import keras 
from keras.datasets import mnist
from keras.layers import BatchNormalization
from keras.layers import Activation, Dense, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.models import Sequential
from keras.optimizers import SGD
 
batch_size = 128
epochs = 20
num_classes = 10
 
# input image dimensions
img_rows, img_cols = 28, 28
 
(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
 
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
 
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
 
model = Sequential()
model.add(Conv2D(24,
                 kernel_initializer="he_normal",
                 kernel_size=(5, 5),
                 input_shape=(img_rows, img_cols, 1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D())
model.add(Conv2D(48,
                 kernel_initializer="he_normal",
                 kernel_size=(5, 5)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dense(512,
                kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(num_classes,
                activation='softmax',
                kernel_initializer="he_normal"))
model.summary()
model.compile(loss='categorical_crossentropy',
              optimizer=SGD(lr=0.5),
              metrics=['accuracy'])
 
history = model.fit(x_train,
                    y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    validation_data=(x_test, y_test))

import keras 
from keras.datasets import mnist
from keras.layers import BatchNormalization
from keras.layers import Activation, Dense, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.models import Sequential
from keras.optimizers import SGD
 
batch_size = 128
epochs = 20
learning_rate = 0.5
num_classes = 10
 
# input image dimensions
img_rows, img_cols = 28, 28
 
(x_train, y_train), (x_test, y_test) = mnist.load_data()
 
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
 
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
 
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
 
model = Sequential()
model.add(Conv2D(24,
                 kernel_initializer="he_normal",
                 kernel_size=(5, 5),
                 input_shape=(img_rows, img_cols, 1)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D())
model.add(Conv2D(48,
                 kernel_initializer="he_normal",
                 kernel_size=(5, 5)))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D())
model.add(Flatten())
model.add(Dense(512,
                kernel_initializer="he_normal"))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dense(num_classes,
                activation='softmax',
                kernel_initializer="he_normal"))
model.summary()
model.compile(loss='categorical_crossentropy',
              optimizer=SGD(lr=learning_rate),
              metrics=['accuracy'])
 
history = model.fit(x_train,
                    y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    validation_data=(x_test, y_test))

Update Keras.py