In [11]:

    

import tensorflow as tf


    

In [12]:

    

import numpy as np
from sklearn import metrics


    

In [13]:

    

layers = tf.contrib.layers
learn = tf.contrib.learn


    

In [14]:

    

def max_poll_2x2(tensor_in):
    return tf.nn.max_pool(tensor_in, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')


    

In [15]:

    

def conv_model(feature, target, mode):
    target = tf.one_hot(tf.cast(target, tf.int32),10, 1, 0)
    feature = tf.reshape(feature, [-1, 28, 28, 1])
    
    with tf.variable_scope('conv_layer1'):
        h_conv1 = layers.convolution2d(feature, 32, 
                                      kernel_size=[5,5], activation_fn = tf.nn.relu)
        h_pool1 = max_pool_2x2(h_conv1)