rnn_pixels.py

#coding=utf-8
'''
以RNN对mnist进行分类 ，将图片看成是一行行序列组成
A picture is representated as a sequence of pixels , coresponding to an image's width (timestep) and height (num of squences)
'''
import numpy as np
import tflearn

import tflearn.datasets.mnist as mnist

X , Y , testX , testY = mnist.load_data(one_hot = True)
X = np.reshape(X , (-1 , 28 , 28))
testX = np.reshape(testX , (-1 , 28 , 28))

net = tflearn.input_data(shape = [None , 28 , 28])
net = tflearn.lstm(net , 128 , return_seq = True) 
'''
Input

3-D Tensor [samples, timesteps, input dim].

Output

if return_seq: 3-D Tensor [samples, timesteps, output dim]. else: 2-D Tensor [samples, output dim].
'''
net = tflearn.lstm(net , 128) # output:[samples, output dim]. 
net = tflearn.fully_connected(net , 10 , activation = 'softmax')
net = tflearn.regression(net , optimizer = 'adam' , loss = 'categorical_crossentropy' , name = 'output1')

model = tflearn.DNN(net , tensorboard_verbose = 2)
model.fit(X , Y , n_epoch = 1 , validation_set = 0.1 , show_metric = True , snapshot_step = 100)