--- a
+++ b/autoencoder_DCAP.py
@@ -0,0 +1,113 @@
+
+
+import tensorflow as tf
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+with open(r"C:\pypro\brcatest_go.csv", 'r') as f:
+    data = pd.read_csv(f)
+
+print(data.shape)
+tcga_input=np.transpose(data)
+print(tcga_input.shape)
+
+learning_rate = 0.01
+training_epochs = 10
+batch_size = 50
+display_step = 1
+examples_to_show = 10
+
+dropout=0.1
+n_input = 60779
+scale = 0.0001
+# tf Graph input (only pictures)
+X = tf.placeholder("float", [None, n_input])
+
+
+n_hidden_1 = 500 # 
+n_hidden_2 = 200 # 
+
+weights = {
+    'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
+    'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
+    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),
+    'decoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_input])),
+}
+biases = {
+    'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
+    'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),
+    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),
+    'decoder_b2': tf.Variable(tf.random_normal([n_input])),
+}
+
+
+def encoder(x):
+    layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['encoder_h1']),
+                                   biases['encoder_b1']))
+    layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['encoder_h2']),
+                                   biases['encoder_b2']))
+    return layer_2
+
+
+
+def decoder(x):
+    layer_1 = tf.nn.tanh(tf.add(tf.matmul(x, weights['decoder_h1']),
+                                   biases['decoder_b1']))
+    layer_2 = tf.nn.tanh(tf.add(tf.matmul(layer_1, weights['decoder_h2']),
+                                   biases['decoder_b2']))
+    return layer_2
+
+
+##################################################################
+
+fc_1 = tf.layers.dense(inputs=X, units=n_hidden_1,
+                       kernel_regularizer=tf.contrib.layers.l2_regularizer(scale=scale))
+fc_1_out = tf.nn.tanh(fc_1)
+fc_1_dropout = tf.layers.dropout(inputs=fc_1_out, rate=dropout)
+
+fc_2 = tf.layers.dense(inputs = fc_1_dropout, units = n_hidden_2, kernel_regularizer= tf.contrib.layers.l2_regularizer(scale=scale))
+fc_2_out = tf.nn.tanh(fc_2)
+encoder_op = tf.layers.dropout(inputs=fc_2_out, rate=dropout)
+
+fc_3 = tf.layers.dense(inputs = encoder_op, units = n_hidden_1, kernel_regularizer= tf.contrib.layers.l2_regularizer(scale=scale))
+fc_3_out = tf.nn.tanh(fc_3)
+fc_3_dropout = tf.layers.dropout(inputs=fc_3_out, rate=dropout)
+
+decoder_op = tf.layers.dense(inputs=fc_3_dropout, units=n_input)
+##################################################################
+
+
+y_pred = decoder_op
+y_true = X
+
+
+cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))#+lossL 
+l2_loss = tf.losses.get_regularization_loss()
+optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost+l2_loss)
+
+with tf.Session() as sess:
+    # tf.initialize_all_variables() no long valid from
+    # 2017-03-02 if using tensorflow >= 0.12
+    if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
+        init = tf.initialize_all_variables()
+    else:
+        init = tf.global_variables_initializer()
+    sess.run(init)
+  
+    total_batch = int(len(tcga_input)/batch_size) 
+    for epoch in range(training_epochs):
+        for i in range(total_batch):
+            # tch_xs, batch_ys = mnist.train.next_batch(batch_size)  # max(x) = 1, min(x) = 0
+            batch_xs = tcga_input[((i)*batch_size):((i+1)*batch_size)]
+            # Run optimization op (backprop) and cost op (to get loss value)
+            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})
+        if epoch % display_step == 0:
+            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c))
+        if epoch == training_epochs - 1:
+                fea_output = sess.run([encoder_op], feed_dict={X: tcga_input})
+                # print(fea_output)
+                print(np.array(fea_output).shape)
+                np.savetxt(r'C:\pypro\fea.csv', np.array(fea_output[0]), delimiter=',')
+    print("Optimization Finished!")
+
+