#!/usr/bin/env python3
# -*- coding:utf-8 -*-
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# File: m1_test.py
# Created Date: Saturday July 9th 2022
# Author: Smiril
# Email: sonar@gmx.com
#############################################################
import tensorflow as tf
tf.__version__
tf.config.list_physical_devices()
logits = [[4.0, 2.0, 1.0], [0.0, 5.0, 1.0]]
inputs = tf.keras.Input(shape=(784,), name="digits")
model = tf.keras.models.load_model('model')
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
model = tf.keras.models.Sequential([
 tf.keras.layers.Flatten(input_shape=(28, 28)),
 tf.keras.layers.Dense(128,activation='selu',name='layer1'),
 tf.keras.layers.Dropout(0.2),
 tf.keras.layers.Dense(64,activation='relu',name='layer2'),
 tf.keras.layers.Dropout(0.2),
 tf.keras.layers.Dense(32,activation='elu',name='layer3'),
 tf.keras.layers.Dropout(0.2),
 tf.keras.layers.Dense(16,activation='tanh',name='layer4'),
 tf.keras.layers.Dropout(0.2),
 tf.keras.layers.Dense(8,activation='sigmoid',name='layer5'),
 tf.keras.layers.Dropout(0.2)
])
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer='adam',
 loss=loss_fn,
 metrics=['accuracy'])
model.fit(x_test, y_test, epochs=10)
outputs = tf.keras.layers.Dense(4, activation="softmax", name="predictions")(x_test)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.build()
model.save('model')
model.summary()