code1

import numpy as np
import keras as k
import matplotlib.pyplot as plt

from keras.layers import Conv2D, MaxPooling2D , Flatten , Dense

from keras.datasets import mnist

model = k.Sequential()
model.add(Conv2D(kernel_size = 3, filters = 64 , activation = "relu", input_shape = (28,28,1)))

model.add(Conv2D(64, 3,activation = "relu"))

model.add(MaxPooling2D(2,2))

model.add(Flatten())

model.add(Dense(128, activation = "relu"))

model.add(Dense(10, activation = "softmax"))



model.compile(optimizer = "rmsprop" , loss = "categorical_crossentropy", metrics = ["accuracy"])

(x_train, y_train),(x_test, y_test) = mnist.load_data()
ohl = k.utils.to_categorical(y_train, 10)

plt.imshow(x_train[0])

plt.show()

print(y_train[0])

x_train = np.reshape(x_train, (-1,28, 28,1))/255

model.fit(x_train, ohl, epochs = 3)


print(model.summary())

print(x_train.shape)

g = int(input("Pick a Number (0-100): "))
image =x_test[g]
image = np.reshape(image , (1,28,28,1))
print(model.predict([image]))
dis = np.reshape(image , (28,28))/255
plt.imshow(dis)