In [1]:
import tensorflow as tf
In [2]:
from tensorflow.examples.tutorials.mnist import input_data
In [3]:
mnist = input_data.read_data_sets("./data/MNIST_data/", one_hot = True)
Function to help intialize random weights for fully connected or convolutional layers, we leave the shape attribute as a parameter for this.
In [4]:
def init_weights(shape):
init_random_dist = tf.truncated_normal(shape = shape, stddev = 0.1)
return tf.Variable(init_random_dist)
Same as init_weights, but for the biases
In [5]:
def init_bias(shape):
init_bias_vals = tf.constant(shape =shape, value = 0.1)
return tf.Variable(init_bias_vals)
Create a 2D convolution using builtin conv2d from TF. From those docs:
Computes a 2-D convolution given 4-D input and filter tensors.
Given an input tensor of shape [batch, in_height, in_width, in_channels]
and a filter / kernel tensor of shape
[filter_height, filter_width, in_channels, out_channels], this op
performs the following:
[filter_height * filter_width * in_channels, output_channels].[batch, out_height, out_width,
filter_height * filter_width * in_channels].
In [6]:
def conv2d(x, W):
return tf.nn.conv2d(x, W,
strides = [1, 1, 1, 1],
padding = 'SAME')
Create a max pooling layer, again using built in TF functions:
Performs the max pooling on the input.
Args:
value: A 4-D `Tensor` with shape `[batch, height, width, channels]` and
type `tf.float32`.
ksize: A list of ints that has length >= 4. The size of the window for
each dimension of the input tensor.
strides: A list of ints that has length >= 4. The stride of the sliding
window for each dimension of the input tensor.
padding: A string, either `'VALID'` or `'SAME'`.
In [7]:
def max_pool_2by2(x):
return tf.nn.max_pool(x,
ksize = [1, 2, 2, 1],
strides = [1, 2, 2, 1],
padding = 'SAME')
Using the conv2d function, we'll return an actual convolutional layer here that uses an ReLu activation.
In [8]:
def convolutional_layer(input_x, shape):
W = init_weights(shape)
b = init_bias([shape[3]])
return tf.nn.relu(conv2d(input_x, W) + b)
This is a normal fully connected layer
In [9]:
def normal_full_layer(input_layer, size):
input_size = int(input_layer.get_shape()[1])
W = init_weights([input_size, size])
b = init_bias([size])
return tf.matmul(input_layer, W) + b
In [10]:
x = tf.placeholder(tf.float32, shape = [None,784])
In [11]:
y_true = tf.placeholder(tf.float32, shape = [None,10])
In [12]:
x_image = tf.reshape(x, [-1, 28, 28, 1])
In [13]:
# Using a 2 by 2 filter
# You can change the 32 output, that essentially represents the amount of filters used
# You need to pass in 32 to the next input though, the 1 comes from the original input of
# a single image.
convo_1 = convolutional_layer(x_image,
shape = [2, 2 , 1, 32])
convo_1_pooling = max_pool_2by2(convo_1)
In [14]:
# Using a 2 by 2 filter
# You can actually change the 64 output if you want, you can think of that as a representation
# of the amount of 6by6 filters used.
convo_2 = convolutional_layer(convo_1_pooling,
shape = [3, 3, 32, 64])
convo_2_pooling = max_pool_2by2(convo_2)
In [15]:
# Why 7 by 7 image? Because we did 2 pooling layers, so (28/2)/2 = 7
# 64 then just comes from the output of the previous Convolution
convo_2_flat = tf.reshape(convo_2_pooling, [-1, 7 * 7 * 64])
full_layer_one = tf.nn.relu(normal_full_layer(convo_2_flat, 1024))
In [16]:
# Dropout placeholder
hold_prob = tf.placeholder(tf.float32)
full_one_dropout = tf.nn.dropout(full_layer_one,
keep_prob = hold_prob)
In [17]:
y_pred = normal_full_layer(full_one_dropout, 10)
In [18]:
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels = y_true,
logits = y_pred))
In [19]:
# Training with the Adam Optimizer
optimizer = tf.train.AdamOptimizer(learning_rate = 0.0001)
train = optimizer.minimize(cross_entropy)
In [20]:
init = tf.global_variables_initializer()
In [21]:
steps = 5000
with tf.Session() as sess:
sess.run(init)
for i in range(steps):
# Training on batches of 50
batch_x , batch_y = mnist.train.next_batch(50)
# Training with dropout = 0.5
sess.run(train,
feed_dict = {x : batch_x,
y_true : batch_y,
hold_prob : 0.5})
# PRINT OUT A MESSAGE EVERY 100 STEPS
if i%100 == 0:
print('Currently on step {}'.format(i))
print('Accuracy is:')
# Matches
matches = tf.equal(tf.argmax(y_pred, 1),
tf.argmax(y_true, 1))
# tf.cast is to given datatype
acc = tf.reduce_mean(tf.cast(matches, tf.float32))
# Calculate the accuracy
# Hold probability = 1 is same as dropout = 0.
print(sess.run(acc, feed_dict = {x : mnist.test.images,
y_true : mnist.test.labels,
hold_prob : 1.0}))
print('\n')