SYMBOL INDEX (144 symbols across 35 files)

FILE: Chapter02/2_mnist_cnn.py
  function add_variable_summary (line 15) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 32) | def convolution_layer(input_layer, filters, kernel_size=[3, 3],
  function pooling_layer (line 44) | def pooling_layer(input_layer, pool_size=[2, 2], strides=2):
  function dense_layer (line 54) | def dense_layer(input_layer, units, activation=tf.nn.relu):

FILE: Chapter02/3_mnist_keras.py
  function simple_cnn (line 24) | def simple_cnn(input_shape):

FILE: Chapter02/4_cat_vs_dog_data_prep.py
  function copy_files (line 8) | def copy_files(prefix_str, range_start, range_end, target_dir):

FILE: Chapter02/5_cat_vs_dog_cnn.py
  function simple_cnn (line 21) | def simple_cnn(input_shape):

FILE: Chapter02/6_cat_vs_dog_augmentation.py
  function simple_cnn (line 20) | def simple_cnn(input_shape):

FILE: Chapter03/1_embedding_vis.py
  function add_variable_summary (line 18) | def add_variable_summary(tf_variable, summary_name):

FILE: Chapter03/3_deep_dream.py
  function resize_image (line 38) | def resize_image(image, size):

FILE: Chapter03/5_serving_client.py
  function _create_rpc_callback (line 18) | def _create_rpc_callback():

FILE: Chapter03/6_bottleneck_features.py
  function get_bottleneck_data (line 39) | def get_bottleneck_data(session, image_data):

FILE: Chapter03/7_annoy.py
  function create_annoy (line 10) | def create_annoy(target_features):

FILE: Chapter03/8_auto_encoder.py
  function fully_connected_layer (line 4) | def fully_connected_layer(input_layer, units):
  function convolution_layer (line 12) | def convolution_layer(input_layer, filter_size):
  function deconvolution_layer (line 22) | def deconvolution_layer(input_layer, filter_size, activation=tf.nn.relu):

FILE: Chapter03/9_denoising.py
  function add_variable_summary (line 16) | def add_variable_summary(tf_variable, summary_name):
  function dense_layer (line 29) | def dense_layer(input_layer, units, activation=tf.nn.tanh):

FILE: Chapter04/1_iou.py
  function calculate_iou (line 4) | def calculate_iou(gt_bb, pred_bb):

FILE: Chapter04/2_overfeat.py
  function add_variable_summary (line 15) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 32) | def convolution_layer(input_layer, filters, kernel_size=[3, 3],
  function pooling_layer (line 44) | def pooling_layer(input_layer, pool_size=[2, 2], strides=2):

FILE: Chapter04/4_yolo.py
  function calculate_iou (line 7) | def calculate_iou(gt_bb, pred_bb):
  function add_variable_summary (line 93) | def add_variable_summary(tf_variable, summary_name):
  function pooling_layer (line 105) | def pooling_layer(input_layer, pool_size=[2, 2], strides=2, padding='val...
  function convolution_layer (line 115) | def convolution_layer(input_layer, filters, kernel_size=[3, 3], padding=...
  function dense_layer (line 130) | def dense_layer(input_layer, units, activation=tf.nn.leaky_relu):

FILE: Chapter04/pascal_voc.py
  class pascal_voc (line 10) | class pascal_voc(object):
    method __init__ (line 11) | def __init__(self, phase, rebuild=False):
    method get (line 28) | def get(self):
    method image_read (line 45) | def image_read(self, imname, flipped=False):
    method prepare (line 54) | def prepare(self):
    method load_labels (line 71) | def load_labels(self):
    method load_pascal_annotation (line 106) | def load_pascal_annotation(self, index):

FILE: Chapter05/2_nerve_segmentation.py
  function dice_coefficient (line 15) | def dice_coefficient(y1, y2):
  function dice_coefficient_loss (line 21) | def dice_coefficient_loss(y1, y2):
  function preprocess (line 25) | def preprocess(imgs):
  function convolution_layer (line 33) | def convolution_layer(filters, kernel=(3,3), activation='relu', input_sh...
  function concatenated_de_convolution_layer (line 47) | def concatenated_de_convolution_layer(filters):
  function pooling_layer (line 59) | def pooling_layer():

FILE: Chapter05/3_satellite.py
  function resize_bilinear (line 24) | def resize_bilinear(images):

FILE: Chapter05/data.py
  function create_train_data (line 14) | def create_train_data():
  function load_train_data (line 49) | def load_train_data():
  function create_test_data (line 55) | def create_test_data():
  function load_test_data (line 86) | def load_test_data():

FILE: Chapter06/1_contrastive_loss.py
  function contrastive_loss (line 4) | def contrastive_loss(model_1, model_2, label, margin=0.1):

FILE: Chapter06/2_siamese_network.py
  function add_variable_summary (line 12) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 25) | def convolution_layer(input_layer, filters, kernel_size=[3, 3],
  function pooling_layer (line 37) | def pooling_layer(input_layer, pool_size=[2, 2], strides=2):
  function dense_layer (line 47) | def dense_layer(input_layer, units, activation=tf.nn.relu):
  function get_model (line 57) | def get_model(input_):

FILE: Chapter06/3_triplet_loss.py
  function triplet_loss (line 4) | def triplet_loss(anchor_face, positive_face, negative_face, margin):

FILE: Chapter06/4_triplet_mining.py
  function mine_triplets (line 5) | def mine_triplets(anchor, targets, negative_samples):

FILE: Chapter06/5_fiducial_points.py
  function add_variable_summary (line 4) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 16) | def convolution_layer(input_layer, filters, kernel_size=[3, 3],
  function pooling_layer (line 28) | def pooling_layer(input_layer, pool_size=[2, 2], strides=2):
  function dense_layer (line 38) | def dense_layer(input_layer, units, activation=tf.nn.tanh):

FILE: Chapter06/6_extract_features.py
  function load_and_align_data (line 11) | def load_and_align_data(image_paths,
  function get_face_embeddings (line 50) | def get_face_embeddings(image_paths,
  function compute_distance (line 69) | def compute_distance(embedding_1, embedding_2):

FILE: Chapter07/1_caption_attention.py
  class LSTM_sent (line 40) | class LSTM_sent(Recurrent):
    method __init__ (line 41) | def __init__(self, output_dim,
    method build (line 62) | def build(self, input_shape):
    method reset_states (line 124) | def reset_states(self):
    method preprocess_input (line 139) | def preprocess_input(self, x, train=False):
    method step (line 167) | def step(self, x, states):
    method get_constants (line 200) | def get_constants(self, x):
    method get_output_shape_for (line 226) | def get_output_shape_for(self, input_shape):
    method compute_mask (line 239) | def compute_mask(self, input, mask):
    method call (line 251) | def call(self, x, mask=None):
    method get_config (line 299) | def get_config(self):

FILE: Chapter08/1_style_transfer.py
  function subtract_imagenet_mean (line 19) | def subtract_imagenet_mean(image):
  function add_imagenet_mean (line 23) | def add_imagenet_mean(image, s):
  class ConvexOptimiser (line 36) | class ConvexOptimiser(object):
    method __init__ (line 37) | def __init__(self, cost_function, tensor_shape):
    method loss (line 42) | def loss(self, point):
    method gradients (line 46) | def gradients(self, point):
  function optimise (line 56) | def optimise(optimiser, iterations, point, tensor_shape, file_name):
  function generate_rand_img (line 66) | def generate_rand_img(shape):
  function grammian_matrix (line 86) | def grammian_matrix(matrix):
  function style_mse_loss (line 93) | def style_mse_loss(x, y):

FILE: Chapter08/2_vanilla_gan.py
  function add_variable_summary (line 8) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 21) | def convolution_layer(input_layer,
  function transpose_convolution_layer (line 37) | def transpose_convolution_layer(input_layer,
  function pooling_layer (line 54) | def pooling_layer(input_layer,
  function dense_layer (line 66) | def dense_layer(input_layer,
  function get_generator (line 78) | def get_generator(input_noise, is_training=True):
  function get_discriminator (line 94) | def get_discriminator(image, is_training=True):

FILE: Chapter08/3_conditional_gan.py
  function add_variable_summary (line 8) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 21) | def convolution_layer(input_layer,
  function transpose_convolution_layer (line 37) | def transpose_convolution_layer(input_layer,
  function pooling_layer (line 54) | def pooling_layer(input_layer,
  function dense_layer (line 66) | def dense_layer(input_layer,
  function get_generator (line 78) | def get_generator(input_noise, is_training=True):
  function get_discriminator (line 94) | def get_discriminator(image, is_training=True):

FILE: Chapter08/4_adverserial_loss.py
  function add_variable_summary (line 9) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 22) | def convolution_layer(input_layer,
  function transpose_convolution_layer (line 38) | def transpose_convolution_layer(input_layer,
  function pooling_layer (line 55) | def pooling_layer(input_layer,
  function dense_layer (line 67) | def dense_layer(input_layer,
  function get_generator (line 79) | def get_generator(input_noise, is_training=True):
  function get_discriminator (line 95) | def get_discriminator(image, is_training=True):
  function fully_connected_layer (line 106) | def fully_connected_layer(input_layer, units):
  function convolution_layer (line 114) | def convolution_layer(input_layer, filter_size):
  function deconvolution_layer (line 124) | def deconvolution_layer(input_layer, filter_size, activation=tf.nn.relu):
  function get_autoencoder (line 134) | def get_autoencoder():

FILE: Chapter08/5_image_translation.py
  function add_variable_summary (line 8) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 21) | def convolution_layer(input_layer,
  function transpose_convolution_layer (line 37) | def transpose_convolution_layer(input_layer,
  function pooling_layer (line 54) | def pooling_layer(input_layer,
  function dense_layer (line 66) | def dense_layer(input_layer,
  function get_generator (line 78) | def get_generator(input_noise, is_training=True):
  function get_discriminator (line 94) | def get_discriminator(image, is_training=True):

FILE: Chapter08/6_infogan.py
  function add_variable_summary (line 9) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 22) | def convolution_layer(input_layer,
  function transpose_convolution_layer (line 38) | def transpose_convolution_layer(input_layer,
  function pooling_layer (line 55) | def pooling_layer(input_layer,
  function dense_layer (line 67) | def dense_layer(input_layer,
  function get_generator (line 79) | def get_generator(input_noise, is_training=True):
  function get_discriminator (line 95) | def get_discriminator(image, is_training=True):

FILE: Chapter08/utils.py
  function beep (line 36) | def beep(): return Audio(filename='/home/jhoward/beep.mp3', autoplay=True)
  function dump (line 37) | def dump(obj, fname): pickle.dump(obj, open(fname, 'wb'))
  function load (line 38) | def load(fname): return pickle.load(open(fname, 'rb'))
  function limit_mem (line 41) | def limit_mem():
  function autolabel (line 48) | def autolabel(plt, fmt='%.2f'):
  function column_chart (line 64) | def column_chart(lbls, vals, val_lbls='%.2f'):
  function save_array (line 71) | def save_array(fname, arr):
  function load_array (line 76) | def load_array(fname): return bcolz.open(fname)[:]
  function load_glove (line 79) | def load_glove(loc):
  function plot_multi (line 84) | def plot_multi(im, dim=(4,4), figsize=(6,6), **kwargs ):
  function plot_train (line 93) | def plot_train(hist):
  function fit_gen (line 109) | def fit_gen(gen, fn, eval_fn, nb_iter):
  function wrap_config (line 115) | def wrap_config(layer):
  function copy_layer (line 119) | def copy_layer(layer): return layer_from_config(wrap_config(layer))
  function copy_layers (line 122) | def copy_layers(layers): return [copy_layer(layer) for layer in layers]
  function copy_weights (line 125) | def copy_weights(from_layers, to_layers):
  function copy_model (line 130) | def copy_model(m):
  function insert_layer (line 136) | def insert_layer(model, new_layer, index):

FILE: Chapter08/vgg16_avg.py
  function VGG16_Avg (line 22) | def VGG16_Avg(include_top=True, weights='imagenet', input_tensor=None, i...

FILE: Chapter09/2_parallel_stream.py
  function add_variable_summary (line 12) | def add_variable_summary(tf_variable, summary_name):
  function convolution_layer (line 25) | def convolution_layer(input_layer, filters, kernel_size=[3, 3],
  function pooling_layer (line 37) | def pooling_layer(input_layer, pool_size=[2, 2], strides=2):
  function dense_layer (line 47) | def dense_layer(input_layer, units, activation=tf.nn.relu):
  function get_model (line 57) | def get_model(input_):