#!/usr/bin/env python
# coding: utf-8

# In[1]:


import warnings
warnings.simplefilter(action='ignore')


# In[2]:


import os
from time import time

import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)  # 过滤掉 Tensorflow 的 Warning 信息

import tensorflow.examples.tutorials.mnist.input_data as input_data
import numpy as np
import matplotlib.pyplot as plt


# ### 1. 数据预处理

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


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print('train images shape:', mnist.train.images.shape)
print('train labels shape:', mnist.train.labels.shape)
print()
print('validation images shape:', mnist.validation.images.shape)
print('validation labels shape:', mnist.validation.labels.shape)
print()
print('test images shape:', mnist.test.images.shape)
print('test labels shape:', mnist.test.labels.shape)


# ### 2. 建立模型

# #### 2.1 定义 layer 函数

# In[5]:


def layer(output_dim, input_dim, inputs, activation=None):
    W = tf.Variable(tf.random_normal([input_dim, output_dim]))
    b = tf.Variable(tf.random_normal([1, output_dim]))
    result = tf.matmul(inputs, W) + b
    
    if activation is None:
        outputs = result
    else:
        outputs = activation(result)
    
    return outputs


# #### 2.2 建立输入层

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x = tf.placeholder('float', [None, 784])


# #### 2.3 建立隐藏层

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h1 = layer(output_dim=1000, input_dim=784, inputs=x, activation=tf.nn.relu)  # 隐藏层的神经元增大为1000


# #### 2.4 建立输出层

# In[8]:


y_predict = layer(output_dim=10, input_dim=1000, inputs=h1, activation=None)


# ### 3. 定义训练方式

# #### 3.1 建立训练数据 label 真实值的 placeholder

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y_label = tf.placeholder('float', [None, 10])


# #### 3.2 定义损失函数

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loss_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_predict, labels=y_label))


# #### 3.3 定义优化器

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optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss_function)


# ### 4. 定义评估模型准确率的方式

# #### 4.1 计算每一项数据是否预测正确

# In[12]:


correct_prediction = tf.equal(tf.argmax(y_label, axis=1), tf.argmax(y_predict, axis=1))


# #### 4.2 计算预测正确结果的平均值

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accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))


# ### 5. 开始训练

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train_epochs = 15
batch_size = 100
total_batch = int(mnist.train.num_examples / batch_size)
epoch_list = []
loss_list = []
acc_list = []


# In[15]:


start_time = time()
sess = tf.Session()
sess.run(tf.global_variables_initializer())

for epoch in range(train_epochs):
    for i in range(total_batch):
        x_batch, y_batch = mnist.train.next_batch(batch_size=batch_size)
        sess.run(optimizer, feed_dict={x: x_batch, y_label: y_batch})
    
    loss, acc = sess.run([loss_function, accuracy], 
                         feed_dict={x: mnist.validation.images, y_label: mnist.validation.labels})
    epoch_list.append(epoch+1)
    loss_list.append(loss)
    acc_list.append(acc)
    print('train epoch:', '%02d' % (epoch + 1), 'loss:', '{:.9f}'.format(loss), 'acc:', acc)

print()
print('train finished. takes', time() - start_time, 'seconds')


# ### 6. 以图形显示训练过程

# In[16]:


def show_train_history(x_values, y_values, title):
    plt.plot(x_values, y_values, label=title)
    plt.xlabel('Epoch')
    plt.ylabel(title)
    plt.legend([title], loc='upper left')
    plt.show()


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show_train_history(epoch_list, acc_list, 'acc')


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show_train_history(epoch_list, loss_list, 'loss')


# ### 7. 评估模型准确率

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print('accuracy:', sess.run(accuracy, feed_dict={x: mnist.test.images, y_label: mnist.test.labels}))


# ### 8. 进行预测

# #### 8.1 执行预测

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prediction_result = sess.run(tf.argmax(y_predict, axis=1), feed_dict={x: mnist.test.images})


# #### 8.2 预测结果

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prediction_result[:10]


# #### 8.3 定义函数以显示10项预测结

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def plot_images_labels_prediction(images, labels, predictions, idx, num=10):
    """
    images: 数字图像数组
    labels: 真实值数组
    predictions: 预测结果数据
    idx: 开始显示的数据index
    num: 要显示的数据项数, 默认为10, 不超过25
    """
    fig = plt.gcf()
    fig.set_size_inches(12, 14)
    if num > 25:
        num = 25
    for i in range(0, num):
        ax = plt.subplot(5, 5, i+1)
        ax.imshow(images[idx].reshape(28, 28), cmap='binary')
        title = 'lable=' + str(np.argmax(labels[idx]))
        if len(predictions) > 0:
            title += ',predict=' + str(predictions[idx])
        ax.set_title(title, fontsize=10)
        ax.set_xticks([])
        ax.set_yticks([])
        idx += 1
    plt.show()


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plot_images_labels_prediction(mnist.test.images, mnist.test.labels, prediction_result, 0, 10)


# ### 9. 找出预测错误

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for i in range(300):
    label = np.argmax(mnist.test.labels[i])
    predict = prediction_result[i]
    if predict != label:
        print('i=' + str(i), 'label=' + str(label), 'predict=' + str(predict))


# In[25]:


sess.close()