train.py

from datetime import datetime

import tensorflow as tf

import dataset

#
# Initialize variables
#

batch_size = 32

# Prepare input data
classes = ['hotdog', 'not-hotdog']
num_classes = len(classes)

# 20% of the data will automatically be used for validation
validation_size = 0.3
img_size = 128
num_channels = 3
train_path = 'images'

# We shall load all the training and validation images and labels into memory using openCV and use that during training
data = dataset.read_train_sets(train_path, img_size, classes, validation_size=validation_size)

print("Complete reading input data. Will Now print a snippet of it")
print("Number of files in Training-set:\t\t{}".format(len(data.train.labels)))
print("Number of files in Validation-set:\t{}".format(len(data.valid.labels)))

#
# Set the in- and output variables of the model and add them to the graph
#

session = tf.Session()
x = tf.placeholder(tf.float32, shape=[None, img_size, img_size, num_channels], name='x')

# labels
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')
y_true_cls = tf.argmax(y_true, dimension=1)

# Network graph parameters
filter_size_conv1 = 3
num_filters_conv1 = 32

filter_size_conv2 = 3
num_filters_conv2 = 32

filter_size_conv3 = 3
num_filters_conv3 = 64

fc_layer_size = 128


#
# Define helper functions to create the layers of the model
#

def create_weights(shape):
    new_weights = tf.Variable(tf.truncated_normal(shape, stddev=0.05))

    # with tf.name_scope('summary'):
    #     tf.summary.histogram(new_weights)

    return new_weights


def create_biases(size):
    return tf.Variable(tf.constant(0.05, shape=[size]))


def create_convolutional_layer(input,
                               num_input_channels,
                               conv_filter_size,
                               num_filters):
    # We shall define the weights that will be trained using create_weights function.
    weights = create_weights(shape=[conv_filter_size, conv_filter_size, num_input_channels, num_filters])
    # We create biases using the create_biases function. These are also trained.
    biases = create_biases(num_filters)

    # Creating the convolutional layer
    layer = tf.nn.conv2d(input=input,
                         filter=weights,
                         strides=[1, 1, 1, 1],
                         padding='SAME')

    layer += biases

    # We shall be using max-pooling.
    layer = tf.nn.max_pool(value=layer,
                           ksize=[1, 2, 2, 1],
                           strides=[1, 2, 2, 1],
                           padding='SAME')
    # Output of pooling is fed to Relu which is the activation function for us.
    layer = tf.nn.relu(layer)

    return layer


def create_flatten_layer(layer):
    # We know that the shape of the layer will be [batch_size img_size img_size num_channels]
    # But let's get it from the previous layer.
    layer_shape = layer.get_shape()

    # Number of features will be img_height * img_width* num_channels. But we shall calculate it in place of hard-coding it.
    num_features = layer_shape[1:4].num_elements()

    # Now, we Flatten the layer so we shall have to reshape to num_features
    layer = tf.reshape(layer, [-1, num_features])

    return layer


def create_fc_layer(input,
                    num_inputs,
                    num_outputs,
                    use_relu=True):
    # Let's define trainable weights and biases.
    weights = create_weights(shape=[num_inputs, num_outputs])
    biases = create_biases(num_outputs)

    # Fully connected layer takes input x and produces wx+b.Since, these are matrices, we use matmul function in Tensorflow
    layer = tf.matmul(input, weights) + biases
    if use_relu:
        layer = tf.nn.relu(layer)

    return layer


layer_conv1 = create_convolutional_layer(input=x,
                                         num_input_channels=num_channels,
                                         conv_filter_size=filter_size_conv1,
                                         num_filters=num_filters_conv1)
layer_conv2 = create_convolutional_layer(input=layer_conv1,
                                         num_input_channels=num_filters_conv1,
                                         conv_filter_size=filter_size_conv2,
                                         num_filters=num_filters_conv2)

layer_conv3 = create_convolutional_layer(input=layer_conv2,
                                         num_input_channels=num_filters_conv2,
                                         conv_filter_size=filter_size_conv3,
                                         num_filters=num_filters_conv3)

layer_flat = create_flatten_layer(layer_conv3)

layer_fc1 = create_fc_layer(input=layer_flat,
                            num_inputs=layer_flat.get_shape()[1:4].num_elements(),
                            num_outputs=fc_layer_size,
                            use_relu=True)

layer_fc2 = create_fc_layer(input=layer_fc1,
                            num_inputs=fc_layer_size,
                            num_outputs=num_classes,
                            use_relu=False)

y_pred = tf.nn.softmax(layer_fc2, name='y_pred')

y_pred_cls = tf.argmax(y_pred, dimension=1)
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,
                                                        labels=y_true)
cost = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=5e-6).minimize(cost)
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.name_scope('summary'):
    tf.summary.histogram('loss function', accuracy)


# Merge all the summaries and write them out to /tmp/mnist_logs (by default)
merged = tf.summary.merge_all()
current_time = datetime.now().strftime("%Y%m%d-%H%M")
train_writer = tf.summary.FileWriter('./logs/{}/train'.format(current_time),
                                      session.graph)
test_writer = tf.summary.FileWriter('./logs/{}/test'.format(current_time))

session.run(tf.global_variables_initializer())


def show_progress(epoch, feed_dict_train, feed_dict_validate, val_loss):
    acc = session.run(accuracy, feed_dict=feed_dict_train)
    val_acc = session.run(accuracy, feed_dict=feed_dict_validate)
    msg = "Training Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%},  Validation Loss: {3:.3f}"
    print(msg.format(epoch + 1, acc, val_acc, val_loss))


total_iterations = 0

saver = tf.train.Saver()


#
# Run the model: do the training phase of the model
#

def train(num_iteration):
    global total_iterations

    for i in range(total_iterations,
                   total_iterations + num_iteration):

        x_batch, y_true_batch, _, cls_batch = data.train.next_batch(batch_size)
        x_valid_batch, y_valid_batch, _, valid_cls_batch = data.valid.next_batch(batch_size)

        feed_dict_tr = {x: x_batch,
                        y_true: y_true_batch}
        feed_dict_val = {x: x_valid_batch,
                         y_true: y_valid_batch}

        session.run(optimizer, feed_dict=feed_dict_tr)
        accuracy_summary, _ = session.run([merged, accuracy], feed_dict=feed_dict_tr)
        train_writer.add_summary(accuracy_summary, i)

        if i % int(data.train.num_examples / batch_size) == 0:
            val_loss_summary, val_loss = session.run([merged, cost], feed_dict=feed_dict_val)
            epoch = int(i / int(data.train.num_examples / batch_size))

            show_progress(epoch, feed_dict_tr, feed_dict_val, val_loss)

            accuracy_summary, _ = session.run([merged, accuracy], feed_dict=feed_dict_val)
            test_writer.add_summary(val_loss_summary, i)
            test_writer.add_summary(accuracy_summary, i)

            saver.save(session, './model/hotdog-classifier')

    total_iterations += num_iteration


if __name__ == '__main__':
    train(num_iteration=3000)