"""Tutorial on how to build a convnet w/ modern changes, e.g.

Batch Normalization, Leaky rectifiers, and strided convolution.

Parag K. Mital, Jan 2016.

"""

# %%

import tensorflow as tf

from batch_norm import batch_norm

from activations import lrelu

from connections import conv2d, linear

from datasets import MNIST

# %% Setup input to the network and true output label. These are

# simply placeholders which we'll fill in later.

mnist = MNIST()

x = tf.placeholder(tf.float32, [None, 784])

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

x_tensor = tf.reshape(x, [-1, 28, 28, 1])

# %% Define the network:

bn1 = batch_norm(-1, name='bn1')

bn2 = batch_norm(-1, name='bn2')

bn3 = batch_norm(-1, name='bn3')

h_1 = lrelu(bn1(conv2d(x_tensor, 32, name='conv1')), name='lrelu1')

h_2 = lrelu(bn2(conv2d(h_1, 64, name='conv2')), name='lrelu2')

h_3 = lrelu(bn3(conv2d(h_2, 64, name='conv3')), name='lrelu3')

h_3_flat = tf.reshape(h_3, [-1, 64 * 4 * 4])

h_4 = linear(h_3_flat, 10)

y_pred = tf.nn.softmax(h_4)

# %% Define loss/eval/training functions

cross_entropy = -tf.reduce_sum(y * tf.log(y_pred))

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

correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))

# %% We now create a new session to actually perform the initialization the

# variables:

sess = tf.Session()

sess.run(tf.initialize_all_variables())

# %% We'll train in minibatches and report accuracy:

n_epochs = 10

batch_size = 100

for epoch_i in range(n_epochs):

for batch_i in range(mnist.train.num_examples // batch_size):

batch_xs, batch_ys = mnist.train.next_batch(batch_size)

sess.run(train_step, feed_dict={

x: batch_xs, y: batch_ys})

print(sess.run(accuracy,

feed_dict={

x: mnist.validation.images,

y: mnist.validation.labels

}))