# -*- coding: utf-8 -*-
import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression
from sklearn.feature_extraction.text import CountVectorizer
import tflearn.datasets.mnist as mnist

def do_cnn_2d(X, Y, testX, testY ):
    # 构造卷积神经网络
    network = input_data(shape=[None, 28, 28, 1], name='input')
    network = conv_2d(network, 32, 3, activation='relu', regularizer="L2")
    network = max_pool_2d(network, 2)
    network = local_response_normalization(network)
    network = conv_2d(network, 64, 3, activation='relu', regularizer="L2")
    network = max_pool_2d(network, 2)
    network = local_response_normalization(network)
    network = fully_connected(network, 128, activation='tanh')
    network = dropout(network, 0.8)
    network = fully_connected(network, 256, activation='tanh')
    network = dropout(network, 0.8)
    network = fully_connected(network, 10, activation='softmax')
    network = regression(network, optimizer='adam', learning_rate=0.01,
                         loss='categorical_crossentropy', name='target')

    # 训练
    model = tflearn.DNN(network, tensorboard_verbose=0)
    model_path='cnn_2d.tfl'
    flag = 1
    if flag==0:
        model.fit({'input': X}, {'target': Y}, n_epoch=5,
                   validation_set=({'input': testX}, {'target': testY}),
                   snapshot_step=100, show_metric=True, run_id='mnist')
        model.save(model_path)
    elif flag==1:
        model.load(model_path)
        print model.evaluate(testX, testY)

if __name__ == '__main__':
    #2d
    X, Y, testX, testY = mnist.load_data(one_hot=True)
    X = X.reshape([-1, 28, 28, 1])
    testX = testX.reshape([-1, 28, 28, 1])
    do_cnn_2d(X, Y, testX, testY)
