Error when checking input: expected lstm_132_input to have 3 dimensions, but got array with shape (23, 1, 3, 1) - python-3.x

Question

I have a data set include with temperature, humidity and wind. Here I want to predict future temperature value in next hour.
I used LSTM to predict future temperature value.
But when I run the model it showed up this error Error when checking input: expected lstm_132_input to have 3 dimensions, but got array with shape (23, 1, 3, 1)
Can anyone help me to solve this problem?
Here is my code:
import datetime
import time
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn import preprocessing
from keras.layers.core import Dense, Dropout, Activation
from keras.activations import linear
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler
data = pd.read_csv('data6.csv' , sep=',')
data['date'] = pd.to_datetime(data['date'] + " " + data['time'], format='%m/%d/%Y %H:%M:%S')
data.set_index('time', inplace=True)
data = data.values
data = data.astype('float32')
# normalize the dataset
def create_data(train,X,n_out=1):
#data = np.reshape(train, (train.shape[0], train_shape[1], train_shape[2]))
x,y=list(),list()
start =0
for _ in range(len(data)):
in_end = start+X
out_end= in_end + n_out
if out_end < len(data):
x_input = data[start:in_end]
x.append(x_input)
y.append(data[in_end:out_end,0])
start +=1
return np.array(x),np.array(y)
scaler = MinMaxScaler()
data = scaler.fit_transform(data)
# split into train and test sets
train = int(len(data) * 0.6)
test = len(data) - train
train, test = data[0:train,:], data[train:len(data),:]
X=1
x_train, y_train = create_data(train,X)
x_test, y_test = create_data(test,X)
x_train=x_train.reshape(x_train.shape +(1,))
x_test=x_test.reshape(x_test.shape + (1,))
n_timesteps, n_features, n_outputs = x_train.shape[1], x_train.shape[2], x_train.shape[1]
model = Sequential()
model.add(LSTM(8, activation='relu', input_shape=(n_timesteps, n_features)))
model.add(Dense(8,activation='relu'))
model.add(Dense(n_outputs))
model.compile(loss='mse', optimizer='adam')
# fit network
model.fit(x_train,y_train, epochs=10,batch_size=1, verbose=0)
My csv file:
My csv file.
My error:
model summary :

Answer 1

you need to add activation to your last layer
model = Sequential()
model.add(LSTM(8, activation='relu', input_shape=(n_timesteps, n_features)))
model.add(Dense(8,activation='relu'))
# here
model.add(Dense(n_outputs,activation='relu'))
model.compile(loss='mse', optimizer='adam')
# fit network
model.fit(x_train,y_train, epochs=10,batch_size=1, verbose=0)