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# # 4-1 cross-entropy

# +
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

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

# +
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

batch_size = 100
n_batch = mnist.train.num_examples // batch_size

x = tf.placeholder(tf.float32, [None,784])
y = tf.placeholder(tf.float32, [None, 10])

W = tf.Variable(tf.truncated_normal([784,10],stddev=0.1))
b = tf.Variable(tf.zeros([10]) + 0.1)
#W = tf.Variable(tf.zeros([784, 10]))
#b = tf.Variable(tf.zeros([10]))
prediction = tf.nn.softmax(tf.matmul(x, W) + b)

#loss = tf.reduce_mean(tf.square(y-prediction))
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

correct_prediction = tf.equal(tf.arg_max(y,1), tf.arg_max(prediction,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(21):
        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})
        acc =sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels})
        print('Iter' + str(epoch) + ": Testing Accuracy " + str(acc))

# -

# # 4-2 drop-out

# +
mnist = input_data.read_data_sets('MNIST_data',one_hot=True)

batch_size = 80
n_batch = mnist.train.num_examples // batch_size

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

W1 = tf.Variable(tf.truncated_normal([784, 2000], stddev=0.1))
b1 = tf.Variable(tf.zeros([2000])+ 0.1)
L1 = tf.nn.tanh(tf.matmul(x,W1) + b1)
L1_drop = tf.nn.dropout(L1, keep_prob=keep_prob)

W2 = tf.Variable(tf.truncated_normal([2000, 2000], stddev=0.1))
b2 = tf.Variable(tf.zeros([2000])+ 0.1)
L2 = tf.nn.tanh(tf.matmul(L1_drop,W2) + b2)
L2_drop = tf.nn.dropout(L2, keep_prob=keep_prob)

W3 = tf.Variable(tf.truncated_normal([2000, 1000], stddev=0.1))
b3 = tf.Variable(tf.zeros([1000])+ 0.1)
L3 = tf.nn.tanh(tf.matmul(L2_drop,W3) + b3)
L3_drop = tf.nn.dropout(L3, keep_prob=keep_prob)

W4 = tf.Variable(tf.truncated_normal([1000, 10], stddev=0.1))
b4 = tf.Variable(tf.zeros([10])+ 0.1)
prediction = tf.nn.softmax(tf.matmul(L3_drop, W4) + b4)

#loss = tf.reduce_mean(tf.square(y-prediction))
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

correct_prediction = tf.equal(tf.arg_max(y,1), tf.arg_max(prediction,1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(21):
        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:1.0})
        acc =sess.run(accuracy, feed_dict={x:mnist.test.images, y:mnist.test.labels, keep_prob:1.0})
        print('Iter' + str(epoch) + ": Testing Accuracy " + str(acc))
# -

# # 4-3 optimizer

# +
# tf.train.AdamOptimizer
# tf.train.MomentumOptimizer
# tf.train.AdadeltaOptimizer
# tf.train.RMSPropOptimizer

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