ffNN.py

from tensorflow.examples.tutorials.mnist import input_data
import sys
layers = int(sys.argv[1])

import tensorflow as tf
from time import time
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])
hiddenLayer = tf.layers.dense(x, 1000, activation = tf.nn.tanh)
lastLayer = hiddenLayer
for i in range(layers):
  lastLayer = tf.layers.dense(lastLayer, 1000, activation = tf.nn.tanh)
output = tf.layers.dense(lastLayer, 10)

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

cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels = y_, logits = output)

train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

sess = tf.Session()
sess.run(tf.global_variables_initializer())


then = time()
for _ in range(550):
  print(_, end = "\r")
  batch_xs, batch_ys = mnist.train.next_batch(100)
  sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
print(layers, time() - then)