TensorFlowGettingStarted/MNIST_MultiLayer_ANN.py at master · timwroge/TensorFlowGettingStarted · GitHub

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import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import datetime as dt
import math

K=320
L=110
M=45


#this is a dataset of numbers between zero and nine that is squashed down into a vector of
#size 1x784
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
#this should create a symbolic vector input for the data set
x = tf.placeholder(tf.float32, [None, 784], name="X")
#None means the dimension can be any length
#W and b are tensors full of zeros
#adding layers

with tf.name_scope("Weights"):
    W1= tf.Variable(tf.truncated_normal([784, K], stddev=norm_dist_elu(784,K)), name="W1")
    tf.summary.histogram("Weights_1", W1)

    W2= tf.Variable(tf.truncated_normal([K, L],stddev=norm_dist_elu(K, L)), name = "W2")
    tf.summary.histogram("Weights_2", W2)

    W3= tf.Variable(tf.truncated_normal([L, M],stddev=norm_dist_elu(L,M)),name = "W3")
    tf.summary.histogram("Weights_3", W3)

##    W4= tf.Variable(tf.truncated_normal([M, N],stddev=norm_dist_elu(M,N)),name = "W4")
##    tf.summary.histogram("Weights_4", W4)

    W_out= tf.Variable(tf.truncated_normal([M, 10],stddev=norm_dist_elu(M, 10)),name = "W_out")
    tf.summary.histogram("Weights_Out", W_out)

with tf.name_scope("Biases"):
    b1 = tf.Variable(tf.zeros([K]), name= "b1")
    tf.summary.histogram("Biases_1", b1)

    b2 = tf.Variable(tf.zeros([L]),name = "b2")
    tf.summary.histogram("Biases_2", b2)

    b3 = tf.Variable(tf.zeros([M]),name = "b3")
    tf.summary.histogram("Biases_3", b1)

##    b4 = tf.Variable(tf.zeros([N]),name = "b4")
##    tf.summary.histogram("Biases_4", b4)

    b_out= tf.Variable(tf.zeros([10]),name = "b_out")
    tf.summary.histogram("Biases_Out", b_out)


with tf.name_scope("MultiLayer_NN"):
 #implementing multilayer nn
 y1 = tf.nn.elu(tf.matmul(x, W1) + b1 , name = "y1")
 y2 = tf.nn.elu(tf.matmul(y1, W2) + b2,name =  "y2")
 y3 = tf.nn.elu(tf.matmul(y2, W3) + b3,name = "y3")
## y4 = tf.nn.elu(tf.matmul(y3, W4) + b4,name = "y4")
 Output= tf.nn.softmax(tf.matmul(y3, W_out) + b_out, name = "Output")

#begin defining the cost

Output_labels = tf.placeholder(tf.float32, [None, 10], name="Output_labels")
with tf.name_scope("Cost"):
  cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=Output_labels, logits=Output), name= "Cross_Entropy")

training_rate=0.0005
train_step = tf.train.AdamOptimizer(training_rate).minimize(cross_entropy)


with tf.Session() as sess:
    init= tf.global_variables_initializer()
    sess.run(init)
    epochs = 10000

    now = dt.datetime.utcnow().strftime("%B.%d.%y@%H.%M.%S.%f")
    filewrite_out=tf.summary.FileWriter("/tmp/MNIST_MultiLayer_ANN/{}".format(now))
    filewrite_out.add_graph(sess.graph)

    tf.summary.scalar("Cost", cross_entropy)
    tf.summary.histogram("Cost", cross_entropy)

    correct_prediction = tf.equal(tf.argmax(Output, 1), tf.argmax(Output_labels, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    tf.summary.scalar("Accuracy", accuracy)
    tf.summary.histogram("Accuracy", accuracy)

    merged_summaries=tf.summary.merge_all()

    with tf.name_scope("Training"):
        for i in range(epochs):
        #by using small batches of a 100 data points as below, this utilizes stochastic gradient descent
          batch_xs, batch_ys = mnist.train.next_batch(100)
          sess.run(train_step, feed_dict={x: batch_xs, Output_labels: batch_ys})


          if i%5 ==0:
              sum_op=sess.run(merged_summaries, feed_dict={x: mnist.test.images, Output_labels:mnist.test.labels })
              filewrite_out.add_summary(sum_op, i)

          if i%10 ==0:
            correct_prediction = tf.equal(tf.argmax(Output, 1), tf.argmax(Output_labels, 1))
            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
            print ("The accuracy for run ", i, " in ", epochs, " is ", sess.run(accuracy, feed_dict={x: mnist.test.images,
                                            Output_labels: mnist.test.labels}))


        # Test trained model on dataset
        correct_prediction = tf.equal(tf.argmax(Output, 1), tf.argmax(Output_labels, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        print('Accuracy of trained model: ',sess.run(accuracy, feed_dict={x: mnist.test.images,
                                            Output_labels: mnist.test.labels}))