arisliang · March 2, 2018 13:44 · Mar 2, 2018
diff --git a/gistfile1.txt b/gistfile1.txt
@@ -0,0 +1,115 @@
+"""
+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))
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