read_csv("training_data.csv", names=names)
motorbike_features = motorbike_train.copy()
outputs = pd.DataFrame()
for i in range(7, len(names)):
outputs = pd.concat([outputs, motorbike_train.pop(names[i])], axis=1)
motorbike_model = tf.keras.Sequential(
[layers.Dense(16), layers.Dense(9, activation="sigmoid")]
)
motorbike_model.compile(
loss=tf.keras.losses.MeanSquaredError(), optimizer=tf.optimizers.Adam()
)
motorbike_model.fit(motorbike_features, outputs, epochs=10)
# print(motorbike_features)
# print(outputs)
print(motorbike_model.predict(motorbike_features.loc[0]))
這會導致以下錯誤：
Traceback (most recent call last):
File "/Users/tobiaswimmer/Documents/Development/atos-ktm-hackathon/ai/create-model.py", line 26, in <module>
print(motorbike_model.predict(motorbike_features.loc[0]))
File "/usr/local/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/var/folders/1r/2f_4g23d3zx5lb8m2_s28qt00000gn/T/__autograph_generated_filekm9z4tos.py", line 15, in tf__predict_function
retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
ValueError: in user code:
File "/usr/local/lib/python3.9/site-packages/keras/engine/training.py", line 1845, in predict_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.9/site-packages/keras/engine/training.py", line 1834, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.9/site-packages/keras/engine/training.py", line 1823, in run_step **
outputs = model.predict_step(data)
File "/usr/local/lib/python3.9/site-packages/keras/engine/training.py", line 1791, in predict_step
return self(x, training=False)
File "/usr/local/lib/python3.9/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/usr/local/lib/python3.9/site-packages/keras/engine/input_spec.py", line 248, in assert_input_compatibility
raise ValueError(
ValueError: Exception encountered when calling layer "sequential" (type Sequential).
Input 0 of layer "dense" is incompatible with the layer: expected axis -1 of input shape to have value 16, but received input with shape (None, 1)
Call arguments received by layer "sequential" (type Sequential):
? inputs=tf.Tensor(shape=(None, 1), dtype=float32)
? training=False
? mask=None
uj5u.com熱心網友回復：
您需要將張量的第一個維度設定為 1，以獲得 (1,16) 的輸入形狀：
import numpy as np
print(motorbike_model.predict(np.expand_dims(motorbike_features.loc[0], axis=0)))
不能使用 sigmoid 作為神經網路的輸出，否則每個輸出值將介于 0 和 1 之間。
編輯：嘗試以下
motorbike_model = tf.keras.Sequential(
[layers.Conv2D(32,1,padding='same',activation='relu',input_shape=(1,16,1)),
layers.Conv2D(64,1,padding='same',activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(9)])
motorbike_model.summary()
motorbike_model.compile(
loss=tf.keras.losses.MeanSquaredError(),
optimizer=tf.optimizers.Adam()
)
motorbike_model.fit((np.expand_dims(motorbike_features, axis= (0,3))), outputs, epochs=150)
print(motorbike_model.predict(np.expand_dims(motorbike_features.loc[0], axis=(0,1,3))))
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