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#! /usr/bin/python

# -*- coding: utf-8 -*-

import numpy as np

import tensorflow as tf

import tensorlayer as tl

tl.logging.set_verbosity(tl.logging.DEBUG)

# set gpu mem fraction or allow growth

# tl.utils.set_gpu_fraction()

# prepare data

X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))

# define the network

ni = tl.layers.Input([None, 784])

nn = tl.layers.Dropout(keep=0.8)(ni)

nn = tl.layers.Dense(n_units=800, act=tf.nn.relu)(nn)

nn = tl.layers.Dropout(keep=0.5)(nn)

nn = tl.layers.Dense(n_units=800, act=tf.nn.relu)(nn)

nn = tl.layers.Dropout(keep=0.5)(nn)

nn = tl.layers.Dense(n_units=10, act=None)(nn)

network = tl.models.Model(inputs=ni, outputs=nn, name="mlp")

# define metric.

def acc(_logits, y_batch):

# return np.mean(np.equal(np.argmax(_logits, 1), y_batch))

return tf.reduce_mean(

tf.cast(tf.equal(tf.argmax(_logits, 1), tf.convert_to_tensor(y_batch, tf.int64)), tf.float32), name='accuracy'

)

# print network information

print(network)

# open tensorboard

# tl.utils.open_tensorboard('./tb_log', port=6006)

# train the network

tl.utils.fit(

network, train_op=tf.optimizers.Adam(learning_rate=0.0001), cost=tl.cost.cross_entropy, X_train=X_train,

y_train=y_train, acc=acc, batch_size=256, n_epoch=20, X_val=X_val, y_val=y_val, eval_train=True,

tensorboard_dir='./tb_log'

)

# test

tl.utils.test(network, acc, X_test, y_test, batch_size=None, cost=tl.cost.cross_entropy)

# evaluation

_logits = tl.utils.predict(network, X_test)

y_pred = np.argmax(_logits, 1)

tl.utils.evaluation(y_test, y_pred, n_classes=10)

# save network weights

network.save_weights('model.h5')

# close tensorboard

# tl.utils.exit_tensorflow(port=6006)