Download and Install the TensorFlow 2.0 Beta Package.
Import TensorFlow into Program
from __future__ import absolute_import,division,print_function,unicode_literal
pip install tensorflow==2.0.0-beta1
import tensorflow as tf
Load and prepare the MNIST dataset. Convert the samples from integers to Floating-point Numbers.
mnist=tf.keras.datasets.mnist
(x_train,y_train),(x_test,y_test)=mnist.load_data()
x_train,x_test=x_train/255.0,x_test/255.0 (Normalisation)
Build the tf.keras.Sequential model by stacking layes. Choose an optimizer and loss function for training.
model=tf.keras.model.Sequential([tf.keras.layers.Flatten(input_shape=(28,28)),tf.keras.layers.Dense(128,activation=’relu’),tf.keras.layers.Dropout(0.2),tf.keras.layers.Dense(10,activation=’softmax’)])
model.compile(optimizer=’adam’,loss=’sparse_categorical_crossentropy’,metrics=[‘accuracy’])
Train and evaluate the model.
model.fit(x_train,y_train,epochs=5)
model.evaluate(x_test,y_test)