modelgen.py

import tensorflow as tf
from tensorflow.keras import layers,models
(train_images,train_labels),(test_images,test_labels) = tf.keras.datasets.mnist.load_data()

train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32')/255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32')/255

train_labels = tf.keras.utils.to_categorical(train_labels)
print('train_labels')
test_labels = tf.keras.utils.to_categorical(test_labels)

model = models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64,activation='relu'))
model.add(layers.Dense(10,activation='softmax'))

model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
model.fit(train_images,train_labels,epochs=5,batch_size=64,validation_split=0.1)

model.save('model.h5')

print('Here am I')