forked from TensorLayer/tensorlayer3
47 lines
1.6 KiB
Python
47 lines
1.6 KiB
Python
#! /usr/bin/python
|
|
# -*- coding: utf-8 -*-
|
|
import os
|
|
os.environ['TL_BACKEND'] = 'tensorflow'
|
|
|
|
from tensorlayer.layers import SequentialLayer
|
|
from tensorlayer.layers import Dense
|
|
import tensorlayer as tl
|
|
import numpy as np
|
|
|
|
layer_list = []
|
|
layer_list.append(Dense(n_units=800, act=tl.ReLU, in_channels=784, name='Dense1'))
|
|
layer_list.append(Dense(n_units=800, act=tl.ReLU, in_channels=800, name='Dense2'))
|
|
layer_list.append(Dense(n_units=10, act=tl.ReLU, in_channels=800, name='Dense3'))
|
|
MLP = SequentialLayer(layer_list)
|
|
|
|
X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 784))
|
|
|
|
|
|
def generator_train():
|
|
inputs = X_train
|
|
targets = y_train
|
|
if len(inputs) != len(targets):
|
|
raise AssertionError("The length of inputs and targets should be equal")
|
|
for _input, _target in zip(inputs, targets):
|
|
yield (_input, np.array(_target))
|
|
|
|
|
|
n_epoch = 50
|
|
batch_size = 128
|
|
print_freq = 2
|
|
shuffle_buffer_size = 128
|
|
|
|
# train_weights = MLP.trainable_weights
|
|
# print(train_weights)
|
|
optimizer = tl.optimizers.Momentum(0.05, 0.9)
|
|
train_ds = tl.dataflow.FromGenerator(
|
|
generator_train, output_types=(tl.float32, tl.int32), column_names=['data', 'label']
|
|
)
|
|
train_ds = tl.dataflow.Shuffle(train_ds, shuffle_buffer_size)
|
|
train_ds = tl.dataflow.Batch(train_ds, batch_size)
|
|
|
|
model = tl.models.Model(network=MLP, loss_fn=tl.cost.softmax_cross_entropy_with_logits, optimizer=optimizer)
|
|
model.train(n_epoch=n_epoch, train_dataset=train_ds, print_freq=print_freq, print_train_batch=False)
|
|
model.save_weights('./model.npz', format='npz_dict')
|
|
model.load_weights('./model.npz', format='npz_dict')
|