In [1]:

    

from __future__ import print_function
import tensorflow as tf
tf.__version__


    

    
Out[1]:





'1.0.0'

In [ ]:

    

#Pseudocode to basic usage in batch mode

# Define a graph
graph = tf.Graph()
with graph.as_default():
    # graph definition
    saver = tf.train.Saver()


# Execute a graph to train a network
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333, allow_growth = True)

with tf.Session(graph=graph, config=tf.ConfigProto(gpu_options=gpu_options)) as session:
    print('Initializing')
    tf.initialize_all_variables().run()
    for epoch in range(nEpochs):
        for batch in batch_list:
            feedDict = {} # dictionary of batch data to run the graph
            _, param1_out, param2_out = session.run([train_step, param1_tensor, param2_tensor], feed_dict=feedDict)
    saver.save(session, 'path_to_model')    

    
    
# Load & execute the trained graph to score new data  
with tf.Session(graph=graph, config=tf.ConfigProto(gpu_options=gpu_options)) as session:
    #Load model
    loader = tf.train.import_meta_graph('path_to_model.meta')
    loader.restore(session, 'path_to_model')
    # Score data
    feedDict = {} # dictionary of batch data to score
    my_predictions = predicts.eval(feed_dict=feedDict)


    

In [3]:

    

# Load data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('/tmp/MNIST_data', one_hot=True)


# Define a graph
graph = tf.Graph()
with graph.as_default():
    # graph definition
    x = tf.placeholder(tf.float32, shape=[None, 784])
    y = tf.placeholder(tf.float32, shape=[None, 10])
    W = tf.Variable(tf.zeros([784,10]))
    b = tf.Variable(tf.zeros([10]))
    y_pred = tf.nn.softmax(tf.matmul(x,W) + b)
    cross_entropy = tf.reduce_mean(-tf.reduce_sum(y * tf.log(y_pred), reduction_indices=[1]))
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
    saver = tf.train.Saver()


# Execute a graph to train a network
nEpochs = 10
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333, allow_growth = True)

with tf.Session(graph=graph, config=tf.ConfigProto(gpu_options=gpu_options)) as session:
    print('Initializing')
    tf.global_variables_initializer().run()
    for epoch in range(nEpochs):
        for i in range(10):
            batch = mnist.train.next_batch(500)
            _, ce = session.run([train_step, cross_entropy], feed_dict={x: batch[0], y: batch[1]})
        print(epoch, ce)
    saver.save(session, '/tmp/mnist_model')    

    
    
# Load & execute the trained graph to score new data  
with tf.Session(graph=graph, config=tf.ConfigProto(gpu_options=gpu_options)) as session:
    #Load model
    loader = tf.train.import_meta_graph('/tmp/mnist_model.meta')
    loader.restore(session, '/tmp/mnist_model')
    
    # Score test data
    feedDict = {x: mnist.test.images[:200], y: mnist.test.labels[:200]} # dictionary of batch data to score
    my_predictions = y_pred.eval(feed_dict=feedDict)
    
    # Evaluate test data
    print('Test ce: ', cross_entropy.eval(feed_dict=feedDict))


    

    




Extracting /tmp/MNIST_data/train-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/train-labels-idx1-ubyte.gz
Extracting /tmp/MNIST_data/t10k-images-idx3-ubyte.gz
Extracting /tmp/MNIST_data/t10k-labels-idx1-ubyte.gz
Initializing
0 0.758188
1 0.79953
2 0.472074
3 0.553901
4 0.571241
5 0.517454
6 0.496778
7 0.386795
8 0.549113
9 0.422646
Test ce:  0.323629

In [ ]: