!pip install tensorflow==2.0-beta1 #Tensorflow and tf.keras import tensorflow as tf from tensorflow import keras #Helper libraries import numpy as np import matplotlib as plt print(tf.version) class_names=["Tshirt/Top","Trouser","Pullover","Dress","Coat","Sandal","Shirt","Sneaker","Bag","Ankle boot"] import matplotlib.pyplot as plt def display_image(img): plt.imshow(img,cmap=plt.cm.binary) plt.show()
display_image(train_images[0]) train_images=train_images/255 test_images=test_images/255 model= keras.Sequential([ keras.layers.Flatten(input_shape=(28,28)), keras.layers.Dense(128,activation="relu"), keras.layers.Dense(10,activation="softmax") ]) model.compile( optimizer="adam", loss="sparse_categorical_crossentropy", metrics="accuracy" ) model.fit(train_images,train_labels,epochs=10) test_loss,test_accuracy=model.evaluate(test_images,test_labels) print(test_accuracy) predictions=model.predict(test_images) for i in range(0,10): display_image(test_images[i]) ind=np.argmax(predictions[i]) print(class_names[ind])