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

# 載入資料集
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)

# 每個批次的大小
batch_size = 100
# 計算一共有多少個批次
n_batch = mnist.train.num_examples // batch_size

# 定義兩個placeholder
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)

# 建立一個簡單的神經網路
W1 = tf.Variable(tf.truncated_normal([784, 1000], stddev=0.1))
b1 = tf.Variable(tf.zeros([1000]) + 0.1)
L1 = tf.nn.tanh(tf.matmul(x, W1) + b1)
L1_drop = tf.nn.dropout(L1, keep_prob)

W2 = tf.Variable(tf.truncated_normal([1000, 500], stddev=0.1))
b2 = tf.Variable(tf.zeros([500]) + 0.1)
L2 = tf.nn.tanh(tf.matmul(L1_drop, W2) + b2)
L2_drop = tf.nn.dropout(L2, keep_prob)

W3 = tf.Variable(tf.truncated_normal([500, 100], stddev=0.1))
b3 = tf.Variable(tf.zeros([100]) + 0.1)
L3 = tf.nn.tanh(tf.matmul(L2_drop, W3) + b3)
L3_drop = tf.nn.dropout(L3, keep_prob)

W4 = tf.Variable(tf.truncated_normal([100, 10], stddev=0.1))
b4 = tf.Variable(tf.zeros([10]) + 0.1)
prediction = tf.nn.softmax(tf.matmul(L3_drop, W4) + b4)

# 二次代價函式
# loss = tf.reduce_mean(tf.square(y-prediction))
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
# 使用梯度下降法
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

# 初始化變數
init = tf.global_variables_initializer()

# 結果存放在一個布林型列表中
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 1))  # argmax返回一維張量中最大的值所在的位置
# 求準確率
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
    sess.run(init)
    for epoch in range(11):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 0.7})

        test_acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels, keep_prob: 1.0})
        train_acc = sess.run(accuracy, feed_dict={x: mnist.train.images, y: mnist.train.labels, keep_prob: 1.0})
        print("Iter " + str(epoch) + ",Testing Accuracy " + str(test_acc) + ",Training Accuracy " + str(train_acc))

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