A simple MNIST classifer AND autoencoder in one
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"""A simple MNIST classifer AND autoencoder in one | |
""" | |
# Import data | |
import input_data | |
mnist = input_data.read_data_sets("/data/mnist/", one_hot=True) | |
import tensorflow as tf | |
sess = tf.InteractiveSession() | |
# Create the model | |
x = tf.placeholder("float", [None, 784]) | |
# l_rate = tf.placeholder("float", [1])# 0.01 | |
l_rate = tf.Variable(0.001) | |
W1 = tf.Variable(tf.zeros([784,400])) | |
b1 = tf.Variable(tf.zeros([400])) | |
W2 = tf.Variable(tf.ones([400,20])) | |
b2 = tf.Variable(tf.zeros([20])) | |
# b2t = tf.Variable(tf.zeros([20])) | |
h= tf.nn.tanh( tf.matmul(x,W1)+b1) # | |
h=tf.nn.dropout(h) # THAT! | |
_y = tf.matmul(h,W2) + b2 # 10 Numbers + 10 'styles' | |
print("_y ",tf.rank(_y)) | |
y = tf.nn.softmax(tf.slice(_y,[0,0],[-1,10])) # softmax of all batches(-1) only on the numbers(10) | |
e1= tf.matmul(_y,tf.transpose(W2)) #+b2 | |
e1=tf.nn.tanh(e1) | |
x_=x_reconstructed= tf.nn.sigmoid(tf.matmul(e1,tf.transpose(W1))) | |
# Define loss and optimizer | |
y_ = tf.placeholder("float", [None,10]) | |
mnist_entropy = -tf.reduce_sum(y_*tf.log(y)) | |
# encod_entropy = -tf.reduce_sum(x_*tf.log(x)) | |
encod_entropy = tf.sqrt(tf.reduce_mean(tf.square(x - x_))) | |
cross_entropy = encod_entropy * mnist_entropy | |
# cross_entropy = -tf.reduce_sum(y_*tf.log(y)) | |
train_step = tf.train.AdamOptimizer(learning_rate=l_rate).minimize(cross_entropy) | |
pretrainer = tf.train.AdamOptimizer(learning_rate=l_rate).minimize(mnist_entropy) | |
# encod_step = tf.train.AdamOptimizer(learning_rate=l_rate).minimize(encod_entropy) | |
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y,1), tf.argmax(y_,1)), "float")) | |
import sys | |
def eval(feed): | |
print("it %d"%i, end=' ') | |
print("cross_entropy ",cross_entropy.eval(feed), end=' ') | |
# print("encod_entropy ",encod_entropy.eval(feed), end=' ') | |
print("overal accuracy ",accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}))#, end='\r'WWWWAAA) | |
# Train | |
tf.initialize_all_variables().run() | |
for i in range(1000):#pretrain | |
batch_xs, batch_ys = mnist.train.next_batch(100) | |
feed = {x: batch_xs, y_: batch_ys} | |
pretrainer.run(feed) | |
if(i%100==0):eval(feed) | |
for i in range(100000): | |
batch_xs, batch_ys = mnist.train.next_batch(100) | |
feed = {x: batch_xs, y_: batch_ys} | |
# if((i+1)%9000==0):sess.run(tf.assign(l_rate,l_rate*0.3)) | |
train_step.run(feed) | |
if(i%100==0):eval(feed) | |
# encod_step.run(feed) # alternating! |
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