SYMBOL INDEX (66 symbols across 30 files)

FILE: Chapter04/EMOTION_CNN/Python 2.7/EmotionDetectorUtils.py
  class testResult (line 19) | class testResult:
    method __init__ (line 21) | def __init__(self):
    method evaluate (line 30) | def evaluate(self,label):
    method display_result (line 47) | def display_result(self,evaluations):
  function read_data (line 57) | def read_data(data_dir, force=False):

FILE: Chapter04/EMOTION_CNN/Python 2.7/EmotionDetector_1.py
  function add_to_regularization_loss (line 29) | def add_to_regularization_loss(W, b):
  function weight_variable (line 33) | def weight_variable(shape, stddev=0.02, name=None):
  function bias_variable (line 41) | def bias_variable(shape, name=None):
  function conv2d_basic (line 48) | def conv2d_basic(x, W, bias):
  function max_pool_2x2 (line 52) | def max_pool_2x2(x):
  function emotion_cnn (line 57) | def emotion_cnn(dataset):
  function loss (line 118) | def loss(pred, label):
  function train (line 126) | def train(loss, step):
  function get_next_batch (line 130) | def get_next_batch(images, labels, step):
  function main (line 137) | def main(argv=None):

FILE: Chapter04/EMOTION_CNN/Python 2.7/test_your_image.py
  function rgb2gray (line 18) | def rgb2gray(rgb):

FILE: Chapter04/EMOTION_CNN/Python 3.5/EmotionDetectorUtils.py
  class testResult (line 19) | class testResult:
    method __init__ (line 21) | def __init__(self):
    method evaluate (line 30) | def evaluate(self,label):
    method display_result (line 47) | def display_result(self,evaluations):
  function read_data (line 57) | def read_data(data_dir, force=False):

FILE: Chapter04/EMOTION_CNN/Python 3.5/EmotionDetector_1.py
  function add_to_regularization_loss (line 29) | def add_to_regularization_loss(W, b):
  function weight_variable (line 33) | def weight_variable(shape, stddev=0.02, name=None):
  function bias_variable (line 41) | def bias_variable(shape, name=None):
  function conv2d_basic (line 48) | def conv2d_basic(x, W, bias):
  function max_pool_2x2 (line 52) | def max_pool_2x2(x):
  function emotion_cnn (line 57) | def emotion_cnn(dataset):
  function loss (line 118) | def loss(pred, label):
  function train (line 126) | def train(loss, step):
  function get_next_batch (line 130) | def get_next_batch(images, labels, step):
  function main (line 137) | def main(argv=None):

FILE: Chapter04/EMOTION_CNN/Python 3.5/test_your_image.py
  function rgb2gray (line 18) | def rgb2gray(rgb):

FILE: Chapter04/MNIST_CNN/Python 2.7/mnist_cnn_1.py
  function init_weights (line 10) | def init_weights(shape):
  function model (line 14) | def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):

FILE: Chapter04/MNIST_CNN/Python 3.5/mnist_cnn_1.py
  function init_weights (line 10) | def init_weights(shape):
  function model (line 14) | def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):

FILE: Chapter05/Python 2.7/Convlutional_AutoEncoder.py
  function cae (line 52) | def cae(_X, _W, _b, _keepprob):

FILE: Chapter05/Python 2.7/deconvolutional_autoencoder_1.py
  function plotresult (line 7) | def plotresult(org_vec,noisy_vec,out_vec):

FILE: Chapter05/Python 2.7/denoising_autoencoder_1.py
  function plotresult (line 7) | def plotresult(org_vec,noisy_vec,out_vec):

FILE: Chapter05/Python 3.5/Convlutional_AutoEncoder.py
  function cae (line 52) | def cae(_X, _W, _b, _keepprob):

FILE: Chapter05/Python 3.5/deconvolutional_autoencoder_1.py
  function plotresult (line 7) | def plotresult(org_vec,noisy_vec,out_vec):

FILE: Chapter05/Python 3.5/denoising_autoencoder_1.py
  function plotresult (line 7) | def plotresult(org_vec,noisy_vec,out_vec):

FILE: Chapter06/Python 2.7/LSTM_model_1.py
  function RNN (line 27) | def RNN(x, weights, biases):

FILE: Chapter06/Python 2.7/bidirectional_RNN_1.py
  function BiRNN (line 28) | def BiRNN(x, weights, biases):

FILE: Chapter06/Python 3.5/LSTM_model_1.py
  function RNN (line 27) | def RNN(x, weights, biases):

FILE: Chapter06/Python 3.5/bidirectional_RNN_1.py
  function BiRNN (line 28) | def BiRNN(x, weights, biases):

FILE: Chapter07/Python 2.7/gpu_computing_with_multiple_GPU.py
  function matpow (line 13) | def matpow(M, n):

FILE: Chapter07/Python 2.7/gpu_example.py
  function matpow (line 16) | def matpow(M, n):

FILE: Chapter07/Python 2.7/gpu_soft_placemnet_1.py
  function matpow (line 13) | def matpow(M, n):

FILE: Chapter07/Python 3.5/gpu_computing_with_multiple_GPU.py
  function matpow (line 13) | def matpow(M, n):

FILE: Chapter07/Python 3.5/gpu_example.py
  function matpow (line 12) | def matpow(M, n):

FILE: Chapter07/Python 3.5/gpu_soft_placemnet_1.py
  function matpow (line 13) | def matpow(M, n):

FILE: Chapter08/Python 2.7/digit_classifier.py
  function multilayer_fully_connected (line 15) | def multilayer_fully_connected(images, labels):
  function lenet5 (line 23) | def lenet5(images, labels):
  function main (line 35) | def main(_=None):

FILE: Chapter08/Python 2.7/pretty_tensor_digit_1.py
  function multilayer_fully_connected (line 14) | def multilayer_fully_connected(images, labels):
  function lenet5 (line 20) | def lenet5(images, labels):
  function main (line 26) | def main(_=None):

FILE: Chapter08/Python 2.7/tflearn_titanic_classifier.py
  function preprocess (line 7) | def preprocess(data, columns_to_ignore):

FILE: Chapter08/Python 3.5/digit_classifier.py
  function multilayer_fully_connected (line 20) | def multilayer_fully_connected(images, labels):
  function lenet5 (line 28) | def lenet5(images, labels):
  function main (line 40) | def main(_=None):

FILE: Chapter08/Python 3.5/pretty_tensor_digit_1.py
  function multilayer_fully_connected (line 17) | def multilayer_fully_connected(images, labels):
  function lenet5 (line 23) | def lenet5(images, labels):
  function main (line 29) | def main(_=None):

FILE: Chapter08/Python 3.5/tflearn_titanic_classifier.py
  function preprocess (line 9) | def preprocess(data, columns_to_ignore):