莫烦TensorFlow_10 过拟合

import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import LabelBinarizer

#load data
digits = load_digits()
X = digits.data
y = digits.target
y = LabelBinarizer().fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3)

 #
 # add layer
 #
def add_layer(inputs, in_size, out_size, n_layer, activation_function = None):  
  layer_name = 'layer%s' % n_layer

  Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')  # hang lie  
  biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name = 'b')  
   
  Wx_plus_b = tf.matmul(inputs, Weights) + biases  
  Wx_plus_b = tf.nn.dropout(Wx_plus_b, keep_prob) #
    
  if activation_function is None:  
    outputs = Wx_plus_b  
  else:  
    outputs = activation_function(Wx_plus_b)  
      
  tf.summary.histogram(layer_name + '/outputs', outputs)  
  return outputs  
  
  
#
# define placeholder for inputs to network
#
keep_prob = tf.placeholder(tf.float32) # 
xs = tf.placeholder(tf.float32, [None, 64]) # 8x8
ys = tf.placeholder(tf.float32, [None, 10])

#
# add output layer
#
l1 = add_layer(xs, 64, 50, 'l1', activation_function = tf.nn.tanh)
prediction = add_layer(l1, 50, 10, 'l2', activation_function = tf.nn.softmax)


#
# the error between prediction and real data
#
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
					      reduction_indices=[1])) #loss
tf.summary.scalar('loss', cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.6).minimize(cross_entropy)


sess = tf.Session()  
merged = tf.summary.merge_all()

#summary writer goes here
train_writer = tf.summary.FileWriter("logs/train", sess.graph)
test_writer = tf.summary.FileWriter("logs/test", sess.graph)

sess.run(tf.global_variables_initializer())

for i in range(500):
  #sess.run(train_step, feed_dict={xs:X_train, ys:y_train, keep_prob:1.0}) # overfitted
  sess.run(train_step, feed_dict={xs:X_train, ys:y_train, keep_prob:0.5}) # keep 0.5, drop 0.5
  if i% 50 == 0:
    #record loss
    train_result = sess.run(merged, feed_dict={xs:X_train, ys:y_train, keep_prob:1})
    test_result = sess.run(merged, feed_dict={xs:X_test, ys:y_test, keep_prob:1})
    train_writer.add_summary(train_result, i)
    test_writer.add_summary(test_result, i)