import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import input_data

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

n_hidden_1 = 256
n_hidden_2 = 128
n_input = 784
n_classes = 10

#参数初始化
x = tf.placeholder('float', [None, n_input])
y = tf.placeholder('float', [None, n_classes])

stddev = 0.1
weights = {
    'w1' : tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=stddev)),
    'w2' : tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=stddev)),
    'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes], stddev=stddev))
}
biases = {
    'b1' : tf.Variable(tf.random_normal([n_hidden_1])),
    'b2' : tf.Variable(tf.random_normal([n_hidden_2])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

print("NETWORK READY")

#前向传播 返回得分值
def multilayer_perceptron(_X, _weights, _biases):
    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(_X, _weights['w1']), _biases['b1']))
    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, _weights['w2']), _biases['b2']))
    return (tf.matmul(layer_2, _weights['out']) + _biases['out'])

pred = multilayer_perceptron(x, weights, biases)

#交叉熵损失函数
#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=pred, labels=y))
#梯度下降优化器
optm = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(cost)
#准确率
corr = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accr = tf.reduce_mean(tf.cast(corr, "float"))

#初始化全局变量
init = tf.global_variables_initializer()
print("FUNCTION READY")

training_epochs = 20
batch_size = 100
display_step = 4

sess = tf.Session()
sess.run(init)

for epoch in range(training_epochs):
    avg_cost = 0
    total_batch = int(mnist.train.num_examples / batch_size)

    for i in range(total_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        feeds = {x : batch_xs, y : batch_ys}
        sess.run(optm, feed_dict=feeds)
        avg_cost += sess.run(cost, feed_dict=feeds)
    avg_cost = avg_cost / total_batch

    #DISPLAY
    if (epoch + 1) % display_step == 0:
        print("Epoch: %03d/%03d cost: %.9f" % (epoch, training_epochs, avg_cost))
        feeds = {x : batch_xs, y : batch_ys}
        train_acc = sess.run(accr, feed_dict=feeds)
        print("TRAIN ACCURACY : %.3f" % train_acc)
        feeds = {x : mnist.test.images, y : mnist.test.labels}
        test_acc = sess.run(accr, feed_dict=feeds)
        print("TEST ACCURACY : %.3f" % test_acc)
print("OPTIMIZATION FINISHED")
