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"""Sample task script."""

import tensorflow as tf

import tensorlayer as tl

# tf.logging.set_verbosity(tf.logging.DEBUG)

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

# connect to database

db = tl.db.TensorHub(ip='localhost', port=27017, dbname='temp', project_name='tutorial')

# load dataset from database

X_train, y_train, X_val, y_val, X_test, y_test = db.find_top_dataset('mnist')

# define the network

def mlp():

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

net = tl.layers.Dropout(keep=0.8, name='drop1')(ni)

net = tl.layers.Dense(n_units=n_units1, act=tf.nn.relu, name='relu1')(net)

net = tl.layers.Dropout(keep=0.5, name='drop2')(net)

net = tl.layers.Dense(n_units=n_units2, act=tf.nn.relu, name='relu2')(net)

net = tl.layers.Dropout(keep=0.5, name='drop3')(net)

net = tl.layers.Dense(n_units=10, act=None, name='output')(net)

M = tl.models.Model(inputs=ni, outputs=net)

return M

network = mlp()

# cost and accuracy

cost = tl.cost.cross_entropy

def acc(y, y_):

correct_prediction = tf.equal(tf.argmax(y, 1), tf.convert_to_tensor(y_, tf.int64))

return tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# define the optimizer

train_op = tf.optimizers.Adam(learning_rate=0.0001)

# train the network

# tl.utils.fit(

# network, train_op, cost, X_train, y_train, acc=acc, batch_size=500, n_epoch=20, print_freq=5,

# X_val=X_val, y_val=y_val, eval_train=False

# )

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=False,

)

# evaluation and save result that match the result_key

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

test_accuracy = float(test_accuracy)

# save model into database

db.save_model(network, model_name='mlp', name=str(n_units1) + '-' + str(n_units2), test_accuracy=test_accuracy)

# in other script, you can load the model as follow

# net = db.find_model(sess=sess, model_name=str(n_units1)+'-'+str(n_units2)