VariationalAutoencoderRunner.py 1.67 KB
Newer Older
Alan Yee's avatar
Alan Yee committed
1
2
3
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
Jiří Vahala's avatar
Jiří Vahala committed
4

Alan Yee's avatar
Alan Yee committed
5
import numpy as np
Jiří Vahala's avatar
Jiří Vahala committed
6
7
8
9
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

10
from autoencoder_models.VariationalAutoencoder import VariationalAutoencoder
Jiří Vahala's avatar
Jiří Vahala committed
11
12
13
14

mnist = input_data.read_data_sets('MNIST_data', one_hot = True)


15
16
def min_max_scale(X_train, X_test):
    preprocessor = prep.MinMaxScaler().fit(X_train)
Jiří Vahala's avatar
Jiří Vahala committed
17
18
19
20
21
22
23
24
25
26
    X_train = preprocessor.transform(X_train)
    X_test = preprocessor.transform(X_test)
    return X_train, X_test


def get_random_block_from_data(data, batch_size):
    start_index = np.random.randint(0, len(data) - batch_size)
    return data[start_index:(start_index + batch_size)]


27
X_train, X_test = min_max_scale(mnist.train.images, mnist.test.images)
Jiří Vahala's avatar
Jiří Vahala committed
28
29
30
31
32
33

n_samples = int(mnist.train.num_examples)
training_epochs = 20
batch_size = 128
display_step = 1

Alan Yee's avatar
Alan Yee committed
34
35
36
37
autoencoder = VariationalAutoencoder(
    n_input = 784,
    n_hidden = 200,
    optimizer = tf.train.AdamOptimizer(learning_rate = 0.001))
Jiří Vahala's avatar
Jiří Vahala committed
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52

for epoch in range(training_epochs):
    avg_cost = 0.
    total_batch = int(n_samples / batch_size)
    # Loop over all batches
    for i in range(total_batch):
        batch_xs = get_random_block_from_data(X_train, batch_size)

        # Fit training using batch data
        cost = autoencoder.partial_fit(batch_xs)
        # Compute average loss
        avg_cost += cost / n_samples * batch_size

    # Display logs per epoch step
    if epoch % display_step == 0:
53
54
        print("Epoch: ", '%d,' % (epoch + 1),
              "Cost: ", "{:.9f}".format(avg_cost))
55
print("Total cost: " + str(autoencoder.calc_total_cost(X_test)))