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)