如何使用张量流神经网络从一组输入值预测一组输出值？
How to predict a set of output values from a set of input values with a tensorflow neural network?
上下文是通过读取加速度、冷却水温度、转速、油耗、制动力等值来确定摩托车部件的健康状况...
输入数据：
water_temperature rpm federeindrückung in mm Spritverbrauch/100km Bremsverzögerung [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
神经网络的预测应该是摩托车各部分在1分钟后的剩余健康度，例如RPM为11500一分钟，发动机失去1%的健康度。
所需的输出数据：
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
目标是将整个过程转换为神经网络。
到目前为止我有这个代码：
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]))
这会导致以下错误：
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
您需要将张量的第一维设置为 1，以获得 (1,16) 的输入形状：
import numpy as np
print(motorbike_model.predict(np.expand_dims(motorbike_features.loc[0], axis=0)))
不能使用sigmoid作为神经网络的输出，否则每个输出值都会在0到1之间。
编辑：
尝试以下
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))))
上下文是通过读取加速度、冷却水温度、转速、油耗、制动力等值来确定摩托车部件的健康状况...
输入数据：
water_temperature rpm federeindrückung in mm Spritverbrauch/100km Bremsverzögerung [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
神经网络的预测应该是摩托车各部分在1分钟后的剩余健康度，例如RPM为11500一分钟，发动机失去1%的健康度。
所需的输出数据：
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
目标是将整个过程转换为神经网络。
到目前为止我有这个代码：
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]))
这会导致以下错误：
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
您需要将张量的第一维设置为 1，以获得 (1,16) 的输入形状：
import numpy as np
print(motorbike_model.predict(np.expand_dims(motorbike_features.loc[0], axis=0)))
不能使用sigmoid作为神经网络的输出，否则每个输出值都会在0到1之间。
编辑： 尝试以下
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))))