import tensorflow as tf
from sklearn.datasets import load_digits
from sklearn.model_selection 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)

def add_layer(inputs, in_size, out_size, layer_name, activation_function = None):
    # add one more layer and return he output of this layer
            Weights = tf.Variable(tf.random_normal([in_size, out_size]))
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
            Wx_plus_b = tf.matmul(inputs, Weights) + biases     #矩阵相乘
            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 loss between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),reduction_indices=[1])) #配合上句食用的softmax分类算法效果好，原理难目前可以不理解
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 in here

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

sess.run(tf.initialize_all_variables())

for i in range(500):
    sess.run(train_step,feed_dict={xs:X_train,ys:y_train, keep_prob: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)