play tensorlayer book Issue 11

gistfile1.txt

"""
ch 3.5
https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mnist.py

code for https://github.com/tensorlayer/chinese-book/issues/11
"""

import time
import numpy as np
import tensorflow as tf
import tensorlayer as tl

import matplotlib as mpl

mpl.use('TkAgg')

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

X_train = np.asarray(X_train, dtype=np.float32)
y_train = np.asarray(y_train, dtype=np.int32)
X_val = np.asarray(X_val, dtype=np.float32)
y_val = np.asarray(y_val, dtype=np.int32)
X_test = np.asarray(X_test, dtype=np.float32)
y_test = np.asarray(y_test, dtype=np.int32)

print('X_train.shape', X_train.shape)
print('y_train.shape', y_train.shape)
print('X_val.shape', X_val.shape)
print('y_val.shape', y_val.shape)
print('X_test.shape', X_test.shape)
print('y_test.shape', y_test.shape)
print('X %s   y %s' % (X_test.dtype, y_test.dtype))

save_path = './ae_tl/autoencoder2.ckpt'

image_width = 28

saver = None

# 模型结构参数
hidden_size = 196
input_size = 784


def main_layers(model='relu', is_train=True, reuse=False):
    """build network
    :param model: 模型类别，可选Sigmoid或Relu
    """
    with tf.variable_scope("ae", reuse=reuse):
        # set model reuse
        tl.layers.set_name_reuse(reuse)

        # # 定义模型
        x = tf.placeholder(tf.float32, shape=[None, input_size], name='x')

        # 输入层 f(x)
        network = tl.layers.InputLayer(x, name='input')

        # ch3.4 introduce noise to input data
        network = tl.layers.DropoutLayer(network, keep=0.5, is_train=is_train, is_fix=False, name='denoising1')

        print('Build Network')
        if model == 'relu':
            network = tl.layers.DenseLayer(network, hidden_size, tf.nn.relu, name='relu1')

            # 隐层输出
            encoded_img = network.outputs

            # 重构层输出 g(h)
            # recon_layer1 = tl.layers.DenseLayer(network, input_size, tf.nn.softplus, name='recon_layer1')
            recon_layer1 = tl.layers.ReconLayer(network, x_recon=x, n_units=784, act=tf.nn.softplus,
                                                name='recon_layer1')

        elif model == 'sigmoid':
            network = tl.layers.DenseLayer(network, hidden_size, tf.nn.sigmoid, name='sigmoid1')

            # 隐层输出
            encoded_img = network.outputs

            # 重构层输出 g(h)
            # recon_layer1 = tl.layers.DenseLayer(network, input_size, tf.nn.sigmoid, name='recon_layer1')
            recon_layer1 = tl.layers.ReconLayer(network, x_recon=x, n_units=784, act=tf.nn.sigmoid,
                                                name='recon_layer1')

        return x, recon_layer1


def train_layers(model='relu'):
    global saver
    # 定义超参数
    n_epochs = 10
    batch_size = 128
    print_interval = 200

    x, recon_layer1 = main_layers(model, is_train=True, reuse=False)

    saver = tf.train.Saver()

    with tf.Session() as sess:
        tl.layers.initialize_global_variables(sess)
        recon_layer1.pretrain(sess, x=x, X_train=X_train, X_val=X_val, denoise_name='ae/denoising1', n_epoch=n_epochs,
                              batch_size=batch_size, print_freq=print_interval, save=True, save_name='w1pre_')

        # 保存模型为TensorFlow的ckpt格式
        saver.save(sess, save_path=save_path)
        print('model saved.')


if __name__ == '__main__':
    all_start_time = time.time()
    model = 'sigmoid'
    train_layers(model=model)

    print('all finished took %.2fs' % (time.time() - all_start_time))