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)


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