Exception has occurred: ModuleNotFoundError No module named 'tensorflow.python'

Question

im working on Simple RNN code with python, i want to use keras but when i run the code it show to me ( tensorflow error ), i uninstall tensorflow then i installed it again but same problem, tensorflow is there and keras also installed,the long path fixed, what is the problem??

this is my code below:

from operator import index
from pyexpat import model
from re import X
from tkinter import Y
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, SimpleRNN

def convertToMatrix(data, step):
    X, Y =[], []
    for i in range(len(data)-step):
      d=i+step
      X.append(data[i:d,])
      Y.append(data[d,])
    return np.array(X), np.array(Y)

step = 4
N = 1000
Tp = 800

# here below we made generate to the wave as sin wave

t=np.arrange(0,N)
x=np.sin(0.02*t)+2*np.random.rand(N)
# random order is to make some noise on wave
df = pd.DataFrame(x)
df.head()

plt.plot(df)
plt.show()

values=df.values
train,test = values[0:Tp,:], values[Tp:N,:]

# add step elements into train and test

test = np.append(test,np.repeat(test[-1,],step))
train = np.append(train,np.repeat(train[-1,],step))

trainX,trainY =convertToMatrix(train,step)
testX,testY =convertToMatrix(test,step)
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX,(testX.shape[0], 1, testX.shape[1]))


model = Sequential()
model.add(SimpleRNN(units=32, input_shape=(1,step), activation="relu"))
model.add(Dense(8, activation="relu"))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='rmsprop')
model.summary()

# simpleRNN is layer also Dense is layer too

model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
predicted=np.concatenate((trainPredict,testPredict), axis=0)

trainScore = model.evaluate(trainX, trainY, verbose=0)
print(trainScore)

index = df.index.values
plt.plot(index,df)
plt.plot(index,predicted)
plt.axvline(df.index[Tp], c="r")
plt.show()