

In [1]:

    
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

DATA_DIR = 'tmp/data' 
NUM_STEPS = 1000 
MINIBATCH_SIZE = 100 
LEARNING_RATE = 0.5

# Layers sizes 
L1 = 200
L2 = 100
L3 = 60
L4 = 30
L5 = 10

data = input_data.read_data_sets(DATA_DIR , one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])
l1 = tf.layers.dense(x,L1,activation=tf.nn.relu,use_bias=True)
l2 = tf.layers.dense(l1,L2,activation=tf.nn.relu,use_bias=True) 
l3 = tf.layers.dense(l2,L3,activation=tf.nn.relu,use_bias=True) 
l4 = tf.layers.dense(l3,L4,activation=tf.nn.relu,use_bias=True) 
y_pred = tf.layers.dense(l4,L5,activation=tf.nn.relu,use_bias=True)

y_true = tf.placeholder(tf.float32, [None, 10])

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=y_pred, labels=y_true))
gd_step = tf.train.GradientDescentOptimizer(LEARNING_RATE).minimize(cross_entropy) 
correct_mask = tf.equal(tf.argmax(y_pred , 1), tf.argmax(y_true , 1))
accuracy = tf.reduce_mean(tf.cast(correct_mask , tf.float32))

with tf.Session() as sess: 
    # Train
    sess.run(tf.global_variables_initializer()) 
    for i in range(NUM_STEPS):
        batch_x , batch_y = data.train.next_batch(MINIBATCH_SIZE)
        sess.run(gd_step, feed_dict={x:batch_x,y_true:batch_y}) 
    ans = sess.run(accuracy, feed_dict={x: data.test.images, y_true: data.test.labels})
    
print("Accuracy: {:.4}%".format(ans*100))









    



Extracting tmp/data/train-images-idx3-ubyte.gz
Extracting tmp/data/train-labels-idx1-ubyte.gz
Extracting tmp/data/t10k-images-idx3-ubyte.gz
Extracting tmp/data/t10k-labels-idx1-ubyte.gz
Accuracy: 96.44%