[英]InvalidArgumentError: Incompatible shapes with Keras LSTM Net
I want to predict the pressure of a machine. 我想预测一台机器的压力。 I have 18 input values and the pressure as output. 我有18个输入值,压力作为输出。 So I have 19 columns and 7657 rows as the database consists of 7657 time steps and each counts for 1 sec. 因此,我有19列和7657行,因为该数据库包含7657个时间步长,每个时间步长为1秒。
I have a problem with the following code: 我的以下代码有问题:
import tensorflow as tf
import pandas as pd
from matplotlib import pyplot
from sklearn.preprocessing import MinMaxScaler
from sklearn import linear_model
from keras.models import Sequential
from keras.layers import Dense #Standard neural network layer
from keras.layers import LSTM
from keras.layers import Activation
from keras.layers import Dropout
df = pd.read_csv('Testdaten_2_Test.csv',delimiter=';')
feature_col_names=['LSDI','LZT1I', ..... ,'LZT5I']
predicted_class_names = ['LMDI']
x = df[feature_col_names].values
y = df[predicted_class_names].values
x_train_size = 6400
x_train, x_test = x[0:x_train_size], x[x_train_size:len(x)]
y_train_size = 6400
y_train, y_test = y[0:y_train_size], y[y_train_size:len(y)]
nb_model = linear_model.LinearRegression()
nb_model.fit(X=x_train, y=y_train)
nb_predict_train = nb_model.predict(x_test)
from sklearn import metrics
def scale(x, y):
# fit scaler
x_scaler = MinMaxScaler(feature_range=(-1, 1))
x_scaler = x_scaler.fit(x)
x_scaled = x_scaler.transform(x)
# fit scaler
y_scaler = MinMaxScaler(feature_range=(-1, 1))
y_scaler = y_scaler.fit(y)
y_scaled = y_scaler.transform(y)
return x_scaler, y_scaler, x_scaled, y_scaled
x_scaler, y_scaler, x_scaled, y_scaled = scale(x, y)
x_train, x_test = x_scaled[0:x_train_size], x_scaled[x_train_size:len(x)]
y_train, y_test = y_scaled[0:y_train_size], y_scaled[y_train_size:len(y)]
x_train=x_train.reshape(x_train_size,1,18)
y_train=y_train.reshape(y_train_size,1,1)
model = Sequential()
model.add(LSTM(10, return_sequences=True,batch_input_shape=(32,1,18)))
model.add(LSTM(10,return_sequences=True))
model.add(LSTM(1,return_sequences=True, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam', metrics=
['accuracy'])
model.fit(x_train, y_train, epochs=10,batch_size=32)
score = model.evaluate(x_test, y_test,batch_size=32)
predicted = model.predict(x_test)
predicted = y_scaler.inverse_transform(predicted)
predicted = [x if x > 0 else 0 for x in predicted]
correct_values = y_scaler.inverse_transform(y_test)
correct_values = [x if x > 0 else 0 for x in correct_values]
print(nb_predict_train)
I Get the Error: 我得到错误:
ValueError: Error when checking input: expected lstm_1_input to have 3 dimensions, but got array with shape (1257, 18) ValueError:检查输入时出错:预期lstm_1_input具有3个维,但数组的形状为(1257,18)
After the last line of code. 最后一行代码之后。
I also tried to reshape the test data but then I get a very similar error. 我也尝试过重塑测试数据,但是后来我得到了一个非常相似的错误。
I think, I'm missing something very easy or basic but I can't figure it out at the moment, as I'm just a beginner in coding neuronal networks. 我认为,我缺少一些非常简单或基本的东西,但是由于我只是编码神经元网络的初学者,因此目前无法解决。 I need this for my master thesis so I would be very thank full if anyone could help me out. 我的硕士论文需要这个,所以如果有人可以帮助我,我将非常感谢。
The problem is that your model input batch_input_shape
is fixed. 问题是您的模型输入batch_input_shape
是固定的。 The length of your test length is 1257 and cannot be divisible by 32. It should be changed as follows: 测试长度的长度为1257,不能被32整除。应按以下步骤进行更改:
model.add(LSTM(10, return_sequences=True,batch_input_shape=(None,1,18)))
You should modify test shape before the model evaluate test. 您应该在模型评估测试之前修改测试形状。
x_test= x_test.reshape(len(x)-x_train_size,1,18)
y_test= y_test.reshape(len(y)-x_train_size,1,1)
score = model.evaluate(x_test, y_test,batch_size=32)
Of course, you have to reshape predicted
and y_test
before inverse_transform
. 当然,您必须在inverse_transform
之前重塑predicted
和y_test
。
predicted = model.predict(x_test)
predicted= predicted.reshape(len(y)-x_train_size,1)
y_test= y_test.reshape(len(y)-x_train_size,1)
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