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"""Simple tutorial following the TensorFlow example of a Convolutional Network.

Parag K. Mital, Jan. 2016"""

# %% Imports

import tensorflow as tf

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

from libs.utils import *

import matplotlib.pyplot as plt

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

# simply placeholders which we'll fill in later.

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

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

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

# %% Since x is currently [batch, height*width], we need to reshape to a

# 4-D tensor to use it in a convolutional graph. If one component of

# `shape` is the special value -1, the size of that dimension is

# computed so that the total size remains constant. Since we haven't

# defined the batch dimension's shape yet, we use -1 to denote this

# dimension should not change size.

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

# %% We'll setup the first convolutional layer

# Weight matrix is [height x width x input_channels x output_channels]

filter_size = 5

n_filters_1 = 16

W_conv1 = weight_variable([filter_size, filter_size, 1, n_filters_1])

# %% Bias is [output_channels]

b_conv1 = bias_variable([n_filters_1])

# %% Now we can build a graph which does the first layer of convolution:

# we define our stride as batch x height x width x channels

# instead of pooling, we use strides of 2 and more layers

# with smaller filters.

h_conv1 = tf.nn.relu(

tf.nn.conv2d(input=x_tensor,

filter=W_conv1,

strides=[1, 2, 2, 1],

padding='SAME') +

b_conv1)

# %% And just like the first layer, add additional layers to create

# a deep net

n_filters_2 = 16

W_conv2 = weight_variable([filter_size, filter_size, n_filters_1, n_filters_2])

b_conv2 = bias_variable([n_filters_2])

h_conv2 = tf.nn.relu(

tf.nn.conv2d(input=h_conv1,

filter=W_conv2,

strides=[1, 2, 2, 1],

padding='SAME') +

b_conv2)

# %% We'll now reshape so we can connect to a fully-connected layer:

h_conv2_flat = tf.reshape(h_conv2, [-1, 7 * 7 * n_filters_2])

# %% Create a fully-connected layer:

n_fc = 1024

W_fc1 = weight_variable([7 * 7 * n_filters_2, n_fc])

b_fc1 = bias_variable([n_fc])

h_fc1 = tf.nn.relu(tf.matmul(h_conv2_flat, W_fc1) + b_fc1)

# %% We can add dropout for regularizing and to reduce overfitting like so:

keep_prob = tf.placeholder(tf.float32)

h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# %% And finally our softmax layer:

W_fc2 = weight_variable([n_fc, 10])

b_fc2 = bias_variable([10])

y_pred = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

# %% Define loss/eval/training functions

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

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

# %% Monitor accuracy

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:

batch_size = 100

n_epochs = 5

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(optimizer, feed_dict={

x: batch_xs, y: batch_ys, keep_prob: 0.5})

print(sess.run(accuracy,

feed_dict={

x: mnist.validation.images,

y: mnist.validation.labels,

keep_prob: 1.0

}))

# %% Let's take a look at the kernels we've learned

W = sess.run(W_conv1)

plt.imshow(montage(W / np.max(W)), cmap='coolwarm')