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)
trainimg = mnist.train.images
trainlabel = mnist.train.labels
testimg = mnist.test.images
testlabel = mnist.test.labels
print("MNIST loaded")
print(trainimg.shape)
print(trainlabel.shape)
print(testimg.shape)
print(testlabel.shape)
#print (trainimg)
print(trainlabel[0])

x = tf.placeholder("float", [None, 784])
y = tf.placeholder("float", [None, 10])  # None is for infinite
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
# LOGISTIC REGRESSION MODEL邏輯迴歸模型
actv = tf.nn.softmax(tf.matmul(x, W) + b)
# COST FUNCTION損失函式
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(actv), reduction_indices=1))
# OPTIMIZER優化器
learning_rate = 0.01
optm = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

# PREDICTION預測
pred = tf.equal(tf.argmax(actv, 1), tf.argmax(y, 1))
# ACCURACY精度
accr = tf.reduce_mean(tf.cast(pred, "float"))
# INITIALIZER初始化
init = tf.global_variables_initializer()
sess = tf.InteractiveSession()
#定義超引數
training_epochs = 50
batch_size = 100
display_step = 5
# SESSION
sess = tf.Session()
sess.run(init)
# MINI-BATCH LEARNING小批量梯度下降學習
for epoch in range(training_epochs):
    avg_cost = 0.
    num_batch = int(mnist.train.num_examples/batch_size)
    for i in range(num_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        sess.run(optm, feed_dict={x: batch_xs, y: batch_ys})
        feeds = {x: batch_xs, y: batch_ys}
        avg_cost += sess.run(cost, feed_dict=feeds) / num_batch
        # DISPLAY
    if epoch % display_step == 0:
        feeds_train = {x: batch_xs, y: batch_ys}
        feeds_test = {x: mnist.test.images, y: mnist.test.labels}
        train_acc = sess.run(accr, feed_dict=feeds_train)
        test_acc = sess.run(accr, feed_dict=feeds_test)
        print("Epoch: %03d/%03d cost: %.9f train_acc: %.3f test_acc: %.3f"
              % (epoch, training_epochs, avg_cost, train_acc, test_acc))
    print("DONE")