diff --git a/deepLearning/05_sda.py b/deepLearning/05_sda.py
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+++ b/deepLearning/05_sda.py
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+"""
+Stacked Denoising Autoencoders (SDA)
+author: Ye Hu
+2016/12/16
+redit：wanyouwen 2018/05/02
+"""
+import timeit
+import numpy as np
+import tensorflow as tf
+import input_data
+
+from logisticRegression import LogisticRegression
+from mlp import HiddenLayer
+from da import DA
+
+class SdA(object):
+    """
+    Stacked denoising autoencoder class
+    the model is constructed by stacking several dAs
+    the dA layers are used to initialize the network, after pre-training,
+    the SdA is similar to a normal MLP
+    """
+    def __init__(self, n_in=784, n_out=10, hidden_layers_sizes=(500, 500),
+                 corruption_levels=(0.1, 0.1)):
+        """
+        :param n_in: int, the dimension of input
+        :param n_out: int, the dimension of output
+        :param hidden_layers_sizes: list or tuple, the hidden layer sizes
+        :param corruption_levels: list or tuple, the corruption lever for each layer
+        """
+        assert len(hidden_layers_sizes) >= 1
+        assert len(hidden_layers_sizes) == len(corruption_levels)
+        self.corruption_levels = corruption_levels
+        self.n_layers = len(hidden_layers_sizes)
+        # define the layers
+        self.layers = []   # the normal layers
+        self.dA_layers = []  # the dA layers
+        self.params = []     # params
+        # define the input and output
+        self.x = tf.placeholder(tf.float32, shape=[None, n_in])
+        self.y = tf.placeholder(tf.float32, shape=[None, n_out])
+        # construct the layers
+        for i in range(self.n_layers):
+            if i == 0:  # the input layer
+                input_size = n_in
+                layer_input = self.x
+            else:
+                input_size = hidden_layers_sizes[i-1]
+                layer_input = self.layers[i-1].output
+            # create the sigmoid layer
+            sigmoid_layer = HiddenLayer(inpt=layer_input, n_in=input_size,
+                                         n_out=hidden_layers_sizes[i], activation=tf.nn.sigmoid)
+            self.layers.append(sigmoid_layer)
+            # create the da layer
+            dA_layer = DA(inpt=layer_input, n_hidden=hidden_layers_sizes[i], n_visiable=input_size,
+                          W=sigmoid_layer.W, bhid=sigmoid_layer.b)
+            self.dA_layers.append(dA_layer)
+
+            # collect the params
+            self.params.extend(sigmoid_layer.params)
+
+        # add the output layer
+        self.output_layer = LogisticRegression(inpt=self.layers[-1].output, n_in=hidden_layers_sizes[-1],
+                                               n_out=n_out)
+        self.params.extend(self.output_layer.params)
+
+        # the finetuning cost
+        self.finetune_cost = self.output_layer.cost(self.y)
+        # the accuracy
+        self.accuracy = self.output_layer.accuarcy(self.y)
+
+    def pretrain(self, sess, X_train, pretraining_epochs=10, batch_size=100, learning_rate=0.001,
+                 display_step=1):
+        """
+        Pretrain the layers
+        :param sess: tf.Session
+        :param X_train: the input of the train set
+        :param batch_size: int
+        :param learning_rate: float
+        """
+        print('Starting pretraining...')
+        start_time = timeit.default_timer()
+        batch_num = int(X_train.train.num_examples / batch_size)
+        for i in range(self.n_layers):
+            # pretraining layer by layer
+            cost = self.dA_layers[i].get_cost(corruption_level=self.corruption_levels[i])
+            params = self.dA_layers[i].params
+            train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost, var_list=params)
+            for epoch in range(pretraining_epochs):
+                avg_cost = 0.0
+                for j in range(batch_num):
+                    x_batch, _ = X_train.train.next_batch(batch_size)
+                    # 训练
+                    sess.run(train_op, feed_dict={self.x: x_batch})
+                    # 计算cost
+                    avg_cost += sess.run(cost, feed_dict={self.x: x_batch,}) / batch_num
+                # 输出
+                if epoch % display_step == 0:
+                    print("Pretraing layer {0} Epoch {1} cost: {2}".format(i, epoch, avg_cost))
+
+        end_time = timeit.default_timer()
+        print("The pretraining process ran for {0}m".format((end_time - start_time) / 60))
+
+    def finetuning(self, sess, trainSet, training_epochs=10, batch_size=100, learning_rate=0.1,
+                   display_step=1):
+        """Finetuing the network"""
+        print("Start finetuning...")
+        start_time = timeit.default_timer()
+        train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(
+            self.finetune_cost, var_list=self.params)
+        for epoch in range(training_epochs):
+            avg_cost = 0.0
+            batch_num = int(trainSet.train.num_examples / batch_size)
+            for i in range(batch_num):
+                x_batch, y_batch = trainSet.train.next_batch(batch_size)
+                # 训练
+                sess.run(train_op, feed_dict={self.x: x_batch, self.y: y_batch})
+                # 计算cost
+                avg_cost += sess.run(self.finetune_cost, feed_dict=
+                {self.x: x_batch, self.y: y_batch}) / batch_num
+            # 输出
+            if epoch % display_step == 0:
+                val_acc = sess.run(self.accuracy, feed_dict={self.x: trainSet.validation.images,
+                                                       self.y: trainSet.validation.labels})
+                print("  Epoch {0} cost: {1}, validation accuacy: {2}".format(epoch, avg_cost, val_acc))
+
+        end_time = timeit.default_timer()
+        print("The finetuning process ran for {0}m".format((end_time - start_time) / 60))
+
+
+if __name__ == "__main__":
+    # mnist examples
+    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+    sda = SdA(n_in=784, n_out=10, hidden_layers_sizes=[500, 500, 500], corruption_levels=[0.1, 0.2, 0.2])
+    sess = tf.Session()
+    init = tf.global_variables_initializer()
+    sess.run(init)
+    # set random_seed
+    tf.set_random_seed(seed=1111)
+    sda.pretrain(sess, X_train=mnist)
+    sda.finetuning(sess, trainSet=mnist)
+