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- #! /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')
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