How to fix Import Error: cannot import name ‘SGD‘ from ‘keras.optimizers‘

Errors : ImportError: cannot import name 'SGD' from 'keras.optimizers' 

 Original Code:

# Baseline Neural Network For The Sonar Dataset
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from keras.constraints import maxnorm
from keras.optimizers import SGD
#from tensorflow.keras.optimizers import SGD
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# fix random seed for reproducibility
seed = 7
np.random.seed(seed)

# load dataset
dataframe = pd.read_csv("sonar.csv",header=None)
dataset = dataframe.values

# split into input(X) and output(Y) variables
X = dataset[:,0:60].astype(float)
Y = dataset[:,60]

# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoder_Y = encoded.transform(Y)

# baseline
def create_baseline():
    # create model
    model = Sequential()
    model.add(Dense(60, input_dim=60, kernel_initializer='normal',activation='relu'))
    model.add(Dense(30, kernel_initializer='normal',activation='relu'))
    model.add(Dense(1, kernel_initializer='normal',activation='sigmoid'))
    # compile model
    sgd = SGD(lr=0.01, momentum=0.8, decay=0.0, nesterov=False)
    model.compile(loss='binary_crossentropy',optimizer=sgd, metrics=['accuracy'])
    return model

np.random.seed(seed)
estimators = []
estimators.append(('standardize',StandardScaler()))
estimators.append(('mlp',KerasClassifier(build_fn=create_baseline,epochs=300,batch_size=16, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))

Modified Code:

from tensorflow.keras.optimizers import SGD 

instead of

from keras.optimizers import SGD

# Baseline Neural Network For The Sonar Dataset
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from keras.constraints import maxnorm
# from keras.optimizers import SGD
from tensorflow.keras.optimizers import SGD
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

# fix random seed for reproducibility
seed = 7
np.random.seed(seed)

# load dataset
dataframe = pd.read_csv("sonar.csv",header=None)
dataset = dataframe.values

# split into input(X) and output(Y) variables
X = dataset[:,0:60].astype(float)
Y = dataset[:,60]

# encode class values as integers
encoder = LabelEncoder()
encoder.fit(Y)
encoded_Y = encoder.transform(Y)

# baseline
def create_baseline():
    # create model
    model = Sequential()
    model.add(Dense(60, input_dim=60, kernel_initializer='normal',activation='relu'))
    model.add(Dense(30, kernel_initializer='normal',activation='relu'))
    model.add(Dense(1, kernel_initializer='normal',activation='sigmoid'))
    # compile model
    sgd = SGD(lr=0.01, momentum=0.8, decay=0.0, nesterov=False)
    model.compile(loss='binary_crossentropy',optimizer=sgd, metrics=['accuracy'])
    return model

np.random.seed(seed)
estimators = []
estimators.append(('standardize',StandardScaler()))
estimators.append(('mlp',KerasClassifier(build_fn=create_baseline,epochs=300,batch_size=16, verbose=0)))
pipeline = Pipeline(estimators)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=seed)
results = cross_val_score(pipeline, X, encoded_Y, cv=kfold)
print("Baseline: %.2f%% (%.2f%%)" % (results.mean()*100, results.std()*100))