import numpy as np frommatplotlib import pyplot as plt fromkeras.datasets import mnist (ti,tl),(tei,tel)=mnist.load_data() for i in range(20): plt.subplot(5,4,i+1) plt.imshow(ti[i],cmap='gray_r') plt.title("label:{}".format(tl[i])) plt.subplots_adjust(hspace=0.5) plt.axis('off') ti=ti/255.0 tei=tei/255.0 fromkeras.models import Sequential fromkeras.layers import Flatten,Dense nn=Sequential() Flatten(input_shape=(28,28)) nn.add(Flatten(input_shape=(28,28))) nn.add(Dense(784,activation='relu')) nn.add(Dense(512,activation='relu')) nn.add(Dense(10,activation='softmax')) nn.compile(optimizer='adam',loss='sparse_categorical_crossentropy', metrics=['accuracy']) nn.fit(ti,tl,epochs=3) plt.imshow(tei[3],cmap='gray_r') plt.title("label:{}".format(tel[3])) p=nn.predict(tei) plt.axis('off') iftel[3]==np.argmax(p[3]): print("Correct Prediction..................... ") else: print("Incorrect Prediction……" Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz 11490434/11490434 [==============================] - 0s 0us/step 1875/1875 [==============================] - 27s 14ms/step - loss: 0.0785 - accuracy: 0.9759 Epoch 3/ 1875/1875 [==============================] - 26s 14ms/step - loss: 0.0539 - accuracy: 0.9834 313/313 [==============================] - 1s 3ms/step