SYMBOL INDEX (209 symbols across 46 files)

FILE: Caffe/cifar10/convert_cifar_data.cpp
  function read_image (line 31) | void read_image(std::ifstream* file, int* label, char* buffer) {
  function convert_dataset (line 39) | void convert_dataset(const string& input_folder, const string& output_fo...
  function main (line 93) | int main(int argc, char** argv) {

FILE: Caffe/cpp_classification/classification.cpp
  class Classifier (line 21) | class Classifier {
  function PairCompare (line 86) | static bool PairCompare(const std::pair<float, int>& lhs,
  function Argmax (line 92) | static std::vector<int> Argmax(const std::vector<float>& v, int N) {
  function main (line 229) | int main(int argc, char** argv) {
  function main (line 262) | int main(int argc, char** argv) {

FILE: Caffe/finetune_flickr_style/assemble_data.py
  function download_image (line 23) | def download_image(args_tuple):

FILE: Caffe/mnist/convert_mnist_data.cpp
  function swap_endian (line 38) | uint32_t swap_endian(uint32_t val) {
  function convert_dataset (line 43) | void convert_dataset(const char* image_filename, const char* label_filen...
  function main (line 113) | int main(int argc, char** argv) {
  function main (line 143) | int main(int argc, char** argv) {

FILE: Caffe/pycaffe/caffenet.py
  function conv_relu (line 7) | def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
  function fc_relu (line 12) | def fc_relu(bottom, nout):
  function max_pool (line 16) | def max_pool(bottom, ks, stride=1):
  function caffenet (line 19) | def caffenet(lmdb, batch_size=256, include_acc=False):
  function make_net (line 47) | def make_net():

FILE: Caffe/pycaffe/layers/pascal_multilabel_datalayers.py
  class PascalMultilabelDataLayerSync (line 20) | class PascalMultilabelDataLayerSync(caffe.Layer):
    method setup (line 27) | def setup(self, bottom, top):
    method forward (line 55) | def forward(self, bottom, top):
    method reshape (line 67) | def reshape(self, bottom, top):
    method backward (line 74) | def backward(self, top, propagate_down, bottom):
  class BatchLoader (line 81) | class BatchLoader(object):
    method __init__ (line 90) | def __init__(self, params, result):
    method load_next_image (line 106) | def load_next_image(self):
  function load_pascal_annotation (line 140) | def load_pascal_annotation(index, pascal_root):
  function check_params (line 196) | def check_params(params):
  function print_info (line 208) | def print_info(name, params):

FILE: Caffe/pycaffe/layers/pyloss.py
  class EuclideanLossLayer (line 5) | class EuclideanLossLayer(caffe.Layer):
    method setup (line 11) | def setup(self, bottom, top):
    method reshape (line 16) | def reshape(self, bottom, top):
    method forward (line 25) | def forward(self, bottom, top):
    method backward (line 29) | def backward(self, top, propagate_down, bottom):

FILE: Caffe/pycaffe/tools.py
  class SimpleTransformer (line 4) | class SimpleTransformer:
    method __init__ (line 11) | def __init__(self, mean=[128, 128, 128]):
    method set_mean (line 15) | def set_mean(self, mean):
    method set_scale (line 21) | def set_scale(self, scale):
    method preprocess (line 27) | def preprocess(self, im):
    method deprocess (line 41) | def deprocess(self, im):
  class CaffeSolver (line 53) | class CaffeSolver:
    method __init__ (line 62) | def __init__(self, testnet_prototxt_path="testnet.prototxt",
    method add_from_file (line 101) | def add_from_file(self, filepath):
    method write (line 113) | def write(self, filepath):

FILE: Caffe/siamese/convert_mnist_siamese_data.cpp
  function swap_endian (line 22) | uint32_t swap_endian(uint32_t val) {
  function read_image (line 27) | void read_image(std::ifstream* image_file, std::ifstream* label_file,
  function convert_dataset (line 36) | void convert_dataset(const char* image_filename, const char* label_filen...
  function main (line 109) | int main(int argc, char** argv) {
  function main (line 126) | int main(int argc, char** argv) {

FILE: Caffe/web_demo/app.py
  function index (line 29) | def index():
  function classify_url (line 34) | def classify_url():
  function classify_upload (line 57) | def classify_upload():
  function embed_image_html (line 82) | def embed_image_html(image):
  function allowed_file (line 92) | def allowed_file(filename):
  class ImagenetClassifier (line 99) | class ImagenetClassifier(object):
    method __init__ (line 119) | def __init__(self, model_def_file, pretrained_model_file, mean_file,
    method classify_image (line 148) | def classify_image(self, image):
  function start_tornado (line 184) | def start_tornado(app, port=5000):
  function start_from_terminal (line 192) | def start_from_terminal(app):

FILE: Caffe/web_demo/exifutil.py
  function open_oriented_im (line 19) | def open_oriented_im(im_path):
  function apply_orientation (line 35) | def apply_orientation(im, orientation):

FILE: Keras/addition_rnn.py
  class CharacterTable (line 35) | class CharacterTable(object):
    method __init__ (line 41) | def __init__(self, chars):
    method encode (line 51) | def encode(self, C, num_rows):
    method decode (line 63) | def decode(self, x, calc_argmax=True):
  class colors (line 69) | class colors:

FILE: Keras/antirectifier.py
  class Antirectifier (line 21) | class Antirectifier(layers.Layer):
    method compute_output_shape (line 49) | def compute_output_shape(self, input_shape):
    method call (line 55) | def call(self, inputs):

FILE: Keras/babi_memnn.py
  function tokenize (line 29) | def tokenize(sent):
  function parse_stories (line 38) | def parse_stories(lines, only_supporting=False):
  function get_stories (line 71) | def get_stories(f, only_supporting=False, max_length=None):
  function vectorize_stories (line 85) | def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):

FILE: Keras/babi_rnn.py
  function tokenize (line 74) | def tokenize(sent):
  function parse_stories (line 83) | def parse_stories(lines, only_supporting=False):
  function get_stories (line 116) | def get_stories(f, only_supporting=False, max_length=None):
  function vectorize_stories (line 129) | def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):

FILE: Keras/conv_filter_visualization.py
  function deprocess_image (line 26) | def deprocess_image(x):
  function normalize (line 56) | def normalize(x):

FILE: Keras/conv_lstm.py
  function generate_movies (line 47) | def generate_movies(n_samples=1200, n_frames=15):

FILE: Keras/deep_dream.py
  function preprocess_image (line 62) | def preprocess_image(image_path):
  function deprocess_image (line 72) | def deprocess_image(x):
  function continuity_loss (line 105) | def continuity_loss(x):
  function eval_loss_and_grads (line 153) | def eval_loss_and_grads(x):
  class Evaluator (line 164) | class Evaluator(object):
    method __init__ (line 175) | def __init__(self):
    method loss (line 179) | def loss(self, x):
    method grads (line 186) | def grads(self, x):

FILE: Keras/image_ocr.py
  function speckle (line 65) | def speckle(img):
  function paint_text (line 78) | def paint_text(text, w, h, rotate=False, ud=False, multi_fonts=False):
  function shuffle_mats_or_lists (line 122) | def shuffle_mats_or_lists(matrix_list, stop_ind=None):
  function text_to_labels (line 144) | def text_to_labels(text, num_classes):
  function is_valid_str (line 157) | def is_valid_str(in_str):
  class TextImageGenerator (line 166) | class TextImageGenerator(keras.callbacks.Callback):
    method __init__ (line 168) | def __init__(self, monogram_file, bigram_file, minibatch_size,
    method get_output_size (line 182) | def get_output_size(self):
    method build_word_list (line 187) | def build_word_list(self, num_words, max_string_len=None, mono_fractio...
    method get_batch (line 236) | def get_batch(self, index, size, train):
    method next_train (line 278) | def next_train(self):
    method next_val (line 288) | def next_val(self):
    method on_train_begin (line 296) | def on_train_begin(self, logs={}):
    method on_epoch_begin (line 301) | def on_epoch_begin(self, epoch, logs={}):
  function ctc_lambda_func (line 319) | def ctc_lambda_func(args):
  function decode_batch (line 330) | def decode_batch(test_func, word_batch):
  class VizCallback (line 347) | class VizCallback(keras.callbacks.Callback):
    method __init__ (line 349) | def __init__(self, run_name, test_func, text_img_gen, num_display_word...
    method show_edit_distance (line 358) | def show_edit_distance(self, num):
    method on_epoch_end (line 376) | def on_epoch_end(self, epoch, logs={}):
  function train (line 399) | def train(run_name, start_epoch, stop_epoch, img_w):

FILE: Keras/imdb_fasttext.py
  function create_ngram_set (line 24) | def create_ngram_set(input_list, ngram_value=2):
  function add_ngram (line 37) | def add_ngram(sequences, token_indice, ngram_range=2):

FILE: Keras/lstm_text_generation.py
  function sample (line 62) | def sample(preds, temperature=1.0):

FILE: Keras/mnist_acgan.py
  function build_generator (line 50) | def build_generator(latent_size):
  function build_discriminator (line 94) | def build_discriminator():

FILE: Keras/mnist_net2net.py
  function preprocess_input (line 74) | def preprocess_input(x):
  function preprocess_output (line 78) | def preprocess_output(y):
  function wider2net_conv2d (line 91) | def wider2net_conv2d(teacher_w1, teacher_b1, teacher_w2, new_width, init):
  function wider2net_fc (line 142) | def wider2net_fc(teacher_w1, teacher_b1, teacher_w2, new_width, init):
  function deeper2net_conv2d (line 192) | def deeper2net_conv2d(teacher_w):
  function copy_weights (line 207) | def copy_weights(teacher_model, student_model, layer_names):
  function make_teacher_model (line 217) | def make_teacher_model(train_data, validation_data, epochs=3):
  function make_wider_student_model (line 240) | def make_wider_student_model(teacher_model, train_data,
  function make_deeper_student_model (line 290) | def make_deeper_student_model(teacher_model, train_data,
  function net2wider_experiment (line 340) | def net2wider_experiment():
  function net2deeper_experiment (line 364) | def net2deeper_experiment():

FILE: Keras/mnist_siamese_graph.py
  function euclidean_distance (line 25) | def euclidean_distance(vects):
  function eucl_dist_output_shape (line 30) | def eucl_dist_output_shape(shapes):
  function contrastive_loss (line 35) | def contrastive_loss(y_true, y_pred):
  function create_pairs (line 44) | def create_pairs(x, digit_indices):
  function create_base_network (line 63) | def create_base_network(input_dim):
  function compute_accuracy (line 75) | def compute_accuracy(predictions, labels):

FILE: Keras/mnist_sklearn_wrapper.py
  function make_model (line 45) | def make_model(dense_layer_sizes, filters, kernel_size, pool_size):

FILE: Keras/mnist_swwae.py
  function convresblock (line 60) | def convresblock(x, nfeats=8, ksize=3, nskipped=2, elu=True):
  function getwhere (line 96) | def getwhere(x):

FILE: Keras/mnist_transfer_cnn.py
  function train_model (line 45) | def train_model(model, train, test, num_classes):

FILE: Keras/neural_doodle.py
  function preprocess_image (line 97) | def preprocess_image(image_path):
  function deprocess_image (line 105) | def deprocess_image(x):
  function kmeans (line 121) | def kmeans(xs, k):
  function load_mask_labels (line 132) | def load_mask_labels():
  function gram_matrix (line 218) | def gram_matrix(x):
  function region_style_loss (line 225) | def region_style_loss(style_image, target_image, style_mask, target_mask):
  function style_loss (line 246) | def style_loss(style_image, target_image, style_masks, target_masks):
  function content_loss (line 265) | def content_loss(content_image, target_image):
  function total_variation_loss (line 269) | def total_variation_loss(x):
  function eval_loss_and_grads (line 312) | def eval_loss_and_grads(x):
  class Evaluator (line 326) | class Evaluator(object):
    method __init__ (line 328) | def __init__(self):
    method loss (line 332) | def loss(self, x):
    method grads (line 339) | def grads(self, x):

FILE: Keras/neural_style_transfer.py
  function preprocess_image (line 98) | def preprocess_image(image_path):
  function deprocess_image (line 108) | def deprocess_image(x):
  function gram_matrix (line 153) | def gram_matrix(x):
  function style_loss (line 169) | def style_loss(style, combination):
  function content_loss (line 183) | def content_loss(base, combination):
  function total_variation_loss (line 190) | def total_variation_loss(x):
  function eval_loss_and_grads (line 231) | def eval_loss_and_grads(x):
  class Evaluator (line 252) | class Evaluator(object):
    method __init__ (line 254) | def __init__(self):
    method loss (line 258) | def loss(self, x):
    method grads (line 265) | def grads(self, x):

FILE: Keras/stateful_lstm.py
  function gen_cosine_amp (line 19) | def gen_cosine_amp(amp=100, period=1000, x0=0, xn=50000, step=1, k=0.0001):

FILE: Keras/variational_autoencoder.py
  function sampling (line 28) | def sampling(args):
  function vae_loss (line 44) | def vae_loss(x, x_decoded_mean):

FILE: Keras/variational_autoencoder_deconv.py
  function sampling (line 57) | def sampling(args):
  function vae_loss (line 109) | def vae_loss(x, x_decoded_mean):

FILE: TensorFlow/examples/3_NeuralNetworks/autoencoder.py
  function encoder (line 52) | def encoder(x):
  function decoder (line 63) | def decoder(x):

FILE: TensorFlow/examples/3_NeuralNetworks/bidirectional_rnn.py
  function BiRNN (line 52) | def BiRNN(x, weights, biases):

FILE: TensorFlow/examples/3_NeuralNetworks/convolutional_network.py
  function conv2d (line 36) | def conv2d(x, W, b, strides=1):
  function maxpool2d (line 43) | def maxpool2d(x, k=2):
  function conv_net (line 50) | def conv_net(x, weights, biases, dropout):

FILE: TensorFlow/examples/3_NeuralNetworks/dynamic_rnn.py
  class ToySequenceData (line 21) | class ToySequenceData(object):
    method __init__ (line 33) | def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3,
    method next (line 63) | def next(self, batch_size):
  function dynamicRNN (line 111) | def dynamicRNN(x, seqlen, weights, biases):

FILE: TensorFlow/examples/3_NeuralNetworks/multilayer_perceptron.py
  function multilayer_perceptron (line 36) | def multilayer_perceptron(x, weights, biases):

FILE: TensorFlow/examples/3_NeuralNetworks/recurrent_network.py
  function RNN (line 50) | def RNN(x, weights, biases):

FILE: TensorFlow/examples/4_Utils/save_restore_model.py
  function multilayer_perceptron (line 36) | def multilayer_perceptron(x, weights, biases):

FILE: TensorFlow/examples/4_Utils/tensorboard_advanced.py
  function multilayer_perceptron (line 39) | def multilayer_perceptron(x, weights, biases):

FILE: TensorFlow/examples/5_MultiGPU/multigpu_basics.py
  function matpow (line 42) | def matpow(M, n):

FILE: TensorFlow/input_data.py
  function maybe_download (line 8) | def maybe_download(filename, work_directory):
  function _read32 (line 18) | def _read32(bytestream):
  function extract_images (line 21) | def extract_images(filename):
  function dense_to_one_hot (line 37) | def dense_to_one_hot(labels_dense, num_classes=10):
  function extract_labels (line 44) | def extract_labels(filename, one_hot=False):
  class DataSet (line 59) | class DataSet(object):
    method __init__ (line 60) | def __init__(self, images, labels, fake_data=False):
    method images (line 81) | def images(self):
    method labels (line 84) | def labels(self):
    method num_examples (line 87) | def num_examples(self):
    method epochs_completed (line 90) | def epochs_completed(self):
    method next_batch (line 92) | def next_batch(self, batch_size, fake_data=False):
  function read_data_sets (line 115) | def read_data_sets(train_dir, fake_data=False, one_hot=False):

FILE: TensorFlow/tensorflow_distributed_mnist_demo.py
  function main (line 35) | def main(_):

FILE: Theano/cnn.py
  function floatX (line 11) | def floatX(X):
  function init_weights (line 14) | def init_weights(shape):
  function rectify (line 17) | def rectify(X):
  function softmax (line 20) | def softmax(X):
  function dropout (line 24) | def dropout(X, p=0.):
  function RMSprop (line 31) | def RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):
  function model (line 43) | def model(X, w, w2, w3, w4, p_drop_conv, p_drop_hidden):

FILE: Theano/linear_regression.py
  function model (line 11) | def model(X, w):

FILE: Theano/neural_networks.py
  function floatX (line 9) | def floatX(X):
  function init_weights (line 12) | def init_weights(shape):
  function rectify (line 15) | def rectify(X):
  function softmax (line 18) | def softmax(X):
  function RMSprop (line 22) | def RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):
  function dropout (line 34) | def dropout(X, p=0.):
  function model (line 41) | def model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):