import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('data',one_hot = True)

def add_layer(input_data,in_size, out_size,activation_function=None):
  Weights = tf.Variable(tf.random_normal([in_size,out_size]))
  Biases = tf.Variable(tf.zeros([1, out_size])+0.1)
  Wx_plus_b = tf.add(tf.matmul(input_data, Weights), Biases)
  if activation_function==None:
    outputs = Wx_plus_b
  else:
    outputs = activation_function(Wx_plus_b)
  #return outputs#, Weights
  return {'outdata':outputs, 'w':Weights}

def get_accuracy(t_y):
#  global l1
#  accu = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(l1['outdata'],1),tf.argmax(t_y,1)), dtype = tf.float32))
  global prediction
  accu = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(prediction['outdata'],1),tf.argmax(t_y,1)), dtype = tf.float32))
  return accu

X = tf.placeholder(tf.float32, [None, 784])
Y = tf.placeholder(tf.float32, [None, 10])

#l1 = add_layer(X, 784, 10, tf.nn.softmax)
#cross_entropy = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(l1['outdata']), reduction_indices= [1]))
#l1 = add_layer(X, 784, 1024, tf.nn.relu)

l1 = add_layer(X, 784, 1024, None)
prediction = add_layer(l1['outdata'], 1024, 10, tf.nn.softmax)
cross_entropy = tf.reduce_mean(-tf.reduce_sum(Y*tf.log(prediction['outdata']), reduction_indices= [1]))

optimizer = tf.train.GradientDescentOptimizer(0.000001)
train = optimizer.minimize(cross_entropy)


newW = tf.Variable(tf.random_normal([1024,10]))
newOut = tf.matmul(l1['outdata'],newW)
newSoftMax = tf.nn.softmax(newOut)

init = tf.global_variables_initializer()
with tf.Session() as sess:
  sess.run(init)
  #print(sess.run(l1_Weights))
  for i in range(2):
    X_train, y_train = mnist.train.next_batch(1)
    X_train = X_train/255  #需要进行归一化处理
    #print(sess.run(l1['w'],feed_dict={X:X_train}))
    #print(sess.run(prediction['w'],feed_dict={X:X_train, Y:y_train}))
    #print(sess.run(l1['outdata'],feed_dict={X:X_train, Y:y_train}).shape)
    print(sess.run(prediction['outdata'],feed_dict={X:X_train, Y:y_train}))
    print(sess.run(newOut, feed_dict={X:X_train}))
    print(sess.run(newSoftMax, feed_dict={X:X_train}))
    print(y_train)
    #print(sess.run(l1['outdata'], feed_dict={X:X_train}))
    sess.run(train, feed_dict={X:X_train, Y:y_train})
    if i%100 == 0:
      #print(sess.run(cross_entropy, feed_dict={X:X_train, Y:y_train}))
      accuracy = get_accuracy(mnist.test.labels)
      print(sess.run(accuracy,feed_dict={X:mnist.test.images}))
    
    #if i%100==0:
    #print(sess.run(prediction, feed_dict={X:X_train}))
    #print(sess.run(cross_entropy, feed_dict={X:X_train,Y:y_train}))