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import tensorflow as tf


    

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import numpy as np
from sklearn import metrics


    

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


    

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def max_pool_2x2(tensor_in):
    return tf.nn.max_pool(tensor_in, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')


    

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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)
        
    with tf.variable_scope('conv_layer2'):
        h_conv2 = layers.convolution2d(h_pool1, 64, 
                                      kernel_size=[5,5],activation_fn = tf.nn.relu)
        h_pool2 = max_pool_2x2(h_conv2)
        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 *64])
    
    h_fc1 = layers.dropout(
        layers.fully_connected(
            h_pool2_flat, 1024, activation_fn=tf.nn.relu),
        keep_prob = 0.5,
        is_training=mode == tf.contrib.learn.ModeKeys.TRAIN)
    
    logits = layers.fully_connected(h_fc1, 10, activation_fn=None)
    loss = tf.contrib.losses.softmax_cross_entropy(logits,target)
    
    train_op = layers.optimize_loss(
        loss,
        tf.contrib.framework.get_global_step(),
        optimizer = 'SGD',
        learning_rate = 0.001)
    return tf.argmax(logits, 1), loss, train_op


    

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def main(unused_args):
    mnist = learn.datasets.load_dataset('mnist')
    
    feature_columns = learn.infer_real_valued_columns_from_input(mnist.train.images)
    classifier = learn.LinearClassifier(
        feature_columns=feature_columns, n_classes=10)
    
    classifier.fit(mnist.train.images, mnist.train.labels.astype(np.int32),
                   batch_size=100, steps=1000)
    score = metrics.accuracy_score(mnist.test.labels, 
                                  list(classifier.predict(mnist.test.images)))
    
    print('Accuracy: {0:f}'.format(score))
    
    
    classifier = learn.Estimator(model_fn = conv_model)
    classifier.fit(mnist.train.images, mnist.train.labels,
                  batch_size = 100, steps = 20000)
    score = metrics.accuracy_score(mnist.test.labels,
                                  list(classifier.predict(mnist.test.images)))
    
    print('Accuracy: {0:f}'.format(score))


    

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tf.app.run()


    

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